methods/utils/response.py

from abc import ABC, abstractmethod
class Responser(ABC):

    @abstractmethod
    def respond(self, system_info: str, user_prompt: str) -> str:
        pass

import openai
from openai import OpenAI

class TurboResponser(Responser):
    """OpenAI LLM responser (新版 openai 库兼容)"""

    def __init__(self, api_key: str = None, api_base: str = None, model: str = 'gpt-4o'):
        """
        初始化客户端和模型
        :param api_key: OpenAI API 密钥
        :param api_base: 可选，自定义 API base 地址
        :param model: 使用的模型
        """
        # api_key = "ak-8f3d147b2c9a5e6m0n4p8x2v7y1k3l9"
        # api_base = "https://models-proxy.stepfun-inc.com/v1"
        self.client = OpenAI(api_key=api_key, base_url=api_base, timeout=1000)
        self.model = model

    def respond(self, system_info: str, user_prompt: str, temperature: float = 0.2) -> str:
        """
        回应系统信息和用户提示
        :param system_info: 系统提示内容
        :param user_prompt: 用户输入内容
        :return: 返回模型生成的回复
        """
        messages = [
            {"role": "system", "content": system_info},
            {"role": "user", "content": user_prompt}
        ]
        if 'qwen3-235b-a22b' in self.model:
            response = self.client.chat.completions.create(
            model=self.model,
            messages=messages,
            temperature=temperature,
            max_tokens=16000,
            extra_body={
                "enable_thinking": False,
                }
            )
            return response.choices[0].message.content
            

        response = self.client.chat.completions.create(
            model=self.model,
            messages=messages,
            temperature=temperature,
            max_tokens=16000,
        )

        return response.choices[0].message.content

    def respond_complete(self, prompt, temperature=0.2):
        response = openai.Completion.create(
            prompt=prompt,
            model=self.model,
            temperature=temperature,
            max_tokens=100000
        )
        
        generated_text = response.choices[0].text.strip()
        return generated_text

import openai
from openai import OpenAI

class OpenResponser(Responser):
    """OpenAI LLM responser (新版 openai 库兼容)"""

    def __init__(self, api_key: str = None, api_base: str = None, model: str = 'gpt-4o'):
        """
        初始化客户端和模型
        :param api_key: OpenAI API 密钥
        :param api_base: 可选，自定义 API base 地址
        :param model: 使用的模型
        """
        api_key = 'ak-83f4a216c9b7e5d0n48m2p9k3l7x4c2'
        api_base = 'https://api.siliconflow.cn/v1'
        self.client = OpenAI(api_key=api_key, base_url=api_base)
        self.model = model

    def respond(self, system_info: str, user_prompt: str, temperature: float = 0) -> str:
        """
        回应系统信息和用户提示
        :param system_info: 系统提示内容
        :param user_prompt: 用户输入内容
        :return: 返回模型生成的回复
        """
        messages = [
            {"role": "system", "content": system_info},
            {"role": "user", "content": user_prompt}
        ]

        response = self.client.chat.completions.create(
            model=self.model,
            messages=messages,
            temperature=temperature
        )

        return response.choices[0].message.content


if __name__ == "__main__":
    responser = TurboResponser(model="qwen3-235b-a22b", api_base="https://models-proxy.stepfun-inc.com/v1", api_key="ak-3f8a2c9e1b7d4f6h5j2k8m3n9p4r6t7")
    anwser = responser.respond(system_info="you are a helpful assistant!",
                               user_prompt="你是什么模型")
    print(anwser)