maternal-app/docs/implementation-docs/azure-openai-integration.md

Azure OpenAI Integration Guide

Overview

This guide details the integration of Azure OpenAI services as a fallback option when OpenAI APIs are unavailable. The application supports both OpenAI and Azure OpenAI endpoints with automatic failover.

Configuration

Environment Variables

Add the following to your .env file:

# OpenAI Configuration (Primary)
OPENAI_API_KEY=your_openai_key
OPENAI_MODEL=gpt-4o
OPENAI_EMBEDDING_MODEL=text-embedding-3-small

# Azure OpenAI Configuration (Fallback)
AZURE_OPENAI_ENABLED=true
AZURE_OPENAI_API_KEY=your_azure_key

# Chat Endpoint
AZURE_OPENAI_CHAT_ENDPOINT=https://footprints-open-ai.openai.azure.com
AZURE_OPENAI_CHAT_DEPLOYMENT=gpt-5-mini
AZURE_OPENAI_CHAT_API_VERSION=2025-04-01-preview

# Voice/Whisper Endpoint
AZURE_OPENAI_WHISPER_ENDPOINT=https://footprints-open-ai.openai.azure.com
AZURE_OPENAI_WHISPER_DEPLOYMENT=whisper
AZURE_OPENAI_WHISPER_API_VERSION=2025-04-01-preview

# Embeddings Endpoint
AZURE_OPENAI_EMBEDDINGS_ENDPOINT=https://footprints-ai.openai.azure.com
AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT=Text-Embedding-ada-002-V2
AZURE_OPENAI_EMBEDDINGS_API_VERSION=2023-05-15

Implementation

Phase 3: Voice Input Integration (Whisper)

Update the Voice Service to support Azure OpenAI Whisper:

// src/modules/voice/services/whisper.service.ts
import { Injectable, Logger } from '@nestjs/common';
import { ConfigService } from '@nestjs/config';
import OpenAI from 'openai';
import axios from 'axios';

@Injectable()
export class WhisperService {
  private readonly logger = new Logger(WhisperService.name);
  private openai: OpenAI;
  private azureEnabled: boolean;

  constructor(private configService: ConfigService) {
    // Initialize OpenAI client
    this.openai = new OpenAI({
      apiKey: this.configService.get('OPENAI_API_KEY'),
    });
    
    this.azureEnabled = this.configService.get('AZURE_OPENAI_ENABLED') === 'true';
  }

  async transcribeAudio(audioBuffer: Buffer, language?: string): Promise<string> {
    try {
      // Try OpenAI first
      return await this.transcribeWithOpenAI(audioBuffer, language);
    } catch (error) {
      this.logger.warn('OpenAI transcription failed, trying Azure OpenAI', error.message);
      
      if (this.azureEnabled) {
        return await this.transcribeWithAzure(audioBuffer, language);
      }
      
      throw error;
    }
  }

  private async transcribeWithOpenAI(audioBuffer: Buffer, language?: string): Promise<string> {
    const file = new File([audioBuffer], 'audio.wav', { type: 'audio/wav' });
    
    const response = await this.openai.audio.transcriptions.create({
      file,
      model: 'whisper-1',
      language: language || 'en',
    });

    return response.text;
  }

  private async transcribeWithAzure(audioBuffer: Buffer, language?: string): Promise<string> {
    const endpoint = this.configService.get('AZURE_OPENAI_WHISPER_ENDPOINT');
    const deployment = this.configService.get('AZURE_OPENAI_WHISPER_DEPLOYMENT');
    const apiVersion = this.configService.get('AZURE_OPENAI_WHISPER_API_VERSION');
    const apiKey = this.configService.get('AZURE_OPENAI_API_KEY');

    const url = `${endpoint}/openai/deployments/${deployment}/audio/transcriptions?api-version=${apiVersion}`;

    const formData = new FormData();
    formData.append('file', new Blob([audioBuffer]), 'audio.wav');
    if (language) {
      formData.append('language', language);
    }

    const response = await axios.post(url, formData, {
      headers: {
        'api-key': apiKey,
        'Content-Type': 'multipart/form-data',
      },
    });

    return response.data.text;
  }
}

Phase 4: AI Assistant (Chat Completion)

Update the AI Service to support Azure OpenAI chat with GPT-5 models:

// src/modules/ai/services/ai.service.ts
import { Injectable, Logger } from '@nestjs/common';
import { ConfigService } from '@nestjs/config';
import OpenAI from 'openai';
import axios from 'axios';

@Injectable()
export class AIService {
  private readonly logger = new Logger(AIService.name);
  private openai: OpenAI;
  private azureEnabled: boolean;

  constructor(private configService: ConfigService) {
    this.openai = new OpenAI({
      apiKey: this.configService.get('OPENAI_API_KEY'),
    });
    
    this.azureEnabled = this.configService.get('AZURE_OPENAI_ENABLED') === 'true';
  }

  async generateResponse(
    messages: Array<{ role: string; content: string }>,
    temperature: number = 0.7,
  ): Promise<string> {
    try {
      // Try OpenAI first
      return await this.generateWithOpenAI(messages, temperature);
    } catch (error) {
      this.logger.warn('OpenAI chat failed, trying Azure OpenAI', error.message);
      
      if (this.azureEnabled) {
        return await this.generateWithAzure(messages, temperature);
      }
      
      throw error;
    }
  }

  private async generateWithOpenAI(
    messages: Array<{ role: string; content: string }>,
    temperature: number,
  ): Promise<string> {
    const response = await this.openai.chat.completions.create({
      model: this.configService.get('OPENAI_MODEL', 'gpt-4o'),
      messages: messages as any,
      temperature,
      max_tokens: 1000,
    });

    return response.choices[0].message.content;
  }

  private async generateWithAzure(
    messages: Array<{ role: string; content: string }>,
    temperature: number,
  ): Promise<string> {
    const endpoint = this.configService.get('AZURE_OPENAI_CHAT_ENDPOINT');
    const deployment = this.configService.get('AZURE_OPENAI_CHAT_DEPLOYMENT');
    const apiVersion = this.configService.get('AZURE_OPENAI_CHAT_API_VERSION');
    const apiKey = this.configService.get('AZURE_OPENAI_API_KEY');

    // NOTE: GPT-5 models use a different API format than GPT-4
    // The response structure includes additional metadata
    const url = `${endpoint}/openai/deployments/${deployment}/chat/completions?api-version=${apiVersion}`;

    const response = await axios.post(
      url,
      {
        messages,
        temperature,
        max_tokens: 1000,
        // GPT-5 specific parameters
        stream: false,
        // Optional GPT-5 features:
        // reasoning_effort: 'medium', // 'low', 'medium', 'high'
        // response_format: { type: 'text' }, // or 'json_object' for structured output
      },
      {
        headers: {
          'api-key': apiKey,
          'Content-Type': 'application/json',
        },
      },
    );

    // GPT-5 response structure may include reasoning tokens
    // Extract the actual message content
    return response.data.choices[0].message.content;
  }
}

Phase 5: Pattern Recognition (Embeddings)

Update the Embeddings Service for pattern analysis:

// src/modules/analytics/services/embeddings.service.ts
import { Injectable, Logger } from '@nestjs/common';
import { ConfigService } from '@nestjs/config';
import OpenAI from 'openai';
import axios from 'axios';

@Injectable()
export class EmbeddingsService {
  private readonly logger = new Logger(EmbeddingsService.name);
  private openai: OpenAI;
  private azureEnabled: boolean;

  constructor(private configService: ConfigService) {
    this.openai = new OpenAI({
      apiKey: this.configService.get('OPENAI_API_KEY'),
    });
    
    this.azureEnabled = this.configService.get('AZURE_OPENAI_ENABLED') === 'true';
  }

  async createEmbedding(text: string): Promise<number[]> {
    try {
      // Try OpenAI first
      return await this.createEmbeddingWithOpenAI(text);
    } catch (error) {
      this.logger.warn('OpenAI embeddings failed, trying Azure OpenAI', error.message);
      
      if (this.azureEnabled) {
        return await this.createEmbeddingWithAzure(text);
      }
      
      throw error;
    }
  }

  private async createEmbeddingWithOpenAI(text: string): Promise<number[]> {
    const response = await this.openai.embeddings.create({
      model: this.configService.get('OPENAI_EMBEDDING_MODEL', 'text-embedding-3-small'),
      input: text,
    });

    return response.data[0].embedding;
  }

  private async createEmbeddingWithAzure(text: string): Promise<number[]> {
    const endpoint = this.configService.get('AZURE_OPENAI_EMBEDDINGS_ENDPOINT');
    const deployment = this.configService.get('AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT');
    const apiVersion = this.configService.get('AZURE_OPENAI_EMBEDDINGS_API_VERSION');
    const apiKey = this.configService.get('AZURE_OPENAI_API_KEY');

    const url = `${endpoint}/openai/deployments/${deployment}/embeddings?api-version=${apiVersion}`;

    const response = await axios.post(
      url,
      {
        input: text,
      },
      {
        headers: {
          'api-key': apiKey,
          'Content-Type': 'application/json',
        },
      },
    );

    return response.data.data[0].embedding;
  }

  async calculateSimilarity(embedding1: number[], embedding2: number[]): Promise<number> {
    // Cosine similarity calculation
    const dotProduct = embedding1.reduce((sum, val, i) => sum + val * embedding2[i], 0);
    const magnitude1 = Math.sqrt(embedding1.reduce((sum, val) => sum + val * val, 0));
    const magnitude2 = Math.sqrt(embedding2.reduce((sum, val) => sum + val * val, 0));
    
    return dotProduct / (magnitude1 * magnitude2);
  }
}

GPT-5 Model Differences

Key Changes from GPT-4

1. Reasoning Capabilities: GPT-5 includes enhanced reasoning with configurable effort levels

   • reasoning_effort: 'low' | 'medium' | 'high'
   • Higher effort levels produce more thorough, step-by-step reasoning

2. Response Metadata: GPT-5 responses may include reasoning tokens

   {
     choices: [{
       message: { content: string },
       reasoning_tokens: number, // New in GPT-5
       finish_reason: string
     }],
     usage: {
       prompt_tokens: number,
       completion_tokens: number,
       reasoning_tokens: number, // New in GPT-5
       total_tokens: number
     }
   }
   

3. Structured Output: Enhanced JSON mode support

   {
     response_format: {
       type: 'json_schema',
       json_schema: {
         name: 'response',
         schema: { /* your schema */ }
       }
     }
   }
   

4. API Version: Use 2025-04-01-preview for GPT-5 features

Testing

Unit Tests

// src/modules/ai/__tests__/ai.service.spec.ts
describe('AIService', () => {
  describe('Azure OpenAI Fallback', () => {
    it('should fallback to Azure when OpenAI fails', async () => {
      // Mock OpenAI failure
      mockOpenAI.chat.completions.create.mockRejectedValue(new Error('API Error'));
      
      // Mock Azure success
      mockAxios.post.mockResolvedValue({
        data: {
          choices: [{ message: { content: 'Response from Azure' } }]
        }
      });

      const result = await aiService.generateResponse([
        { role: 'user', content: 'Hello' }
      ]);

      expect(result).toBe('Response from Azure');
      expect(mockAxios.post).toHaveBeenCalledWith(
        expect.stringContaining('gpt-5-mini'),
        expect.any(Object),
        expect.any(Object)
      );
    });
  });
});

Error Handling

// src/common/exceptions/ai-service.exception.ts
export class AIServiceException extends HttpException {
  constructor(
    message: string,
    public provider: 'openai' | 'azure',
    public originalError?: Error,
  ) {
    super(
      {
        statusCode: HttpStatus.SERVICE_UNAVAILABLE,
        error: 'AI_SERVICE_UNAVAILABLE',
        message,
        provider,
      },
      HttpStatus.SERVICE_UNAVAILABLE,
    );
  }
}

Monitoring

Add logging for failover events:

// Log when failover occurs
this.logger.warn('OpenAI service unavailable, switching to Azure OpenAI', {
  endpoint: 'chat',
  model: deployment,
  requestId: context.requestId,
});

// Track usage metrics
this.metricsService.increment('ai.provider.azure.requests');
this.metricsService.increment('ai.provider.openai.failures');

Cost Optimization

1. Primary/Fallback Strategy: Use OpenAI as primary to take advantage of potentially lower costs
2. Rate Limiting: Implement exponential backoff before failover
3. Circuit Breaker: After 5 consecutive failures, switch primary provider temporarily
4. Token Tracking: Monitor token usage across both providers

// src/modules/ai/services/circuit-breaker.service.ts
export class CircuitBreakerService {
  private failureCount = 0;
  private readonly threshold = 5;
  private circuitOpen = false;
  private readonly resetTimeout = 60000; // 1 minute

  async execute<T>(
    primaryFn: () => Promise<T>,
    fallbackFn: () => Promise<T>,
  ): Promise<T> {
    if (this.circuitOpen) {
      return fallbackFn();
    }

    try {
      const result = await primaryFn();
      this.failureCount = 0;
      return result;
    } catch (error) {
      this.failureCount++;
      
      if (this.failureCount >= this.threshold) {
        this.openCircuit();
      }
      
      return fallbackFn();
    }
  }

  private openCircuit() {
    this.circuitOpen = true;
    setTimeout(() => {
      this.circuitOpen = false;
      this.failureCount = 0;
    }, this.resetTimeout);
  }
}

Security Considerations

1. API Key Rotation: Store keys in secure vault (AWS Secrets Manager, Azure Key Vault)
2. Network Security: Use private endpoints when available
3. Rate Limiting: Implement per-user and per-endpoint rate limits
4. Audit Logging: Log all AI requests with user context for compliance

Deployment Checklist

• Environment variables configured for both OpenAI and Azure
• API keys validated and working
• Fallback logic tested in staging
• Monitoring and alerting configured
• Rate limiting implemented
• Circuit breaker tested
• Error handling covers all failure scenarios
• Cost tracking enabled
• Security review completed