import markdown import mdtex2html import threading import importlib import traceback import importlib import inspect import re from latex2mathml.converter import convert as tex2mathml from functools import wraps, lru_cache def ArgsGeneralWrapper(f): """ 装饰器函数,用于重组输入参数,改变输入参数的顺序与结构。 """ def decorated(txt, txt2, *args, **kwargs): txt_passon = txt if txt == "" and txt2 != "": txt_passon = txt2 yield from f(txt_passon, *args, **kwargs) return decorated def get_reduce_token_percent(text): try: # text = "maximum context length is 4097 tokens. However, your messages resulted in 4870 tokens" pattern = r"(\d+)\s+tokens\b" match = re.findall(pattern, text) EXCEED_ALLO = 500 # 稍微留一点余地,否则在回复时会因余量太少出问题 max_limit = float(match[0]) - EXCEED_ALLO current_tokens = float(match[1]) ratio = max_limit/current_tokens assert ratio > 0 and ratio < 1 return ratio, str(int(current_tokens-max_limit)) except: return 0.5, '不详' def predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[], sys_prompt='', long_connection=True): """ 调用简单的predict_no_ui接口,但是依然保留了些许界面心跳功能,当对话太长时,会自动采用二分法截断 i_say: 当前输入 i_say_show_user: 显示到对话界面上的当前输入,例如,输入整个文件时,你绝对不想把文件的内容都糊到对话界面上 chatbot: 对话界面句柄 top_p, temperature: gpt参数 history: gpt参数 对话历史 sys_prompt: gpt参数 sys_prompt long_connection: 是否采用更稳定的连接方式(推荐) """ import time from request_llm.bridge_chatgpt import predict_no_ui, predict_no_ui_long_connection from toolbox import get_conf TIMEOUT_SECONDS, MAX_RETRY = get_conf('TIMEOUT_SECONDS', 'MAX_RETRY') # 多线程的时候,需要一个mutable结构在不同线程之间传递信息 # list就是最简单的mutable结构,我们第一个位置放gpt输出,第二个位置传递报错信息 mutable = [None, ''] # multi-threading worker def mt(i_say, history): while True: try: if long_connection: mutable[0] = predict_no_ui_long_connection( inputs=i_say, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt) else: mutable[0] = predict_no_ui( inputs=i_say, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt) break except ConnectionAbortedError as token_exceeded_error: # 尝试计算比例,尽可能多地保留文本 p_ratio, n_exceed = get_reduce_token_percent( str(token_exceeded_error)) if len(history) > 0: history = [his[int(len(his) * p_ratio):] for his in history if his is not None] else: i_say = i_say[: int(len(i_say) * p_ratio)] mutable[1] = f'警告,文本过长将进行截断,Token溢出数:{n_exceed},截断比例:{(1-p_ratio):.0%}。' except TimeoutError as e: mutable[0] = '[Local Message] 请求超时。' raise TimeoutError except Exception as e: mutable[0] = f'[Local Message] 异常:{str(e)}.' raise RuntimeError(f'[Local Message] 异常:{str(e)}.') # 创建新线程发出http请求 thread_name = threading.Thread(target=mt, args=(i_say, history)) thread_name.start() # 原来的线程则负责持续更新UI,实现一个超时倒计时,并等待新线程的任务完成 cnt = 0 while thread_name.is_alive(): cnt += 1 chatbot[-1] = (i_say_show_user, f"[Local Message] {mutable[1]}waiting gpt response {cnt}/{TIMEOUT_SECONDS*2*(MAX_RETRY+1)}"+''.join(['.']*(cnt % 4))) yield chatbot, history, '正常' time.sleep(1) # 把gpt的输出从mutable中取出来 gpt_say = mutable[0] if gpt_say == '[Local Message] Failed with timeout.': raise TimeoutError return gpt_say def write_results_to_file(history, file_name=None): """ 将对话记录history以Markdown格式写入文件中。如果没有指定文件名,则使用当前时间生成文件名。 """ import os import time if file_name is None: # file_name = time.strftime("chatGPT分析报告%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md' file_name = 'chatGPT分析报告' + \ time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md' os.makedirs('./gpt_log/', exist_ok=True) with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f: f.write('# chatGPT 分析报告\n') for i, content in enumerate(history): try: # 这个bug没找到触发条件,暂时先这样顶一下 if type(content) != str: content = str(content) except: continue if i % 2 == 0: f.write('## ') f.write(content) f.write('\n\n') res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}') print(res) return res def regular_txt_to_markdown(text): """ 将普通文本转换为Markdown格式的文本。 """ text = text.replace('\n', '\n\n') text = text.replace('\n\n\n', '\n\n') text = text.replace('\n\n\n', '\n\n') return text def CatchException(f): """ 装饰器函数,捕捉函数f中的异常并封装到一个生成器中返回,并显示到聊天当中。 """ @wraps(f) def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT): try: yield from f(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT) except Exception as e: from check_proxy import check_proxy from toolbox import get_conf proxies, = get_conf('proxies') tb_str = '```\n' + traceback.format_exc() + '```' if chatbot is None or len(chatbot) == 0: chatbot = [["插件调度异常", "异常原因"]] chatbot[-1] = (chatbot[-1][0], f"[Local Message] 实验性函数调用出错: \n\n{tb_str} \n\n当前代理可用性: \n\n{check_proxy(proxies)}") yield chatbot, history, f'异常 {e}' return decorated def HotReload(f): """ 装饰器函数,实现函数插件热更新 """ @wraps(f) def decorated(*args, **kwargs): fn_name = f.__name__ f_hot_reload = getattr(importlib.reload(inspect.getmodule(f)), fn_name) yield from f_hot_reload(*args, **kwargs) return decorated def report_execption(chatbot, history, a, b): """ 向chatbot中添加错误信息 """ chatbot.append((a, b)) history.append(a) history.append(b) def text_divide_paragraph(text): """ 将文本按照段落分隔符分割开,生成带有段落标签的HTML代码。 """ if '```' in text: # careful input return text else: # wtf input lines = text.split("\n") for i, line in enumerate(lines): lines[i] = lines[i].replace(" ", " ") text = "".join(lines) return text def markdown_convertion(txt): """ 将Markdown格式的文本转换为HTML格式。如果包含数学公式,则先将公式转换为HTML格式。 """ pre = '