没时间了，上代码

from a import chat,chat2,chat3
import time

    #print(i,end="",flush=True)
def extract_content(text, tag):
    import re
    pattern = re.compile(rf'<{tag}>(.*?)</{tag}>', re.DOTALL)
    match = pattern.search(text)
    if match:
        
        return match.group(1).strip()
    
memory=open("memory.txt","r",encoding="utf-8").read()
make_prompt = lambda memory,word: f"""

你是一个助手，每次对话时，你会知道当前基本信息和记忆。
你需要输出建议（使用<output>和</output>包裹）和修改后的记忆（使用<memory>和</memory>包裹）。

记忆如下：
<memory>{memory}</memory>

输入如下：
“{word}”
时间 {time.strftime(r"%Y-%m-%d %H:%M:%S", time.localtime())}
"""
while True:
    try:
        prompt = make_prompt(memory,input("输入："))
    except KeyboardInterrupt:
        break
    words=""
    import tqdm
    while 1:
        for i in tqdm.tqdm(chat3(prompt=prompt)):
            #print(i,end="",flush=True)
            words+=i
    

        output = extract_content(words, "output")
        memory_unsafe = extract_content(words, "memory")  
        
        if words.count("<output>")>=1 and memory_unsafe and len(memory_unsafe)>30:
            break        
        else:
            print("重新尝试")
            with open("error.txt","a",encoding="utf-8") as f:
                f.write(words)
    with open("memory.txt","w",encoding="utf-8") as f:
            if memory_unsafe and len(memory_unsafe)>30:
                memory=memory_unsafe
                f.write(memory_unsafe)
    print(output)  
    

import requests

ip="71.211.164.135"
# 生成文本
def chat(prompt):
    response = requests.post(
        f"http://{ip}:11434/api/generate",
        json={
            "model": "deepseek-r1:latest",
            "prompt": prompt,
            "stream": True  # 启用流式处理
        },
        stream=True  # 启用流式处理
    )

    # 逐行读取响应内容
    for line in response.iter_lines():
        if line:
            import json
            #print(type(line.decode('utf-8')))
            
            yield json.loads(line.decode('utf-8'))["response"]
def chat2(prompt):
    ip="127.0.0.1"
    response = requests.post(
        
        f"http://{ip}:11434/api/generate",
        json={
            "model": "qwen2:0.5b",
            "prompt": prompt,
            "stream": True  # 启用流式处理
        },
        stream=True  # 启用流式处理
    )

    # 逐行读取响应内容
    for line in response.iter_lines():
        if line:
            import json
            #print(type(line.decode('utf-8')))
            
            yield json.loads(line.decode('utf-8'))["response"]
def chat3(prompt):
    #连接阿里云百炼智能
    
    from openai import OpenAI
    import os

# 初始化OpenAI客户端
    client = OpenAI(
        # 如果没有配置环境变量，请用百炼API Key替换：api_key="sk-xxx"
        api_key = os.getenv("DASHSCOPE_API_KEY"),
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1")

    reasoning_content = ""  # 定义完整思考过程
    answer_content = ""     # 定义完整回复
    is_answering = False   # 判断是否结束思考过程并开始回复

# 创建聊天完成请求
    completion = client.chat.completions.create(
        model="deepseek-r1",  # 此处以 deepseek-r1 为例，可按需更换模型名称
        messages=[
            {"role": "user", "content": prompt}
        ],
        stream=True,
    # 解除以下注释会在最后一个chunk返回Token使用量
    # stream_options={
    #     "include_usage": True
    # }
    )

    #print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")

    for chunk in completion:
        # 如果chunk.choices为空，则打印usage
        if not chunk.choices:
            print("\nUsage:")
            print(chunk.usage)
        else:
            delta = chunk.choices[0].delta
            # 打印思考过程
            if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
                #print(delta.reasoning_content, end='', flush=True)

                yield "..."# delta.reasoning_content
                reasoning_content += delta.reasoning_content
            else:
                # 开始回复
                if delta.content != "" and is_answering == False:
                    #print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
                    is_answering = True
                # 打印回复过程
                yield delta.content
                answer_content += delta.content

# print("=" * 20 + "完整思考过程" + "=" * 20 + "\n")
# print(reasoning_content)
# print("=" * 20 + "完整回复" + "=" * 20 + "\n")
# print(answer_content)
#chat3("你好")