azure-openai-2025

Name: azure-openai-2025
Author: josiahsiegel

github.com/josiahsiegel云与 DevOps59164 次安装19 周前更新

代码仓库

关于 azure-openai-2025

面向部署和使用 Azure OpenAI Service 的团队的参考技能，涵盖模型、部署、集成、安全与运维，涉及文本、推理、图像、视频和音频能力，以及 Azure AI Foundry。

主要功能

012025 年模型与能力概览
02Azure CLI 部署示例
03Python SDK 使用模式
04Azure AI Foundry 集成
05配额与容量管理
06安全与监控指南

适用场景

01为任务选择模型
02部署 Azure OpenAI 模型
03配置专用终结点和托管身份
04监控用量并管理部署容量

安装

复制下面的命令，在终端中运行。

$npx skills add https://github.com/josiahsiegel/claude-plugin-marketplace --skill azure-openai-2025

安装后在 Claude Code 中输入/azure-openai-2025确认已加载。

附带文件references/cost-monitoring.md

详细介绍

Azure OpenAI Service - 2025 年模型与功能

包含 Azure OpenAI Service 的完整知识库，涵盖最新的 2025 年模型，包括 GPT-5、GPT-4.1、推理模型以及 Azure AI Foundry 集成。

概览

Azure OpenAI Service 通过 REST API 提供对 OpenAI 最强大模型的访问，并具备企业级安全性、合规性和区域可用性。

部署 Azure OpenAI

创建 Azure OpenAI 资源

# 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

部署 GPT-5 模型

# 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

部署推理模型

# 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

部署支持 1M 上下文的 GPT-4.1

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

部署图像生成模型

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

部署 Sora 视频生成

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

使用 Azure OpenAI 模型

Python SDK（GPT-5）

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 推理模型）

# 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（支持 1M 上下文的 GPT-4.1）

# 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)

图像生成（GPT-image-1）

# 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}")

视频生成（Sora）

# 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}")

音频转录

# 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 集成

模型路由器（自动选择模型）

from azure.ai.foundry import ModelRouter

# Initialize model router
router = ModelRouter(
    endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
    credential=os.getenv("AZURE_OPENAI_API_KEY")
)

# Automatically select optimal model
response = router.complete(
    prompt="Analyze this complex scientific paper...",
    optimization_goals=["quality", "cost"],
    available_models=["gpt-5", "gpt-5-mini", "gpt-4-1"]
)

print(f"Selected model: {response.model_used}")
print(f"Response: {response.content}")
print(f"Cost: ${response.cost}")

优势：

根据提示词复杂度自动选择模型
平衡质量与成本
在保持质量的同时，最多可降低 40% 的成本

Agentic 检索（Azure AI Search 集成）

from azure.search.documents import SearchClient
from azure.core.credentials import AzureKeyCredential

# Initialize search client
search_client = SearchClient(
    endpoint=os.getenv("SEARCH_ENDPOINT"),
    index_name="documents",
    credential=AzureKeyCredential(os.getenv("SEARCH_KEY"))
)

# Agentic retrieval with Azure OpenAI
response = client.chat.completions.create(
    model="gpt-5",
    messages=[
        {"role": "system", "content": "You have access to a document search system."},
        {"role": "user", "content": "What are the company's revenue projections for Q3?"}
    ],
    tools=[{
        "type": "function",
        "function": {
            "name": "search_documents",
            "description": "Search company documents",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"}
                },
                "required": ["query"]
            }
        }
    }],
    tool_choice="auto"
)

# Process tool calls
if response.choices[0].message.tool_calls:
    for tool_call in response.choices[0].message.tool_calls:
        if tool_call.function.name == "search_documents":
            query = json.loads(tool_call.function.arguments)["query"]
            results = search_client.search(query)
            # Feed results back to model for final answer

改进：

处理复杂的多部分问题时，效果提升 40%
自动分解查询
相关性排序
生成引用

Foundry 可观测性（预览版）

from azure.ai.foundry import FoundryObservability

# Enable observability
observability = FoundryObservability(
    workspace_id=os.getenv("AI_FOUNDRY_WORKSPACE_ID"),
    enable_tracing=True,
    enable_metrics=True
)

# Monitor agent execution
with observability.trace_agent("customer_support_agent") as trace:
    response = client.chat.completions.create(
        model="gpt-5",
        messages=messages
    )

    trace.log_tool_call("search_kb", {"query": "refund policy"})
    trace.log_reasoning_step("Retrieved refund policy document")
    trace.log_token_usage(response.usage.total_tokens)

# View in Azure AI Foundry portal:
# - End-to-end trace logs
# - Reasoning steps and tool calls
# - Performance metrics
# - Cost analysis

容量和配额管理

检查配额

# List deployments with usage
az cognitiveservices account deployment list \
  --resource-group MyRG \
  --name myopenai \
  --output table

# Check usage metrics
az monitor metrics list \
  --resource $(az cognitiveservices account show -g MyRG -n myopenai --query id -o tsv) \
  --metric "TokenTransaction" \
  --start-time 2025-01-01T00:00:00Z \
  --end-time 2025-01-31T23:59:59Z \
  --interval PT1H \
  --aggregation Total

更新容量

# Scale up deployment capacity
az cognitiveservices account deployment update \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name gpt-5 \
  --sku-capacity 200

# Scale down during off-peak
az cognitiveservices account deployment update \
  --resource-group MyRG \
  --name myopenai \
  --deployment-name gpt-5 \
  --sku-capacity 50

申请提高配额

导航至 Azure Portal → Azure OpenAI 资源
打开“Quotas”窗格
选择模型和区域
点击“Request quota increase”
提供申请理由和目标容量

安全和网络

专用终结点

# Create private endpoint
az network private-endpoint create \
  --name openai-private-endpoint \
  --resource-group MyRG \
  --vnet-name MyVNet \
  --subnet PrivateEndpointSubnet \
  --private-connection-resource-id $(az cognitiveservices account show -g MyRG -n myopenai --query id -o tsv) \
  --group-id account \
  --connection-name openai-connection

# Create private DNS zone
az network private-dns zone create \
  --resource-group MyRG \
  --name privatelink.openai.azure.com

# Link to VNet
az network private-dns link vnet create \
  --resource-group MyRG \
  --zone-name privatelink.openai.azure.com \
  --name openai-dns-link \
  --virtual-network MyVNet \
  --registration-enabled false

# Create DNS zone group
az network private-endpoint dns-zone-group create \
  --resource-group MyRG \
  --endpoint-name openai-private-endpoint \
  --name default \
  --private-dns-zone privatelink.openai.azure.com \
  --zone-name privatelink.openai.azure.com

托管标识访问

# Enable system-assigned identity
az cognitiveservices account identity assign \
  --name myopenai \
  --resource-group MyRG

# Grant role to managed identity
PRINCIPAL_ID=$(az cognitiveservices account show -g MyRG -n myopenai --query identity.principalId -o tsv)

az role assignment create \
  --assignee $PRINCIPAL_ID \
  --role "Cognitive Services OpenAI User" \
  --scope /subscriptions/<sub-id>/resourceGroups/MyRG

内容筛选

# Configure content filtering
az cognitiveservices account update \
  --name myopenai \
  --resource-group MyRG \
  --set properties.customContentFilter='{
    "hate": {"severity": "medium", "enabled": true},
    "violence": {"severity": "medium", "enabled": true},
    "sexual": {"severity": "medium", "enabled": true},
    "selfHarm": {"severity": "high", "enabled": true}
  }'

成本优化、监控和警报

有关 Azure OpenAI 部署的详细成本优化策略、token 预算、配额感知路由、Azure Monitor 指标、Log Analytics 查询和警报规则，请参阅 references/cost-monitoring.md。在控制支出或配置生产环境可观测性时，请加载该参考文档。

最佳实践

✓ 使用 Model Router 自动优化成本 ✓ 实施缓存，减少重复请求 ✓ 监控令牌用量并设置预算 ✓ 在生产工作负载中使用专用终结点 ✓ 启用托管标识，而非使用 API 密钥 ✓ 配置内容筛选以确保安全 ✓ 根据实际需求合理调整容量 ✓ 使用 Foundry Observability 进行监控 ✓ 实施重试逻辑并采用指数退避 ✓ 根据任务复杂度选择合适的模型

参考资料

借助 GPT-5 和推理模型，Azure OpenAI Service 将企业级 AI 带入您的应用！

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基本信息

作者: josiahsiegel
仓库: josiahsiegel/claude-plugin-marketplace
分类: 云与 DevOps
安装次数: 164
GitHub Stars: 59

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