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AI System Architecture

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Overview

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Architecture Diagram

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Component Descriptions

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AI API Layer

• API routes for chat completion, sentiment analysis, crisis detection, etc.
• Request validation using Zod schemas
• Authentication and authorization checks
• Rate limiting
• Response formatting

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AI Services Layer

• SentimentAnalysisService: Analyzes the sentiment of text
• CrisisDetectionService: Detects potential crisis situations in text
• ResponseGenerationService: Generates therapeutic responses
• InterventionAnalysisService: Analyzes the effectiveness of therapeutic interventions

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AI Provider Layer

• OpenAIProvider: Interacts with the OpenAI API
• AnthropicProvider: Interacts with the Anthropic API
• Provider factory for creating provider instances

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Error Handling

• AIError class for standardized errors
• Error transformation utilities
• Retry mechanism with exponential backoff
• API error response handling

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Performance Optimization

• Response caching with LRU eviction
• Token usage estimation and optimization
• Performance metrics collection
• Latency and token usage warnings

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Audit Logging

• HIPAA-compliant audit logging
• Usage tracking
• Error logging
• Performance metrics logging

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Data Flow

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Chat Completion Flow

1. Client sends a request to the /api/ai/completion endpoint
2. API layer validates the request and authenticates the user
3. API layer creates an AI service instance using the factory
4. AI service sends the request to the appropriate provider
5. Provider sends the request to the AI API (OpenAI, Anthropic, etc.)
6. Provider receives the response and transforms it into a standardized format
7. AI service returns the response to the API layer
8. API layer formats the response and sends it to the client

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Error Handling Flow

1. AI provider encounters an error (e.g., rate limit exceeded)
2. Provider layer catches the error and transforms it into a standardized AIError
3. If the error is transient, the retry mechanism attempts to retry the request
4. If the retry fails or the error is not transient, the error is propagated to the API layer
5. API layer formats the error response and sends it to the client
6. Error is logged in the audit log

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Performance Optimization Flow

1. AI service receives a request
2. Performance optimization layer checks the cache for a matching request
3. If a cache hit occurs, the cached response is returned immediately
4. If a cache miss occurs, the request is sent to the provider
5. Provider sends the request to the AI API
6. Response is received and cached for future requests
7. Performance metrics are collected and logged
8. Response is returned to the client

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Design Decisions

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Provider Abstraction

• Easy switching between providers
• Fallback to alternative providers if one is unavailable
• Comparison of results from different providers
• Future addition of new providers

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Service Specialization

• Optimized prompts for specific tasks
• Consistent response formats
• Reuse of common functionality
• Easier testing and validation

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Error Standardization

• Consistent error handling
• Improved error messages for users
• Easier debugging and monitoring
• Retry mechanisms for transient errors

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Performance Optimization

• Reduced latency through caching
• Lower costs through token optimization
• Better monitoring through performance metrics
• Improved reliability through retry mechanisms

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HIPAA Compliance

• Comprehensive audit logging
• Secure handling of sensitive data
• Access controls and authentication
• Encryption of data in transit and at rest

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Implementation Details

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Directory Structure

src/lib/ai/
├── index.ts                # Main exports
├── factory.ts              # AI service factory
├── error-handling.ts       # Error handling utilities
├── performance.ts          # Performance optimization utilities
├── analytics.ts            # Usage analytics
├── providers/
│   ├── index.ts            # Provider exports
│   ├── openai.ts           # OpenAI provider
│   └── anthropic.ts        # Anthropic provider
├── services/
│   ├── index.ts            # Service exports
│   ├── sentiment.ts        # Sentiment analysis service
│   ├── crisis.ts           # Crisis detection service
│   ├── response.ts         # Response generation service
│   └── intervention.ts     # Intervention analysis service
└── models/
    ├── index.ts            # Model exports
    ├── messages.ts         # Message types
    ├── completion.ts       # Completion types
    └── errors.ts           # Error types

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Key Interfaces

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AI Service

interface AIService {
  createChatCompletion(
    messages: AIMessage[],
    options?: AIServiceOptions,
  ): Promise<AICompletion>

  createStreamingChatCompletion(
    messages: AIMessage[],
    options?: AIServiceOptions,
  ): Promise<AIStreamingCompletion>

  getModelInfo(model: string): ModelInfo
}

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AI Provider

interface AIProvider {
  createChatCompletion(
    messages: AIMessage[],
    options?: AIServiceOptions,
  ): Promise<AICompletion>

  createStreamingChatCompletion(
    messages: AIMessage[],
    options?: AIServiceOptions,
  ): Promise<AIStreamingCompletion>

  getModelInfo(model: string): ModelInfo

  getSupportedModels(): string[]
}

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AI Error

interface AIErrorOptions {
  code: AIErrorCode
  statusCode?: number
  context?: Record<string, any>
  cause?: Error
}

class AIError extends Error {
  code: AIErrorCode
  statusCode: number
  context?: Record<string, any>
  cause?: Error

  constructor(message: string, options: AIErrorOptions)
}

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Security Considerations

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Data Privacy

• All AI requests and responses are encrypted in transit using TLS
• Sensitive data is not stored in logs or caches
• User data is anonymized where possible
• Data retention policies are enforced

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Authentication and Authorization

• All AI endpoints require authentication
• Role-based access control restricts access to AI features
• API keys for AI providers are stored securely
• Audit logging tracks all access to AI features

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Rate Limiting

• Rate limits prevent abuse of AI endpoints
• Separate rate limits for different AI features
• Gradual backoff for repeated requests
• Alerts for suspicious activity

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Content Filtering

• AI responses are filtered for harmful content
• Crisis detection identifies potential harm
• Moderation systems review high-risk interactions
• Escalation procedures for serious concerns

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Performance Considerations

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Caching

• Response caching reduces latency and costs
• Cache invalidation strategies prevent stale data
• Cache size limits prevent memory issues
• Cache hit/miss metrics track effectiveness

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Token Optimization

• Message truncation reduces token usage
• Token estimation prevents exceeding limits
• Token usage tracking identifies optimization opportunities
• Cost allocation tracks usage by feature

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Latency Management

• Timeout handling prevents hanging requests
• Performance metrics identify slow requests
• Retry mechanisms handle transient errors
• Fallback providers ensure availability

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Monitoring and Analytics

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Usage Tracking

• Request counts by endpoint, provider, and model
• Token usage by provider and model
• Response times by endpoint, provider, and model
• Error rates by endpoint, provider, and model

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Performance Metrics

• Average response time
• 95th percentile response time
• Cache hit rate
• Token usage efficiency

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Error Tracking

• Error counts by type and provider
• Retry success rate
• Rate limit hits
• Content filter triggers

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Health Checks

• Provider availability monitoring
• API endpoint health checks
• Performance degradation alerts
• Error rate thresholds

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Future Enhancements

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Additional Providers

• Google AI/Gemini integration
• Azure OpenAI integration
• DeepSeek integration
• Local LLM support

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Advanced Features

• Fine-tuning support
• Embeddings for semantic search
• Batch processing for analytics
• Model evaluation and comparison

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Performance Improvements

• Distributed caching
• Predictive prefetching
• Adaptive token optimization
• Load balancing across providers

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Security Enhancements

• Enhanced content filtering
• Improved anonymization
• Federated learning
• Differential privacy