# Azure OpenAI Integration - Implementation Summary

## Overview

The AI service has been updated to support both OpenAI and Azure OpenAI with automatic fallback, proper environment configuration, and full support for GPT-5 models including reasoning tokens.

---

## Environment Configuration

### ✅ Complete Environment Variables (.env)

```bash
# AI Services Configuration
# Primary provider: 'openai' or 'azure'
AI_PROVIDER=azure

# OpenAI Configuration (Primary - if AI_PROVIDER=openai)
OPENAI_API_KEY=sk-your-openai-api-key-here
OPENAI_MODEL=gpt-4o-mini
OPENAI_EMBEDDING_MODEL=text-embedding-3-small
OPENAI_MAX_TOKENS=1000

# Azure OpenAI Configuration (if AI_PROVIDER=azure)
AZURE_OPENAI_ENABLED=true

# Azure OpenAI - Chat/Completion Endpoint (GPT-5)
# Each deployment has its own API key for better security and quota management
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
AZURE_OPENAI_CHAT_API_KEY=your-chat-api-key-here
AZURE_OPENAI_CHAT_MAX_TOKENS=1000
AZURE_OPENAI_REASONING_EFFORT=medium

# Azure OpenAI - Whisper/Voice 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
AZURE_OPENAI_WHISPER_API_KEY=your-whisper-api-key-here

# Azure OpenAI - 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
AZURE_OPENAI_EMBEDDINGS_API_KEY=your-embeddings-api-key-here
```

### Configuration for Your Setup

Based on your requirements:

```bash
AI_PROVIDER=azure
AZURE_OPENAI_ENABLED=true

# Chat (GPT-5 Mini) - Separate API key
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
AZURE_OPENAI_CHAT_API_KEY=[your_chat_key]
AZURE_OPENAI_REASONING_EFFORT=medium  # or 'minimal', 'low', 'high'

# Voice (Whisper) - Separate API key
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
AZURE_OPENAI_WHISPER_API_KEY=[your_whisper_key]

# Embeddings - Separate API key
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
AZURE_OPENAI_EMBEDDINGS_API_KEY=[your_embeddings_key]
```

### Why Separate API Keys?

Each Azure OpenAI deployment can have its own API key for:
- **Security**: Limit blast radius if a key is compromised
- **Quota Management**: Separate rate limits per service
- **Cost Tracking**: Monitor usage per deployment
- **Access Control**: Different team members can have access to different services

---

## AI Service Implementation

### ✅ Key Features

**1. Multi-Provider Support**
- Primary: Azure OpenAI (GPT-5)
- Fallback: OpenAI (GPT-4o-mini)
- Automatic failover if Azure unavailable

**2. GPT-5 Specific Features**
- ✅ Reasoning tokens tracking
- ✅ Configurable reasoning effort (minimal, low, medium, high)
- ✅ Extended context (272K input + 128K output = 400K total)
- ✅ Response metadata with token counts

**3. Response Format**
```typescript
interface ChatResponseDto {
  conversationId: string;
  message: string;
  timestamp: Date;
  metadata?: {
    model?: string;                    // 'gpt-5-mini' or 'gpt-4o-mini'
    provider?: 'openai' | 'azure';
    reasoningTokens?: number;          // GPT-5 only
    totalTokens?: number;
  };
}
```

**4. Azure GPT-5 Request**
```typescript
const requestBody = {
  messages: azureMessages,
  temperature: 0.7,
  max_tokens: 1000,
  stream: false,
  reasoning_effort: 'medium',  // GPT-5 specific
};
```

**5. Azure GPT-5 Response**
```typescript
{
  choices: [{
    message: { content: string },
    reasoning_tokens: number,  // NEW in GPT-5
  }],
  usage: {
    prompt_tokens: number,
    completion_tokens: number,
    reasoning_tokens: number,  // NEW in GPT-5
    total_tokens: number,
  }
}
```

---

## GPT-5 vs GPT-4 Differences

### Reasoning Tokens

**GPT-5 introduces `reasoning_tokens`**:
- Hidden tokens used for internal reasoning
- Not part of message content
- Configurable via `reasoning_effort` parameter
- Higher effort = more reasoning tokens = better quality

**Reasoning Effort Levels**:
```typescript
'minimal'  // Fastest, lowest reasoning tokens
'low'      // Quick responses with basic reasoning
'medium'   // Balanced (default)
'high'     // Most thorough, highest reasoning tokens
```

### Context Length

**GPT-5**:
- Input: 272,000 tokens (vs GPT-4's 128K)
- Output: 128,000 tokens
- Total context: 400,000 tokens

**GPT-4o**:
- Input: 128,000 tokens
- Total context: 128,000 tokens

### Token Efficiency

**GPT-5 Benefits**:
- 22% fewer output tokens vs o3
- 45% fewer tool calls
- Better performance per dollar despite reasoning overhead

### Pricing

**Azure OpenAI GPT-5**:
- Input: $1.25 / 1M tokens
- Output: $10.00 / 1M tokens
- Cached input: $0.125 / 1M (90% discount for repeated prompts)

---

## Implementation Details

### Service Initialization

The AI service now:
1. Checks `AI_PROVIDER` environment variable
2. Configures Azure OpenAI if provider is 'azure'
3. Falls back to OpenAI if Azure not configured
4. Logs which provider is active

```typescript
constructor() {
  this.aiProvider = this.configService.get('AI_PROVIDER', 'openai');

  if (this.aiProvider === 'azure') {
    // Load Azure configuration from environment
    this.azureChatEndpoint = this.configService.get('AZURE_OPENAI_CHAT_ENDPOINT');
    this.azureChatDeployment = this.configService.get('AZURE_OPENAI_CHAT_DEPLOYMENT');
    // ... more configuration
  } else {
    // Load OpenAI configuration
    this.chatModel = new ChatOpenAI({ ... });
  }
}
```

### Chat Method Flow

```typescript
async chat(userId, chatDto) {
  // 1. Validate configuration
  // 2. Get/create conversation
  // 3. Build context with user data
  // 4. Generate response based on provider:

  if (this.aiProvider === 'azure') {
    const response = await this.generateWithAzure(messages);
    // Returns: { content, reasoningTokens, totalTokens }
  } else {
    const response = await this.generateWithOpenAI(messages);
    // Returns: content string
  }

  // 5. Save conversation with token tracking
  // 6. Return response with metadata
}
```

### Azure Generation Method

```typescript
private async generateWithAzure(messages) {
  const url = `${endpoint}/openai/deployments/${deployment}/chat/completions?api-version=${apiVersion}`;

  const requestBody = {
    messages: azureMessages,
    temperature: 0.7,
    max_tokens: 1000,
    reasoning_effort: 'medium',  // GPT-5 parameter
  };

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

  return {
    content: response.data.choices[0].message.content,
    reasoningTokens: response.data.usage.reasoning_tokens,
    totalTokens: response.data.usage.total_tokens,
  };
}
```

### Automatic Fallback

If Azure fails, the service automatically retries with OpenAI:

```typescript
catch (error) {
  // Fallback to OpenAI if Azure fails
  if (this.aiProvider === 'azure' && this.chatModel) {
    this.logger.warn('Azure OpenAI failed, attempting OpenAI fallback...');
    this.aiProvider = 'openai';
    return this.chat(userId, chatDto);  // Recursive call with OpenAI
  }
  throw new BadRequestException('Failed to generate AI response');
}
```

---

## Testing the Integration

### 1. Check Provider Status

```bash
GET /api/v1/ai/provider-status
```

Response:
```json
{
  "provider": "azure",
  "model": "gpt-5-mini",
  "configured": true,
  "endpoint": "https://footprints-open-ai.openai.azure.com"
}
```

### 2. Test Chat with GPT-5

```bash
POST /api/v1/ai/chat
Authorization: Bearer {token}

{
  "message": "How much should a 3-month-old eat per feeding?"
}
```

Response:
```json
{
  "conversationId": "conv_123",
  "message": "A 3-month-old typically eats...",
  "timestamp": "2025-01-15T10:30:00Z",
  "metadata": {
    "model": "gpt-5-mini",
    "provider": "azure",
    "reasoningTokens": 145,
    "totalTokens": 523
  }
}
```

### 3. Monitor Reasoning Tokens

Check logs for GPT-5 reasoning token usage:

```
[AIService] Azure OpenAI response: {
  model: 'gpt-5-mini',
  finish_reason: 'stop',
  prompt_tokens: 256,
  completion_tokens: 122,
  reasoning_tokens: 145,  // GPT-5 reasoning overhead
  total_tokens: 523
}
```

---

## Optimizing Reasoning Effort

### When to Use Each Level

**Minimal** (`reasoning_effort: 'minimal'`):
- Simple queries
- Quick responses needed
- Cost optimization
- Use case: "What time is it?"

**Low** (`reasoning_effort: 'low'`):
- Straightforward questions
- Fast turnaround required
- Use case: "How many oz in 120ml?"

**Medium** (`reasoning_effort: 'medium'`) - **Default**:
- Balanced performance
- Most common use cases
- Use case: "Is my baby's sleep pattern normal?"

**High** (`reasoning_effort: 'high'`):
- Complex reasoning required
- Premium features
- Use case: "Analyze my baby's feeding patterns over the last month and suggest optimizations"

### Dynamic Reasoning Effort

You can adjust based on query complexity:

```typescript
// Future enhancement: Analyze query complexity
const effort = this.determineReasoningEffort(chatDto.message);

const requestBody = {
  messages: azureMessages,
  reasoning_effort: effort,  // Dynamic based on query
};
```

---

## Future Enhancements

### 1. Voice Service (Whisper)

Implement similar pattern for voice transcription:

```typescript
export class WhisperService {
  async transcribeAudio(audioBuffer: Buffer): Promise<string> {
    if (this.aiProvider === 'azure') {
      return this.transcribeWithAzure(audioBuffer);
    }
    return this.transcribeWithOpenAI(audioBuffer);
  }

  private async transcribeWithAzure(audioBuffer: Buffer) {
    const url = `${this.azureWhisperEndpoint}/openai/deployments/${this.azureWhisperDeployment}/audio/transcriptions?api-version=${this.azureWhisperApiVersion}`;

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

    const response = await axios.post(url, formData, {
      headers: {
        'api-key': this.azureWhisperApiKey,  // Separate key for Whisper
      },
    });

    return response.data.text;
  }
}
```

### 2. Embeddings Service

For pattern recognition and similarity search:

```typescript
export class EmbeddingsService {
  async createEmbedding(text: string): Promise<number[]> {
    if (this.aiProvider === 'azure') {
      return this.createEmbeddingWithAzure(text);
    }
    return this.createEmbeddingWithOpenAI(text);
  }

  private async createEmbeddingWithAzure(text: string) {
    const url = `${this.azureEmbeddingsEndpoint}/openai/deployments/${this.azureEmbeddingsDeployment}/embeddings?api-version=${this.azureEmbeddingsApiVersion}`;

    const response = await axios.post(url, { input: text }, {
      headers: {
        'api-key': this.azureEmbeddingsApiKey,  // Separate key for Embeddings
      },
    });

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

### 3. Prompt Caching

Leverage Azure's cached input pricing (90% discount):

```typescript
// Reuse identical system prompts for cost savings
const systemPrompt = `You are a helpful parenting assistant...`; // Cache this
```

### 4. Streaming Responses

For better UX with long responses:

```typescript
const requestBody = {
  messages: azureMessages,
  stream: true,  // Enable streaming
  reasoning_effort: 'medium',
};

// Handle streamed response
```

---

## Troubleshooting

### Common Issues

**1. "AI service not configured"**
- Check `AI_PROVIDER` is set to 'azure'
- Verify `AZURE_OPENAI_CHAT_API_KEY` is set (not the old `AZURE_OPENAI_API_KEY`)
- Confirm `AZURE_OPENAI_CHAT_ENDPOINT` is correct

**2. "Invalid API version"**
- GPT-5 requires `2025-04-01-preview` or later
- Update `AZURE_OPENAI_CHAT_API_VERSION`

**3. "Deployment not found"**
- Verify `AZURE_OPENAI_CHAT_DEPLOYMENT` matches Azure deployment name
- Check deployment is in same region as endpoint

**4. High token usage**
- GPT-5 reasoning tokens are additional overhead
- Reduce `reasoning_effort` if cost is concern
- Use `'minimal'` for simple queries

**5. Slow responses**
- Higher `reasoning_effort` = slower responses
- Use `'low'` or `'minimal'` for time-sensitive queries
- Consider caching common responses

### Debug Logging

Enable debug logs to see requests/responses:

```typescript
this.logger.debug('Azure OpenAI request:', {
  url,
  deployment,
  reasoning_effort,
  messageCount,
});

this.logger.debug('Azure OpenAI response:', {
  model,
  finish_reason,
  prompt_tokens,
  completion_tokens,
  reasoning_tokens,
  total_tokens,
});
```

---

## Summary

✅ **Fully Configured**:
- Environment variables for all Azure endpoints
- Chat (GPT-5), Whisper, Embeddings separately configurable
- No hardcoded values

✅ **GPT-5 Support**:
- Reasoning tokens tracked and returned
- Configurable reasoning effort (minimal/low/medium/high)
- Extended 400K context window ready

✅ **Automatic Fallback**:
- Azure → OpenAI if Azure fails
- Graceful degradation

✅ **Monitoring**:
- Detailed logging for debugging
- Token usage tracking (including reasoning tokens)
- Provider status endpoint

✅ **Production Ready**:
- Proper error handling
- Timeout configuration (30s)
- Metadata in responses

---

## Next Steps

1. **Add your actual API keys** to `.env`:
   ```bash
   AZURE_OPENAI_CHAT_API_KEY=[your_chat_key]
   AZURE_OPENAI_WHISPER_API_KEY=[your_whisper_key]
   AZURE_OPENAI_EMBEDDINGS_API_KEY=[your_embeddings_key]
   ```

2. **Restart the backend** to pick up configuration:
   ```bash
   npm run start:dev
   ```

3. **Test the integration**:
   - Check provider status endpoint
   - Send a test chat message
   - Verify reasoning tokens in response

4. **Monitor token usage**:
   - Review logs for reasoning token counts
   - Adjust `reasoning_effort` based on usage patterns
   - Consider cost optimization strategies

5. **Implement Voice & Embeddings** (optional):
   - Follow similar patterns as chat service
   - Use separate Azure endpoints already configured