# 环境变量
os.environ['GITHUB_API_KEY']

from litellm import completion
import os

os.environ['GITHUB_API_KEY'] = ""
response = completion(
    model="github/llama3-8b-8192", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
)
print(response)

from litellm import completion
import os

os.environ['GITHUB_API_KEY'] = ""
response = completion(
    model="github/llama3-8b-8192", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
    stream=True
)

for chunk in response:
    print(chunk)

# 示例伪函数，硬编码返回当前天气
import json
def get_current_weather(location, unit="fahrenheit"):
    """获取指定地点的当前天气"""
    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": "system",
        "content": "你是一个函数调用的LLM，使用从get_current_weather提取的数据回答关于旧金山天气的问题。",
    },
    {
        "role": "user",
        "content": "旧金山的天气怎么样？",
    },
]
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "获取指定地点的当前天气",
            "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="github/llama3-8b-8192",
    messages=messages,
    tools=tools,
    tool_choice="auto",  # auto 是默认选项，但我们明确指定
)
print("Response\n", response)
response_message = response.choices[0].message
tool_calls = response_message.tool_calls


# 步骤2：检查模型是否想要调用函数
if tool_calls:
    # 步骤3：调用函数
    # 注意：JSON响应可能不总是有效的；务必处理错误
    available_functions = {
        "get_current_weather": get_current_weather,
    }
    messages.append(
        response_message
    )  # 将助手的回复扩展到对话中
    print("Response message\n", response_message)
    # 步骤4：将每个函数调用的信息和函数响应发送给模型
    for tool_call in tool_calls:
        function_name = tool_call.function.name
        function_to_call = available_functions[function_name]
        function_args = json.loads(tool_call.function.arguments)
        function_response = function_to_call(
            location=function_args.get("location"),
            unit=function_args.get("unit"),
        )
        messages.append(
            {
                "tool_call_id": tool_call.id,
                "role": "tool",
                "name": function_name,
                "content": function_response,
            }
        )  # 将函数响应扩展到对话中
    print(f"messages: {messages}")
    second_response = litellm.completion(
        model="github/llama3-8b-8192", messages=messages
    )  # 获取模型的新响应，模型可以看到函数响应
    print("second response\n", second_response)