HelloAgentsLLM.py

import os
from openai import OpenAI
from dotenv import load_dotenv
from typing import List, Dict

# 加载 .env 文件中的环境变量
load_dotenv()

class HelloAgentsLLM:
    """
    为 "Hello Agents" 定制的LLM客户端。
    它用于调用任何兼容OpenAI接口的服务，并默认使用流式响应。
    """

    def __init__(
        self,
        model: str = None,
        apiKey: str = None,
        baseUrl: str = None,
        timeout: int = None,
    ):
        """
        初始化客户端。优先使用传入参数，如果未提供，则从环境变量加载。
        """
        self.model = model or os.getenv("MODEL_NAME")
        apiKey = apiKey or os.getenv("DS_API_KEY")
        baseUrl = baseUrl or os.getenv("DS_BASE_URL")
        timeout = timeout or int(os.getenv("DS_TIMEOUT", 60))
        if not all([self.model, apiKey, baseUrl]):
            raise ValueError(
                "模型ID、API密钥和服务地址必须被提供或在.env文件中定义。"
            )
        self.client = OpenAI(api_key=apiKey, base_url=baseUrl, timeout=timeout)

    def think(
        self,
        messages: List[Dict[str, str]],
        temperature: float = 0,
    ) -> str:
        """
        调用大语言模型进行思考，并返回其响应。
        """
        print(f"🧠 正在调用 {self.model} 模型...")
        try:
            response = self.client.chat.completions.create(
                model=self.model,
                messages=messages,
                temperature=temperature,
                stream=True,
            )
            # 处理流式响应
            print("✅ 大语言模型响应成功:")
            collected_content = []
            for chunk in response:
                content = chunk.choices[0].delta.content or ""
                print(content, end="", flush=True)
                collected_content.append(content)
            print()  # 在流式输出结束后换行
            return "".join(collected_content)
        except Exception as e:
            print(f"❌ 调用LLM API时发生错误: {e}")
            return None


# --- 客户端使用示例 ---
if __name__ == '__main__':
    try:
        llmClient = HelloAgentsLLM()
        exampleMessages = [
            {
                "role": "system",
                "content": "You are a helpful assistant that writes\nPython code."
            },
            {"role": "user", "content": "你是谁"}
        ]
        print("--- 调用LLM ---")
        responseText = llmClient.think(exampleMessages)
        if responseText:
            print("\n\n--- 完整模型响应 ---")
            print(responseText)
    except ValueError as e:
        print(e)