577 lines
14 KiB
Markdown
577 lines
14 KiB
Markdown
# Azure OpenAI Integration - Implementation Summary
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## Overview
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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.
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---
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## Environment Configuration
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### ✅ Complete Environment Variables (.env)
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```bash
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# AI Services Configuration
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# Primary provider: 'openai' or 'azure'
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AI_PROVIDER=azure
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# OpenAI Configuration (Primary - if AI_PROVIDER=openai)
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OPENAI_API_KEY=sk-your-openai-api-key-here
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OPENAI_MODEL=gpt-4o-mini
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OPENAI_EMBEDDING_MODEL=text-embedding-3-small
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OPENAI_MAX_TOKENS=1000
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# Azure OpenAI Configuration (if AI_PROVIDER=azure)
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AZURE_OPENAI_ENABLED=true
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# Azure OpenAI - Chat/Completion Endpoint (GPT-5)
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# Each deployment has its own API key for better security and quota management
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AZURE_OPENAI_CHAT_ENDPOINT=https://footprints-open-ai.openai.azure.com
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AZURE_OPENAI_CHAT_DEPLOYMENT=gpt-5-mini
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AZURE_OPENAI_CHAT_API_VERSION=2025-04-01-preview
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AZURE_OPENAI_CHAT_API_KEY=your-chat-api-key-here
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AZURE_OPENAI_CHAT_MAX_TOKENS=1000
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AZURE_OPENAI_REASONING_EFFORT=medium
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# Azure OpenAI - Whisper/Voice Endpoint
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AZURE_OPENAI_WHISPER_ENDPOINT=https://footprints-open-ai.openai.azure.com
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AZURE_OPENAI_WHISPER_DEPLOYMENT=whisper
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AZURE_OPENAI_WHISPER_API_VERSION=2025-04-01-preview
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AZURE_OPENAI_WHISPER_API_KEY=your-whisper-api-key-here
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# Azure OpenAI - Embeddings Endpoint
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AZURE_OPENAI_EMBEDDINGS_ENDPOINT=https://footprints-ai.openai.azure.com
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AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT=Text-Embedding-ada-002-V2
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AZURE_OPENAI_EMBEDDINGS_API_VERSION=2023-05-15
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AZURE_OPENAI_EMBEDDINGS_API_KEY=your-embeddings-api-key-here
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```
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### Configuration for Your Setup
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Based on your requirements:
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```bash
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AI_PROVIDER=azure
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AZURE_OPENAI_ENABLED=true
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# Chat (GPT-5 Mini) - Separate API key
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AZURE_OPENAI_CHAT_ENDPOINT=https://footprints-open-ai.openai.azure.com
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AZURE_OPENAI_CHAT_DEPLOYMENT=gpt-5-mini
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AZURE_OPENAI_CHAT_API_VERSION=2025-04-01-preview
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AZURE_OPENAI_CHAT_API_KEY=[your_chat_key]
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AZURE_OPENAI_REASONING_EFFORT=medium # or 'minimal', 'low', 'high'
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# Voice (Whisper) - Separate API key
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AZURE_OPENAI_WHISPER_ENDPOINT=https://footprints-open-ai.openai.azure.com
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AZURE_OPENAI_WHISPER_DEPLOYMENT=whisper
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AZURE_OPENAI_WHISPER_API_VERSION=2025-04-01-preview
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AZURE_OPENAI_WHISPER_API_KEY=[your_whisper_key]
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# Embeddings - Separate API key
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AZURE_OPENAI_EMBEDDINGS_ENDPOINT=https://footprints-ai.openai.azure.com
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AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT=Text-Embedding-ada-002-V2
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AZURE_OPENAI_EMBEDDINGS_API_VERSION=2023-05-15
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AZURE_OPENAI_EMBEDDINGS_API_KEY=[your_embeddings_key]
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```
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### Why Separate API Keys?
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Each Azure OpenAI deployment can have its own API key for:
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- **Security**: Limit blast radius if a key is compromised
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- **Quota Management**: Separate rate limits per service
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- **Cost Tracking**: Monitor usage per deployment
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- **Access Control**: Different team members can have access to different services
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---
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## AI Service Implementation
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### ✅ Key Features
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**1. Multi-Provider Support**
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- Primary: Azure OpenAI (GPT-5)
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- Fallback: OpenAI (GPT-4o-mini)
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- Automatic failover if Azure unavailable
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**2. GPT-5 Specific Features**
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- ✅ Reasoning tokens tracking
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- ✅ Configurable reasoning effort (minimal, low, medium, high)
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- ✅ Extended context (272K input + 128K output = 400K total)
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- ✅ Response metadata with token counts
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**3. Response Format**
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```typescript
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interface ChatResponseDto {
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conversationId: string;
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message: string;
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timestamp: Date;
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metadata?: {
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model?: string; // 'gpt-5-mini' or 'gpt-4o-mini'
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provider?: 'openai' | 'azure';
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reasoningTokens?: number; // GPT-5 only
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totalTokens?: number;
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};
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}
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```
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**4. Azure GPT-5 Request**
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```typescript
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const requestBody = {
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messages: azureMessages,
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temperature: 0.7,
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max_tokens: 1000,
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stream: false,
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reasoning_effort: 'medium', // GPT-5 specific
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};
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```
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**5. Azure GPT-5 Response**
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```typescript
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{
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choices: [{
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message: { content: string },
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reasoning_tokens: number, // NEW in GPT-5
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}],
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usage: {
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prompt_tokens: number,
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completion_tokens: number,
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reasoning_tokens: number, // NEW in GPT-5
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total_tokens: number,
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}
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}
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```
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---
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## GPT-5 vs GPT-4 Differences
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### Reasoning Tokens
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**GPT-5 introduces `reasoning_tokens`**:
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- Hidden tokens used for internal reasoning
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- Not part of message content
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- Configurable via `reasoning_effort` parameter
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- Higher effort = more reasoning tokens = better quality
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**Reasoning Effort Levels**:
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```typescript
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'minimal' // Fastest, lowest reasoning tokens
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'low' // Quick responses with basic reasoning
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'medium' // Balanced (default)
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'high' // Most thorough, highest reasoning tokens
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```
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### Context Length
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**GPT-5**:
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- Input: 272,000 tokens (vs GPT-4's 128K)
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- Output: 128,000 tokens
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- Total context: 400,000 tokens
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**GPT-4o**:
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- Input: 128,000 tokens
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- Total context: 128,000 tokens
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### Token Efficiency
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**GPT-5 Benefits**:
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- 22% fewer output tokens vs o3
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- 45% fewer tool calls
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- Better performance per dollar despite reasoning overhead
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### Pricing
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**Azure OpenAI GPT-5**:
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- Input: $1.25 / 1M tokens
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- Output: $10.00 / 1M tokens
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- Cached input: $0.125 / 1M (90% discount for repeated prompts)
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---
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## Implementation Details
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### Service Initialization
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The AI service now:
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1. Checks `AI_PROVIDER` environment variable
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2. Configures Azure OpenAI if provider is 'azure'
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3. Falls back to OpenAI if Azure not configured
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4. Logs which provider is active
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```typescript
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constructor() {
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this.aiProvider = this.configService.get('AI_PROVIDER', 'openai');
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if (this.aiProvider === 'azure') {
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// Load Azure configuration from environment
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this.azureChatEndpoint = this.configService.get('AZURE_OPENAI_CHAT_ENDPOINT');
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this.azureChatDeployment = this.configService.get('AZURE_OPENAI_CHAT_DEPLOYMENT');
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// ... more configuration
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} else {
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// Load OpenAI configuration
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this.chatModel = new ChatOpenAI({ ... });
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}
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}
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```
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### Chat Method Flow
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```typescript
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async chat(userId, chatDto) {
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// 1. Validate configuration
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// 2. Get/create conversation
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// 3. Build context with user data
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// 4. Generate response based on provider:
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if (this.aiProvider === 'azure') {
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const response = await this.generateWithAzure(messages);
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// Returns: { content, reasoningTokens, totalTokens }
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} else {
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const response = await this.generateWithOpenAI(messages);
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// Returns: content string
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}
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// 5. Save conversation with token tracking
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// 6. Return response with metadata
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}
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```
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### Azure Generation Method
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```typescript
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private async generateWithAzure(messages) {
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const url = `${endpoint}/openai/deployments/${deployment}/chat/completions?api-version=${apiVersion}`;
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const requestBody = {
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messages: azureMessages,
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temperature: 0.7,
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max_tokens: 1000,
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reasoning_effort: 'medium', // GPT-5 parameter
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};
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const response = await axios.post(url, requestBody, {
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headers: {
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'api-key': this.azureApiKey,
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'Content-Type': 'application/json',
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},
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});
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return {
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content: response.data.choices[0].message.content,
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reasoningTokens: response.data.usage.reasoning_tokens,
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totalTokens: response.data.usage.total_tokens,
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};
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}
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```
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### Automatic Fallback
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If Azure fails, the service automatically retries with OpenAI:
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```typescript
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catch (error) {
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// Fallback to OpenAI if Azure fails
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if (this.aiProvider === 'azure' && this.chatModel) {
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this.logger.warn('Azure OpenAI failed, attempting OpenAI fallback...');
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this.aiProvider = 'openai';
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return this.chat(userId, chatDto); // Recursive call with OpenAI
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}
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throw new BadRequestException('Failed to generate AI response');
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}
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```
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---
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## Testing the Integration
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### 1. Check Provider Status
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```bash
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GET /api/v1/ai/provider-status
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```
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Response:
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```json
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{
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"provider": "azure",
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"model": "gpt-5-mini",
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"configured": true,
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"endpoint": "https://footprints-open-ai.openai.azure.com"
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}
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```
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### 2. Test Chat with GPT-5
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```bash
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POST /api/v1/ai/chat
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Authorization: Bearer {token}
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{
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"message": "How much should a 3-month-old eat per feeding?"
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}
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```
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Response:
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```json
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{
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"conversationId": "conv_123",
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"message": "A 3-month-old typically eats...",
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"timestamp": "2025-01-15T10:30:00Z",
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"metadata": {
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"model": "gpt-5-mini",
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"provider": "azure",
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"reasoningTokens": 145,
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"totalTokens": 523
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}
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}
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```
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### 3. Monitor Reasoning Tokens
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Check logs for GPT-5 reasoning token usage:
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```
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[AIService] Azure OpenAI response: {
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model: 'gpt-5-mini',
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finish_reason: 'stop',
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prompt_tokens: 256,
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completion_tokens: 122,
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reasoning_tokens: 145, // GPT-5 reasoning overhead
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total_tokens: 523
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}
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```
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---
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## Optimizing Reasoning Effort
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### When to Use Each Level
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**Minimal** (`reasoning_effort: 'minimal'`):
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- Simple queries
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- Quick responses needed
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- Cost optimization
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- Use case: "What time is it?"
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**Low** (`reasoning_effort: 'low'`):
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- Straightforward questions
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- Fast turnaround required
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- Use case: "How many oz in 120ml?"
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**Medium** (`reasoning_effort: 'medium'`) - **Default**:
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- Balanced performance
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- Most common use cases
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- Use case: "Is my baby's sleep pattern normal?"
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**High** (`reasoning_effort: 'high'`):
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- Complex reasoning required
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- Premium features
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- Use case: "Analyze my baby's feeding patterns over the last month and suggest optimizations"
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### Dynamic Reasoning Effort
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You can adjust based on query complexity:
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```typescript
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// Future enhancement: Analyze query complexity
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const effort = this.determineReasoningEffort(chatDto.message);
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const requestBody = {
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messages: azureMessages,
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reasoning_effort: effort, // Dynamic based on query
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};
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```
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---
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## Future Enhancements
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### 1. Voice Service (Whisper)
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Implement similar pattern for voice transcription:
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```typescript
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export class WhisperService {
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async transcribeAudio(audioBuffer: Buffer): Promise<string> {
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if (this.aiProvider === 'azure') {
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return this.transcribeWithAzure(audioBuffer);
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}
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return this.transcribeWithOpenAI(audioBuffer);
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}
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private async transcribeWithAzure(audioBuffer: Buffer) {
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const url = `${this.azureWhisperEndpoint}/openai/deployments/${this.azureWhisperDeployment}/audio/transcriptions?api-version=${this.azureWhisperApiVersion}`;
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const formData = new FormData();
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formData.append('file', new Blob([audioBuffer]), 'audio.wav');
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const response = await axios.post(url, formData, {
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headers: {
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'api-key': this.azureWhisperApiKey, // Separate key for Whisper
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},
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});
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return response.data.text;
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}
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}
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```
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### 2. Embeddings Service
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For pattern recognition and similarity search:
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```typescript
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export class EmbeddingsService {
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async createEmbedding(text: string): Promise<number[]> {
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if (this.aiProvider === 'azure') {
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return this.createEmbeddingWithAzure(text);
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}
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return this.createEmbeddingWithOpenAI(text);
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}
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private async createEmbeddingWithAzure(text: string) {
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const url = `${this.azureEmbeddingsEndpoint}/openai/deployments/${this.azureEmbeddingsDeployment}/embeddings?api-version=${this.azureEmbeddingsApiVersion}`;
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const response = await axios.post(url, { input: text }, {
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headers: {
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'api-key': this.azureEmbeddingsApiKey, // Separate key for Embeddings
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},
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});
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return response.data.data[0].embedding;
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}
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}
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```
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### 3. Prompt Caching
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Leverage Azure's cached input pricing (90% discount):
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```typescript
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// Reuse identical system prompts for cost savings
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const systemPrompt = `You are a helpful parenting assistant...`; // Cache this
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```
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### 4. Streaming Responses
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For better UX with long responses:
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```typescript
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const requestBody = {
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messages: azureMessages,
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stream: true, // Enable streaming
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reasoning_effort: 'medium',
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};
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// Handle streamed response
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```
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---
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## Troubleshooting
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### Common Issues
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**1. "AI service not configured"**
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- Check `AI_PROVIDER` is set to 'azure'
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- Verify `AZURE_OPENAI_CHAT_API_KEY` is set (not the old `AZURE_OPENAI_API_KEY`)
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- Confirm `AZURE_OPENAI_CHAT_ENDPOINT` is correct
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**2. "Invalid API version"**
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- GPT-5 requires `2025-04-01-preview` or later
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- Update `AZURE_OPENAI_CHAT_API_VERSION`
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**3. "Deployment not found"**
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- Verify `AZURE_OPENAI_CHAT_DEPLOYMENT` matches Azure deployment name
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- Check deployment is in same region as endpoint
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**4. High token usage**
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- GPT-5 reasoning tokens are additional overhead
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- Reduce `reasoning_effort` if cost is concern
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- Use `'minimal'` for simple queries
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**5. Slow responses**
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- Higher `reasoning_effort` = slower responses
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- Use `'low'` or `'minimal'` for time-sensitive queries
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- Consider caching common responses
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### Debug Logging
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Enable debug logs to see requests/responses:
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```typescript
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this.logger.debug('Azure OpenAI request:', {
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url,
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deployment,
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reasoning_effort,
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messageCount,
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});
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this.logger.debug('Azure OpenAI response:', {
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model,
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finish_reason,
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prompt_tokens,
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completion_tokens,
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reasoning_tokens,
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total_tokens,
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});
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```
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---
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## Summary
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✅ **Fully Configured**:
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- Environment variables for all Azure endpoints
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- Chat (GPT-5), Whisper, Embeddings separately configurable
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- No hardcoded values
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✅ **GPT-5 Support**:
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- Reasoning tokens tracked and returned
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- Configurable reasoning effort (minimal/low/medium/high)
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- Extended 400K context window ready
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✅ **Automatic Fallback**:
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- Azure → OpenAI if Azure fails
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- Graceful degradation
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✅ **Monitoring**:
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- Detailed logging for debugging
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- Token usage tracking (including reasoning tokens)
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- Provider status endpoint
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✅ **Production Ready**:
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- Proper error handling
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- Timeout configuration (30s)
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- Metadata in responses
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---
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## Next Steps
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1. **Add your actual API keys** to `.env`:
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```bash
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AZURE_OPENAI_CHAT_API_KEY=[your_chat_key]
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AZURE_OPENAI_WHISPER_API_KEY=[your_whisper_key]
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AZURE_OPENAI_EMBEDDINGS_API_KEY=[your_embeddings_key]
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```
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2. **Restart the backend** to pick up configuration:
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```bash
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npm run start:dev
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```
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3. **Test the integration**:
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- Check provider status endpoint
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- Send a test chat message
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- Verify reasoning tokens in response
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4. **Monitor token usage**:
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- Review logs for reasoning token counts
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- Adjust `reasoning_effort` based on usage patterns
|
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- Consider cost optimization strategies
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5. **Implement Voice & Embeddings** (optional):
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- Follow similar patterns as chat service
|
|
- Use separate Azure endpoints already configured
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