GPTW.TOP

import openai

openai.log = "debug"

openai.api_key = "sk-***************"#填入自己的key
openai.api_base = "https://api.pro365.top/v1"


#api_base地址和官方调用类似，只需将api.openai.com替换为api.pro365.top即可，其他部分保持不变
#例：原接口地址：https://api.openai.com/v1/chat/completions   更改为：https://api.pro365.top/v1/chat/completions 


# 非流式响应（不建议使用，尽量采用流式传输）
# completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world!"}])
# print(completion.choices[0].message.content)

def gpt_35_api_stream(messages: list):
    """为提供的对话消息创建新的回答 (流式传输)

    Args:
        messages (list): 完整的对话消息
        api_key (str): OpenAI API 密钥

    Returns:
        tuple: (results, error_desc)
    """
    try:
        response = openai.ChatCompletion.create(
            model='gpt-4',
            messages=messages,
            stream=True,
        )
        completion = {'role': '', 'content': ''}
        for event in response:
            if event['choices'][0]['finish_reason'] == 'stop':
                print(f'收到的完成数据: {completion}')
                break
            for delta_k, delta_v in event['choices'][0]['delta'].items():
                print(f'流响应数据: {delta_k} = {delta_v}')
                completion[delta_k] += delta_v
        messages.append(completion)  # 直接在传入参数 messages 中追加消息
        return (True, '')
    except Exception as err:
        return (False, f'OpenAI API 异常: {err}')

if __name__ == '__main__':
    messages = [{'role': 'user','content': '鲁迅为什么打周树人'},]
    print(gpt_35_api_stream(messages))
    print(messages)

from openai import OpenAI

client = OpenAI(
    # 输入中转Key
    api_key="sk-RJKsAtaL**********BcA9E4Bf75",
    base_url="https://api.pro365.top/v1"
)

completion = client.chat.completions.create(
    model="gpt-4-1106-preview",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "鲁迅为什么打周树人"}
    ],
    stream=True  # 是否开启流式输出
)

# 非流式输出获取结果
# print(completion.choices[0].message)
# 流式输出获取结果
for chunk in completion:
    print(chunk.choices[0].delta)