Version: 1.0
Date: December 2024
Status: Draft

This document describes the software design for YapTrap, detailing the system architecture, component design, and implementation approach that satisfies the requirements specified in the SRS.

This design covers:

• Overall system architecture and component relationships
• Database schema and data flow design
• Platform integration patterns and APIs
• AI/LLM integration architecture
• Administrative interface design
• Monitoring and logging infrastructure

• Modularity: Clear separation of concerns with well-defined interfaces
• Scalability: Horizontal scaling capabilities within Val Town constraints
• Reliability: Defensive programming with comprehensive error handling
• Maintainability: Clean code with extensive testing and documentation
• Security: Security-first design with data protection throughout

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Messaging     │    │   YapTrap       │    │   Admin         │
│   Platforms     │◄──►│   Core System   │◄──►│   Dashboard     │
│                 │    │                 │    │                 │
│ • Slack         │    │ • Message Proc  │    │ • Monitoring    │
│ • SMS/MMS       │    │ • AI Engine     │    │ • Configuration │
│ • Email         │    │ • Database      │    │ • Analytics     │
│ • WhatsApp      │    │ • Platform APIs │    │ • User Mgmt     │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │
                       ┌─────────────────┐
                       │   External      │
                       │   Services      │
                       │                 │
                       │ • LLM APIs      │
                       │ • Database      │
                       │ • Monitoring    │
                       └─────────────────┘

• Message Router: Central message processing and routing
• Platform Handlers: Platform-specific integration modules
• AI Engine: LLM integration and response generation
• Personality Engine: User profiling and adaptation logic
• Database Layer: Data persistence and retrieval
• Admin Interface: Web-based management dashboard
• Monitoring System: Logging, metrics, and health monitoring

backend/
├── index.ts              # Main HTTP entry point (Hono app)
├── database/
│   ├── schema.ts         # Database schema definitions
│   ├── queries.ts        # Database query functions
│   └── migrations.ts     # Schema migrations
├── platforms/
│   ├── base.ts          # Base platform handler interface
│   ├── slack.ts         # Slack integration
│   ├── sms.ts           # SMS/MMS via Telnyx
│   ├── email.ts         # Email via SendGrid
│   └── whatsapp.ts      # WhatsApp Business API
├── ai/
│   ├── engine.ts        # LLM integration layer
│   ├── personality.ts   # Personality analysis and profiling
│   └── context.ts       # Conversation context management
├── monitoring/
│   ├── logger.ts        # Centralized logging
│   ├── metrics.ts       # Performance and usage metrics
│   └── health.ts        # System health monitoring
└── routes/
    ├── webhooks.ts      # Platform webhook handlers
    ├── admin.ts         # Admin API endpoints
    └── static.ts        # Static file serving

frontend/
├── index.html           # Main admin dashboard
├── components/
│   ├── Dashboard.tsx    # Main dashboard component
│   ├── ConversationView.tsx
│   ├── MetricsView.tsx
│   └── ConfigView.tsx
└── styles/
    └── main.css

shared/
├── types.ts             # Shared TypeScript interfaces
├── utils.ts             # Common utility functions
└── constants.ts         # System constants and configuration

-- Conversations: Track group conversations across platforms
CREATE TABLE conversations (
    id TEXT PRIMARY KEY,
    platform TEXT NOT NULL,
    platform_id TEXT NOT NULL,
    name TEXT,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
    last_activity DATETIME,
    settings JSON,
    is_active BOOLEAN DEFAULT 1
);

-- Users: Individual participants across all conversations
CREATE TABLE users (
    id TEXT PRIMARY KEY,
    platform TEXT NOT NULL,
    platform_user_id TEXT NOT NULL,
    display_name TEXT,
    personality_profile JSON,
    preferences JSON,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
    updated_at DATETIME DEFAULT CURRENT_TIMESTAMP
);

-- Messages: All messages in conversations
CREATE TABLE messages (
    id TEXT PRIMARY KEY,
    conversation_id TEXT NOT NULL,
    user_id TEXT,
    content TEXT NOT NULL,
    message_type TEXT DEFAULT 'user', -- 'user', 'yaptrap', 'system'
    platform_message_id TEXT,
    timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
    metadata JSON,
    FOREIGN KEY (conversation_id) REFERENCES conversations(id),
    FOREIGN KEY (user_id) REFERENCES users(id)
);

-- Conversation Participants: Many-to-many relationship
CREATE TABLE conversation_participants (
    conversation_id TEXT NOT NULL,
    user_id TEXT NOT NULL,
    joined_at DATETIME DEFAULT CURRENT_TIMESTAMP,
    role TEXT DEFAULT 'member', -- 'member', 'admin', 'bot'
    is_active BOOLEAN DEFAULT 1,
    PRIMARY KEY (conversation_id, user_id),
    FOREIGN KEY (conversation_id) REFERENCES conversations(id),
    FOREIGN KEY (user_id) REFERENCES users(id)
);

-- System Logs: Comprehensive logging for monitoring
CREATE TABLE system_logs (
    id TEXT PRIMARY KEY,
    timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
    level TEXT NOT NULL, -- 'debug', 'info', 'warn', 'error'
    component TEXT NOT NULL,
    message TEXT NOT NULL,
    metadata JSON,
    conversation_id TEXT,
    user_id TEXT
);

-- Configuration: System and conversation-specific settings
CREATE TABLE configuration (
    key TEXT PRIMARY KEY,
    value JSON NOT NULL,
    scope TEXT DEFAULT 'global', -- 'global', 'conversation', 'user'
    scope_id TEXT,
    updated_at DATETIME DEFAULT CURRENT_TIMESTAMP,
    updated_by TEXT
);

1. Webhook Reception: Platform sends message to webhook endpoint
2. Message Normalization: Convert platform-specific format to internal format
3. User/Conversation Resolution: Identify or create user and conversation records
4. Context Analysis: Analyze message content and conversation context
5. Response Decision: Determine if and how YapTrap should respond
6. Response Generation: Generate appropriate response using AI engine
7. Platform Delivery: Send response back through platform API
8. Logging: Record all activities for monitoring and learning

1. Message Analysis: Extract communication patterns and preferences
2. Profile Update: Update user personality profile with new insights
3. Group Dynamics: Analyze group interaction patterns
4. Adaptation: Adjust YapTrap's behavior based on learned preferences

interface MessageRouter {
    // Route incoming webhook messages to appropriate platform handlers
    routeMessage(platform: string, payload: any): Promise<void>;
    
    // Process normalized messages through the AI pipeline
    processMessage(message: NormalizedMessage): Promise<void>;
    
    // Determine response strategy based on context and configuration
    shouldRespond(context: ConversationContext): Promise<boolean>;
}

interface PlatformHandler {
    // Normalize incoming platform messages to internal format
    normalizeMessage(payload: any): Promise<NormalizedMessage>;
    
    // Send response back to platform
    sendMessage(conversationId: string, content: string): Promise<void>;
    
    // Handle platform-specific authentication and setup
    authenticate(): Promise<boolean>;
    
    // Validate webhook signatures and requests
    validateWebhook(payload: any, signature: string): boolean;
}

interface AIEngine {
    // Generate contextual response based on conversation history
    generateResponse(context: ConversationContext): Promise<string>;
    
    // Analyze message sentiment and intent
    analyzeMessage(message: NormalizedMessage): Promise<MessageAnalysis>;
    
    // Update conversation context with new message
    updateContext(context: ConversationContext, message: NormalizedMessage): Promise<ConversationContext>;
}

interface PersonalityEngine {
    // Analyze user communication patterns
    analyzeUserPersonality(userId: string, messages: NormalizedMessage[]): Promise<PersonalityProfile>;
    
    // Update personality profile with new insights
    updatePersonalityProfile(userId: string, insights: PersonalityInsights): Promise<void>;
    
    // Get personality-adapted response style
    getResponseStyle(userId: string, groupContext: GroupContext): Promise<ResponseStyle>;
}

// Platform webhook receivers
POST /webhooks/slack          # Slack events and messages
POST /webhooks/sms            # Telnyx SMS/MMS webhooks  
POST /webhooks/email          # SendGrid inbound email
POST /webhooks/whatsapp       # WhatsApp Business API webhooks

// Administrative endpoints
GET  /admin/conversations     # List all conversations
GET  /admin/users            # List all users
GET  /admin/metrics          # System metrics and health
POST /admin/config           # Update configuration
GET  /admin/logs             # Stream system logs

// Normalized message format across all platforms
interface NormalizedMessage {
    id: string;
    conversationId: string;
    userId: string;
    content: string;
    timestamp: Date;
    platform: string;
    messageType: 'text' | 'image' | 'file' | 'system';
    metadata: Record<string, any>;
}

// Conversation context for AI processing
interface ConversationContext {
    conversationId: string;
    participants: User[];
    recentMessages: NormalizedMessage[];
    groupDynamics: GroupDynamics;
    yapTrapSettings: YapTrapSettings;
}

// User personality profile
interface PersonalityProfile {
    userId: string;
    communicationStyle: CommunicationStyle;
    interests: string[];
    responsePreferences: ResponsePreferences;
    groupRole: GroupRole;
    lastUpdated: Date;
}

• Webhook Validation: Verify all incoming webhooks using platform-specific signatures
• API Authentication: Secure admin endpoints with authentication tokens
• Rate Limiting: Implement rate limiting to prevent abuse
• Input Validation: Sanitize and validate all input data

• Encryption: Encrypt sensitive data at rest and in transit
• Access Control: Implement role-based access control for admin functions
• Audit Logging: Log all data access and modifications
• Data Retention: Implement configurable data retention policies

• Structured Logging: Use consistent JSON format for all log entries
• Log Levels: Implement debug, info, warn, error levels
• Contextual Logging: Include conversation and user context in logs
• Performance Logging: Track response times and system performance

• Message Metrics: Track message volume, response rates, engagement
• Performance Metrics: Monitor response times, error rates, throughput
• User Metrics: Track user satisfaction, personality learning accuracy
• System Metrics: Monitor resource usage, health status

• Main Entry Point: Single HTTP handler in backend/index.ts
• Environment Configuration: Use Val Town environment variables
• Database: SQLite for development, external database for production
• Static Assets: Serve admin interface through Val Town

• Database: External PostgreSQL or SQLite for data persistence
• LLM Services: OpenAI, xAI, Claude APIs for AI functionality
• Platform APIs: Direct integration with messaging platform APIs
• Monitoring: External logging and metrics collection services

• Graceful Degradation: Continue operation with reduced functionality during failures
• Retry Logic: Implement exponential backoff for transient failures
• Circuit Breakers: Prevent cascade failures in external service calls
• Fallback Responses: Provide default responses when AI services fail

• Automatic Recovery: Self-healing for common failure scenarios
• Manual Intervention: Clear procedures for manual recovery
• Data Recovery: Backup and restore procedures for data loss
• Service Recovery: Procedures for restoring service after outages

• Stateless Design: All components designed for horizontal scaling
• Caching Strategy: Cache frequently accessed data and AI responses
• Database Optimization: Efficient queries and indexing strategy
• Rate Limiting: Protect against overload and abuse

• Response Time: Target sub-5-second response times
• Concurrent Processing: Handle multiple conversations simultaneously
• Resource Management: Efficient memory and CPU usage
• Network Optimization: Minimize external API calls and data transfer

Document Control:

• Author: Technical Architect
• Reviewers: Senior Developer, DevOps Engineer
• Approval: Technical Lead
• Implementation Reference: All code must trace back to this design
• Next Review: Upon major architectural changes