azure-openai-2025

What this skill does

# Azure OpenAI Service - 2025 Models and Features

Complete knowledge base for Azure OpenAI Service with latest 2025 models including GPT-5, GPT-4.1, reasoning models, and Azure AI Foundry integration.

## Overview

Azure OpenAI Service provides REST API access to OpenAI's most powerful models with enterprise-grade security, compliance, and regional availability.

## Latest Models (2025)

### GPT-5 Series (GA August 2025)

**Registration Required Models:**
- `gpt-5-pro`: Highest capability, complex reasoning
- `gpt-5`: Balanced performance and cost
- `gpt-5-codex`: Optimized for code generation

**No Registration Required:**
- `gpt-5-mini`: Faster, more affordable
- `gpt-5-nano`: Ultra-fast for simple tasks
- `gpt-5-chat`: Optimized for conversational use

### GPT-4.1 Series

- `gpt-4.1`: 1 million token context window
- `gpt-4.1-mini`: Efficient version with 1M context
- `gpt-4.1-nano`: Fastest variant

**Key Improvements:**
- 1,000,000 token context (vs 128K in GPT-4 Turbo)
- Better instruction following
- Reduced hallucinations
- Improved multilingual support

### Reasoning Models

**o4-mini**: Lightweight reasoning model
- Faster inference
- Lower cost
- Suitable for structured reasoning tasks

**o3**: Advanced reasoning model
- Complex problem solving
- Mathematical reasoning
- Scientific analysis

**o1**: Original reasoning model
- General-purpose reasoning
- Step-by-step explanations

**o1-mini**: Efficient reasoning
- Balanced cost and performance

### Image Generation

**GPT-image-1 (2025-04-15)**
- DALL-E 3 successor
- Higher quality images
- Better prompt understanding
- Improved safety filters

### Video Generation

**Sora (2025-05-02)**
- Text-to-video generation
- Realistic and imaginative scenes
- Up to 60 seconds of video
- Multiple camera angles and styles

### Audio Models

**gpt-4o-transcribe**: Speech-to-text powered by GPT-4o
- High accuracy transcription
- Multiple languages
- Speaker diarization

**gpt-4o-mini-transcribe**: Faster, more affordable transcription
- Good accuracy
- Lower latency
- Cost-effective

## Deploying Azure OpenAI

### Create Azure OpenAI Resource

```bash
# Create OpenAI account
az cognitiveservices account create \
  --name myopenai \
  --resource-group MyRG \
  --kind OpenAI \
  --sku S0 \
  --location eastus \
  --custom-domain myopenai \
  --public-network-access Disabled \
  --identity-type SystemAssigned

# Get endpoint and key
az cognitiveservices account show \
  --name myopenai \
  --resource-group MyRG \
  --query "properties.endpoint" \
  --output tsv

az cognitiveservices account keys list \
  --name myopenai \
  --resource-group MyRG \
  --query "key1" \
  --output tsv
```

### Deploy GPT-5 Model

```bash
# Deploy gpt-5
az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name gpt-5 \
  --model-name gpt-5 \
  --model-version latest \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 100 \
  --scale-type Standard

# Deploy gpt-5-pro (requires registration)
az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name gpt-5-pro \
  --model-name gpt-5-pro \
  --model-version latest \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 50
```

### Deploy Reasoning Models

```bash
# Deploy o3 reasoning model
az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name o3-reasoning \
  --model-name o3 \
  --model-version latest \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 50

# Deploy o4-mini
az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name o4-mini \
  --model-name o4-mini \
  --model-version latest \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 100
```

### Deploy GPT-4.1 with 1M Context

```bash
az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name gpt-4-1 \
  --model-name gpt-4.1 \
  --model-version latest \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 100
```

### Deploy Image Generation Model

```bash
az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name image-gen \
  --model-name gpt-image-1 \
  --model-version 2025-04-15 \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 10
```

### Deploy Sora Video Generation

```bash
az cognitiveservices account deployment create \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name sora \
  --model-name sora \
  --model-version 2025-05-02 \
  --model-format OpenAI \
  --sku-name Standard \
  --sku-capacity 5
```

## Using Azure OpenAI Models

### Python SDK (GPT-5)

```python
from openai import AzureOpenAI
import os

# Initialize client
client = AzureOpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
    api_version="2025-02-01-preview",
    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
)

# GPT-5 completion
response = client.chat.completions.create(
    model="gpt-5",  # deployment name
    messages=[
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    max_tokens=1000,
    temperature=0.7,
    top_p=0.95
)

print(response.choices[0].message.content)
```

### Python SDK (o3 Reasoning Model)

```python
# o3 reasoning with chain-of-thought
response = client.chat.completions.create(
    model="o3-reasoning",
    messages=[
        {"role": "system", "content": "You are an expert problem solver. Show your reasoning step-by-step."},
        {"role": "user", "content": "If a train travels 120 km in 2 hours, then speeds up to travel 180 km in the next 2 hours, what is the average speed for the entire journey?"}
    ],
    max_tokens=2000,
    temperature=0.2  # Lower temperature for reasoning tasks
)

print(response.choices[0].message.content)
```

### Python SDK (GPT-4.1 with 1M Context)

```python
# Read a large document
with open('large_document.txt', 'r') as f:
    document = f.read()

# GPT-4.1 can handle up to 1M tokens
response = client.chat.completions.create(
    model="gpt-4-1",
    messages=[
        {"role": "system", "content": "You are a document analysis expert."},
        {"role": "user", "content": f"Analyze this document and provide key insights:\n\n{document}"}
    ],
    max_tokens=4000
)

print(response.choices[0].message.content)
```

### Image Generation (GPT-image-1)

```python
# Generate image with DALL-E 3 successor
response = client.images.generate(
    model="image-gen",
    prompt="A futuristic city with flying cars and vertical gardens, cyberpunk style, highly detailed, 4K",
    size="1024x1024",
    quality="hd",
    n=1
)

image_url = response.data[0].url
print(f"Generated image: {image_url}")
```

### Video Generation (Sora)

```python
# Generate video with Sora
response = client.videos.generate(
    model="sora",
    prompt="A serene lakeside at sunset with birds flying overhead and gentle waves on the shore",
    duration=10,  # seconds
    resolution="1080p",
    fps=30
)

video_url = response.data[0].url
print(f"Generated video: {video_url}")
```

### Audio Transcription

```python
# Transcribe audio file
audio_file = open("meeting_recording.mp3", "rb")

response = client.audio.transcriptions.create(
    model="gpt-4o-transcribe",
    file=audio_file,
    language="en",
    response_format="verbose_json"
)

print(f"Transcription: {response.text}")
print(f"Duration: {response.duration}s")

# Speaker diarization
for segment in response.segments:
    print(f"[{segment.start}s - {segment.end}s] {segment.text}")
```

## Azure AI Foundry Integration

### Model Router (Automatic Model Selection)

```python
from azure.ai.foundry import ModelRouter

# Initialize model router
router = ModelRouter(
    endpoint=os.getenv("AZURE_OPENAI_EN