Azure OpenAI

import os
os.environ["AZURE_API_KEY"] = "" # "my-azure-api-key"
os.environ["AZURE_API_BASE"] = "" # "https://example-endpoint.openai.azure.com"
os.environ["AZURE_API_VERSION"] = "" # "2023-05-15"

# 可选
os.environ["AZURE_AD_TOKEN"] = ""
os.environ["AZURE_API_TYPE"] = ""

from litellm import completion

## 设置 ENV 变量
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""

# azure 调用
response = completion(
    model = "azure/<your_deployment_name>", 
    messages = [{ "content": "Hello, how are you?","role": "user"}]
)

import litellm

# azure 调用
response = litellm.completion(
    model = "azure/<your deployment name>",             # model = azure/<your deployment name> 
    api_base = "",                                      # azure api base
    api_version = "",                                   # azure api version
    api_key = "",                                       # azure api key
    messages = [{"role": "user", "content": "good morning"}],
)

import litellm

# azure 调用
response = litellm.completion(
    model = "azure/<your deployment name>",             # model = azure/<your deployment name> 
    api_base = "",                                      # azure api base
    api_version = "",                                   # azure api version
    azure_ad_token="", # azure_ad_token 
    messages = [{"role": "user", "content": "good morning"}],
)

base_url=https://gpt-4-vision-resource.openai.azure.com/openai/deployments/gpt-4-vision/extensions
# base_url="{azure_endpoint}/openai/deployments/{azure_deployment}/extensions"

import os 
from litellm import completion

os.environ["AZURE_API_KEY"] = "your-api-key"

# azure call
response = completion(
            model="azure/gpt-4-vision",
            timeout=5,
            messages=[
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": "Whats in this image?"},
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": "https://avatars.githubusercontent.com/u/29436595?v=4"
                            },
                        },
                    ],
                }
            ],
            base_url="https://gpt-4-vision-resource.openai.azure.com/openai/deployments/gpt-4-vision/extensions",
            api_key=os.getenv("AZURE_VISION_API_KEY"),
            enhancements={"ocr": {"enabled": True}, "grounding": {"enabled": True}},
            dataSources=[
                {
                    "type": "AzureComputerVision",
                    "parameters": {
                        "endpoint": "https://gpt-4-vision-enhancement.cognitiveservices.azure.com/",
                        "key": os.environ["AZURE_VISION_ENHANCE_KEY"],
                    },
                }
            ],
)

import litellm

## 设置环境变量
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""

response = litellm.completion(
    model="azure_text/<your-deployment-name",
    messages=[{"role": "user", "content": "波士顿的天气怎么样？"}]
)

print(response)

from litellm import completion

## 设置环境变量
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""

# azure 调用
speech_file_path = Path(__file__).parent / "speech.mp3"
response = speech(
        model="azure/<your-deployment-name",
        voice="alloy",
        input="敏捷的棕色狐狸跃过懒狗",
    )
response.stream_to_file(speech_file_path)

pip install litellm

from litellm import Router

model_list = [{ # 模型部署列表 
"model_name": "gpt-3.5-turbo", # openai 模型名称 
"litellm_params": { # litellm 完成/嵌入调用的参数 
"model": "azure/chatgpt-v-2", 
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
},
"tpm": 240000,
"rpm": 1800
}, {
    "model_name": "gpt-3.5-turbo", # openai 模型名称 
"litellm_params": { # litellm 完成/嵌入调用的参数 
"model": "azure/chatgpt-functioncalling", 
"api_key": os.getenv("AZURE_API_KEY"),
"api_version": os.getenv("AZURE_API_VERSION"),
"api_base": os.getenv("AZURE_API_BASE")
},
"tpm": 240000,
"rpm": 1800
}, {
    "model_name": "gpt-3.5-turbo", # openai 模型名称 
"litellm_params": { # litellm 完成/嵌入调用的参数 
"model": "gpt-3.5-turbo", 
"api_key": os.getenv("OPENAI_API_KEY"),
},
"tpm": 1000000,
"rpm": 9000
}]

router = Router(model_list=model_list)

# 替换 openai.chat.completions.create
response = router.completion(model="gpt-3.5-turbo", 
messages=[{"role": "user", "content": "嘿，最近怎么样？"}]

print(response)

router = Router(model_list=model_list, 
                redis_host=os.getenv("REDIS_HOST"), 
                redis_password=os.getenv("REDIS_PASSWORD"), 
                redis_port=os.getenv("REDIS_PORT"))

print(response)

# 设置Azure环境变量
import os
os.environ['AZURE_API_KEY'] = "" # litellm从.env中读取AZURE_API_KEY并发送请求
os.environ['AZURE_API_BASE'] = "https://openai-gpt-4-test-v-1.openai.azure.com/"
os.environ['AZURE_API_VERSION'] = "2023-07-01-preview"

import litellm
import json
# 示例硬编码的虚拟函数，始终返回相同的天气信息
# 在生产环境中，这可以是你的后端API或外部API
def get_current_weather(location, unit="fahrenheit"):
    """获取给定地点的当前天气"""
    if "tokyo" in location.lower():
        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
    elif "san francisco" in location.lower():
        return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"})
    elif "paris" in location.lower():
        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
    else:
        return json.dumps({"location": location, "temperature": "unknown"})

## 步骤1：将对话和可用函数发送给模型
messages = [{"role": "user", "content": "旧金山、东京和巴黎的天气怎么样？"}]
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "获取给定地点的当前天气",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "城市和州，例如：San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]

response = litellm.completion(
    model="azure/chatgpt-functioncalling", # model = azure/<你的Azure部署名称>
    messages=messages,
    tools=tools,
    tool_choice="auto",  # auto是默认值，但我们这里显式指定
)
print("\nLLM响应1:\n", response)
response_message = response.choices[0].message
tool_calls = response.choices[0].message.tool_calls
print("\n工具选择:\n", tool_calls)