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Azure AI Studio

LiteLLM 支援 Azure AI Studio 上的所有模型

使用方式​

環境變數​

import os 
os.environ["AZURE_AI_API_KEY"] = ""
os.environ["AZURE_AI_API_BASE"] = ""

範例呼叫​

from litellm import completion
import os
## set ENV variables
os.environ["AZURE_AI_API_KEY"] = "azure ai key"
os.environ["AZURE_AI_API_BASE"] = "azure ai base url" # e.g.: https://Mistral-large-dfgfj-serverless.eastus2.inference.ai.azure.com/

# predibase llama-3 call
response = completion(
    model="azure_ai/command-r-plus", 
    messages = [{ "content": "Hello, how are you?","role": "user"}]
)

傳遞額外參數 - max_tokens, temperature​

請參閱所有 litellm.completion 支援的參數 這裡

# !uv add litellm
from litellm import completion
import os
## set ENV variables
os.environ["AZURE_AI_API_KEY"] = "azure ai api key"
os.environ["AZURE_AI_API_BASE"] = "azure ai api base"

# command r plus call
response = completion(
    model="azure_ai/command-r-plus", 
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    max_tokens=20,
    temperature=0.5
)

代理

  model_list:
    - model_name: command-r-plus
      litellm_params:
        model: azure_ai/command-r-plus
        api_key: os.environ/AZURE_AI_API_KEY
        api_base: os.environ/AZURE_AI_API_BASE
        max_tokens: 20
        temperature: 0.5
  1. 啟動 proxy
$ litellm --config /path/to/config.yaml
  1. 將請求送至 LiteLLM Proxy Server
import openai
client = openai.OpenAI(
    api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
    base_url="http://0.0.0.0:4000" # litellm-proxy-base url
)

response = client.chat.completions.create(
    model="mistral",
    messages = [
        {
            "role": "user",
            "content": "what llm are you"
        }
    ],
)

print(response)

函式呼叫​

from litellm import completion

# set env
os.environ["AZURE_AI_API_KEY"] = "your-api-key"
os.environ["AZURE_AI_API_BASE"] = "your-api-base"

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]

response = completion(
    model="azure_ai/mistral-large-latest",
    messages=messages,
    tools=tools,
    tool_choice="auto",
)
# Add any assertions, here to check response args
print(response)
assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
assert isinstance(
    response.choices[0].message.tool_calls[0].function.arguments, str
)

支援的模型​

LiteLLM 支援 所有 azure ai 模型。以下是幾個範例：

模型名稱函式呼叫
Cohere command-r-pluscompletion(model="azure_ai/command-r-plus", messages)
Cohere command-rcompletion(model="azure_ai/command-r", messages)
mistral-large-latestcompletion(model="azure_ai/mistral-large-latest", messages)
AI21-Jamba-Instructcompletion(model="azure_ai/ai21-jamba-instruct", messages)

使用方式 - Azure Anthropic (Azure Foundry Claude)​

LiteLLM 透過 azure_ai/ 提供者轉送 Azure Claude 部署，因此 Azure Foundry 上的 Claude Opus 模型可繼續支援 Tool Search、Effort、串流，以及其餘進階功能集。將 AZURE_AI_API_BASE 指向 https://<resource>.services.ai.azure.com/anthropic（LiteLLM 會自動附加 /v1/messages），並使用 AZURE_AI_API_KEY 或 Azure AD 權杖進行驗證。

import os
from litellm import completion

# Configure Azure credentials
os.environ["AZURE_AI_API_KEY"] = "your-azure-ai-api-key"
os.environ["AZURE_AI_API_BASE"] = "https://my-resource.services.ai.azure.com/anthropic"

response = completion(
    model="azure_ai/claude-opus-4-1",
    messages=[{"role": "user", "content": "Explain how Azure Anthropic hosts Claude Opus differently from the public Anthropic API."}],
    max_tokens=1200,
    temperature=0.7,
    stream=True,
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Rerank 端點​

使用方式​

from litellm import rerank
import os

os.environ["AZURE_AI_API_KEY"] = "sk-.."
os.environ["AZURE_AI_API_BASE"] = "https://.."

query = "What is the capital of the United States?"
documents = [
    "Carson City is the capital city of the American state of Nevada.",
    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
    "Washington, D.C. is the capital of the United States.",
    "Capital punishment has existed in the United States since before it was a country.",
]

response = rerank(
    model="azure_ai/cohere-rerank-v3.5",
    query=query,
    documents=documents,
    top_n=3,
)
print(response)
🚅
LiteLLM Enterprise
為正式環境打造的 SSO/SAML、稽核記錄、支出追蹤、多團隊管理與防護欄。
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