import traceback from toolbox import update_ui, get_conf def input_clipping(inputs, history, max_token_limit): import tiktoken import numpy as np enc = tiktoken.encoding_for_model(*get_conf('LLM_MODEL')) def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) mode = 'input-and-history' # 当 输入部分的token占比 小于 全文的一半时，只裁剪历史 input_token_num = get_token_num(inputs) if input_token_num < max_token_limit//2: mode = 'only-history' max_token_limit = max_token_limit - input_token_num everything = [inputs] if mode == 'input-and-history' else [''] everything.extend(history) n_token = get_token_num('\n'.join(everything)) everything_token = [get_token_num(e) for e in everything] delta = max(everything_token) // 16 # 截断时的颗粒度 while n_token > max_token_limit: where = np.argmax(everything_token) encoded = enc.encode(everything[where], disallowed_special=()) clipped_encoded = encoded[:len(encoded)-delta] everything[where] = enc.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char everything_token[where] = get_token_num(everything[where]) n_token = get_token_num('\n'.join(everything)) if mode == 'input-and-history': inputs = everything[0] else: pass history = everything[1:] return inputs, history def request_gpt_model_in_new_thread_with_ui_alive( inputs, inputs_show_user, llm_kwargs, chatbot, history, sys_prompt, refresh_interval=0.2, handle_token_exceed=True, retry_times_at_unknown_error=2, ): """ Request GPT model，请求GPT模型同时维持用户界面活跃。 输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）: inputs (string): List of inputs （输入） inputs_show_user (string): List of inputs to show user（展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性） top_p (float): Top p value for sampling from model distribution （GPT参数，浮点数） temperature (float): Temperature value for sampling from model distribution（GPT参数，浮点数） chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化） history (list): List of chat history （历史，对话历史列表） sys_prompt (string): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样） refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果） handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启 retry_times_at_unknown_error：失败时的重试次数 输出 Returns: future: 输出，GPT返回的结果 """ import time from concurrent.futures import ThreadPoolExecutor from request_llm.bridge_all import predict_no_ui_long_connection # 用户反馈 chatbot.append([inputs_show_user, ""]) yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 executor = ThreadPoolExecutor(max_workers=16) mutable = ["", time.time()] def _req_gpt(inputs, history, sys_prompt): retry_op = retry_times_at_unknown_error exceeded_cnt = 0 while True: # watchdog error if len(mutable) >= 2 and (time.time()-mutable[1]) > 5: raise RuntimeError("检测到程序终止。") try: # 【第一种情况】：顺利完成 result = predict_no_ui_long_connection( inputs=inputs, llm_kwargs=llm_kwargs, history=history, sys_prompt=sys_prompt, observe_window=mutable) return result except ConnectionAbortedError as token_exceeded_error: # 【第二种情况】：Token溢出 if handle_token_exceed: exceeded_cnt += 1 # 【选择处理】 尝试计算比例，尽可能多地保留文本 from toolbox import get_reduce_token_percent p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error)) MAX_TOKEN = 4096 EXCEED_ALLO = 512 + 512 * exceeded_cnt inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO) mutable[0] += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n' continue # 返回重试 else: # 【选择放弃】 tb_str = '```\n' + traceback.format_exc() + '```' mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n" return mutable[0] # 放弃 except: # 【第三种情况】：其他错误：重试几次 tb_str = '```\n' + traceback.format_exc() + '```' print(tb_str) mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n" if retry_op > 0: retry_op -= 1 mutable[0] += f"[Local Message] 重试中，请稍等 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}：\n\n" if "Rate limit reached" in tb_str: time.sleep(30) time.sleep(5) continue # 返回重试 else: time.sleep(5) return mutable[0] # 放弃 # 提交任务 future = executor.submit(_req_gpt, inputs, history, sys_prompt) while True: # yield一次以刷新前端页面 time.sleep(refresh_interval) # “喂狗”（看门狗） mutable[1] = time.time() if future.done(): break chatbot[-1] = [chatbot[-1][0], mutable[0]] yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 final_result = future.result() chatbot[-1] = [chatbot[-1][0], final_result] yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息 return final_result def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( inputs_array, inputs_show_user_array, llm_kwargs, chatbot, history_array, sys_prompt_array, refresh_interval=0.2, max_workers=-1, scroller_max_len=30, handle_token_exceed=True, show_user_at_complete=False, retry_times_at_unknown_error=2, ): """ Request GPT model using multiple threads with UI and high efficiency 请求GPT模型的[多线程]版。 具备以下功能： 实时在UI上反馈远程数据流 使用线程池，可调节线程池的大小避免openai的流量限制错误 处理中途中止的情况 网络等出问题时，会把traceback和已经接收的数据转入输出 输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）: inputs_array (list): List of inputs （每个子任务的输入） inputs_show_user_array (list): List of inputs to show user（每个子任务展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性） llm_kwargs: llm_kwargs参数 chatbot: chatbot （用户界面对话窗口句柄，用于数据流可视化） history_array (list): List of chat history （历史对话输入，双层列表，第一层列表是子任务分解，第二层列表是对话历史） sys_prompt_array (list): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样） refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果） max_workers (int, optional): Maximum number of threads (default: see config.py) （最大线程数，如果子任务非常多，需要用此选项防止高频地请求openai导致错误） scroller_max_len (int, optional): Maximum length for scroller (default: 30)（数据流的显示最后收到的多少个字符，仅仅服务于视觉效果） handle_token_exceed (bool, optional): （是否在输入过长时，自动缩减文本） handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启 show_user_at_complete (bool, optional): (在结束时，把完整输入-输出结果显示在聊天框) retry_times_at_unknown_error：子任务失败时的重试次数 输出 Returns: list: List of GPT model responses （每个子任务的输出汇总，如果某个子任务出错，response中会携带traceback报错信息，方便调试和定位问题。） """ import time, random from concurrent.futures import ThreadPoolExecutor from request_llm.bridge_all import predict_no_ui_long_connection assert len(inputs_array) == len(history_array) assert len(inputs_array) == len(sys_prompt_array) if max_workers == -1: # 读取配置文件 try: max_workers, = get_conf('DEFAULT_WORKER_NUM') except: max_workers = 8 if max_workers <= 0 or max_workers >= 20: max_workers = 8 # 屏蔽掉 chatglm的多线程，可能会导致严重卡顿 if not llm_kwargs['llm_model'].startswith('gpt-'): max_workers = 1 executor = ThreadPoolExecutor(max_workers=max_workers) n_frag = len(inputs_array) # 用户反馈 chatbot.append(["请开始多线程操作。", ""]) yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 # 跨线程传递 mutable = [["", time.time(), "等待中"] for _ in range(n_frag)] # 子线程任务 def _req_gpt(index, inputs, history, sys_prompt): gpt_say = "" retry_op = retry_times_at_unknown_error exceeded_cnt = 0 mutable[index][2] = "执行中" while True: # watchdog error if len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > 5: raise RuntimeError("检测到程序终止。") try: # 【第一种情况】：顺利完成 # time.sleep(10); raise RuntimeError("测试") gpt_say = predict_no_ui_long_connection( inputs=inputs, llm_kwargs=llm_kwargs, history=history, sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True ) mutable[index][2] = "已成功" return gpt_say except ConnectionAbortedError as token_exceeded_error: # 【第二种情况】：Token溢出， if handle_token_exceed: exceeded_cnt += 1 # 【选择处理】 尝试计算比例，尽可能多地保留文本 from toolbox import get_reduce_token_percent p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error)) MAX_TOKEN = 4096 EXCEED_ALLO = 512 + 512 * exceeded_cnt inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO) gpt_say += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n' mutable[index][2] = f"截断重试" continue # 返回重试 else: # 【选择放弃】 tb_str = '```\n' + traceback.format_exc() + '```' gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n" if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0] mutable[index][2] = "输入过长已放弃" return gpt_say # 放弃 except: # 【第三种情况】：其他错误 tb_str = '```\n' + traceback.format_exc() + '```' print(tb_str) gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n" if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0] if retry_op > 0: retry_op -= 1 wait = random.randint(5, 20) if "Rate limit reached" in tb_str: wait = wait * 3 fail_info = "OpenAI请求速率限制 " else: fail_info = "" # 也许等待十几秒后，情况会好转 for i in range(wait): mutable[index][2] = f"{fail_info}等待重试 {wait-i}"; time.sleep(1) # 开始重试 mutable[index][2] = f"重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}" continue # 返回重试 else: mutable[index][2] = "已失败" wait = 5 time.sleep(5) return gpt_say # 放弃 # 异步任务开始 futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip( range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)] cnt = 0 while True: # yield一次以刷新前端页面 time.sleep(refresh_interval) cnt += 1 worker_done = [h.done() for h in futures] if all(worker_done): executor.shutdown() break # 更好的UI视觉效果 observe_win = [] # 每个线程都要“喂狗”（看门狗） for thread_index, _ in enumerate(worker_done): mutable[thread_index][1] = time.time() # 在前端打印些好玩的东西 for thread_index, _ in enumerate(worker_done): print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\ replace('\n', '').replace('```', '...').replace( ' ', '.').replace('
', '.....').replace('$', '.')+"`... ]" observe_win.append(print_something_really_funny) # 在前端打印些好玩的东西 stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n' if not done else f'`{mutable[thread_index][2]}`\n\n' for thread_index, done, obs in zip(range(len(worker_done)), worker_done, observe_win)]) # 在前端打印些好玩的东西 chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))] yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 # 异步任务结束 gpt_response_collection = [] for inputs_show_user, f in zip(inputs_show_user_array, futures): gpt_res = f.result() gpt_response_collection.extend([inputs_show_user, gpt_res]) # 是否在结束时，在界面上显示结果 if show_user_at_complete: for inputs_show_user, f in zip(inputs_show_user_array, futures): gpt_res = f.result() chatbot.append([inputs_show_user, gpt_res]) yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 time.sleep(0.3) return gpt_response_collection def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit): def cut(txt_tocut, must_break_at_empty_line): # 递归 if get_token_fn(txt_tocut) <= limit: return [txt_tocut] else: lines = txt_tocut.split('\n') estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines) estimated_line_cut = int(estimated_line_cut) for cnt in reversed(range(estimated_line_cut)): if must_break_at_empty_line: if lines[cnt] != "": continue print(cnt) prev = "\n".join(lines[:cnt]) post = "\n".join(lines[cnt:]) if get_token_fn(prev) < limit: break if cnt == 0: raise RuntimeError("存在一行极长的文本！") # print(len(post)) # 列表递归接龙 result = [prev] result.extend(cut(post, must_break_at_empty_line)) return result try: return cut(txt, must_break_at_empty_line=True) except RuntimeError: return cut(txt, must_break_at_empty_line=False) def force_breakdown(txt, limit, get_token_fn): """ 当无法用标点、空行分割时，我们用最暴力的方法切割 """ for i in reversed(range(len(txt))): if get_token_fn(txt[:i]) < limit: return txt[:i], txt[i:] return "Tiktoken未知错误", "Tiktoken未知错误" def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit): # 递归 def cut(txt_tocut, must_break_at_empty_line, break_anyway=False): if get_token_fn(txt_tocut) <= limit: return [txt_tocut] else: lines = txt_tocut.split('\n') estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines) estimated_line_cut = int(estimated_line_cut) cnt = 0 for cnt in reversed(range(estimated_line_cut)): if must_break_at_empty_line: if lines[cnt] != "": continue prev = "\n".join(lines[:cnt]) post = "\n".join(lines[cnt:]) if get_token_fn(prev) < limit: break if cnt == 0: if break_anyway: prev, post = force_breakdown(txt_tocut, limit, get_token_fn) else: raise RuntimeError(f"存在一行极长的文本！{txt_tocut}") # print(len(post)) # 列表递归接龙 result = [prev] result.extend(cut(post, must_break_at_empty_line, break_anyway=break_anyway)) return result try: # 第1次尝试，将双空行（\n\n）作为切分点 return cut(txt, must_break_at_empty_line=True) except RuntimeError: try: # 第2次尝试，将单空行（\n）作为切分点 return cut(txt, must_break_at_empty_line=False) except RuntimeError: try: # 第3次尝试，将英文句号（.）作为切分点 res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在 return [r.replace('。\n', '.') for r in res] except RuntimeError as e: try: # 第4次尝试，将中文句号（。）作为切分点 res = cut(txt.replace('。', '。。\n'), must_break_at_empty_line=False) return [r.replace('。。\n', '。') for r in res] except RuntimeError as e: # 第5次尝试，没办法了，随便切一下敷衍吧 return cut(txt, must_break_at_empty_line=False, break_anyway=True) def read_and_clean_pdf_text(fp): """ 这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好 **输入参数说明** - `fp`：需要读取和清理文本的pdf文件路径 **输出参数说明** - `meta_txt`：清理后的文本内容字符串 - `page_one_meta`：第一页清理后的文本内容列表 **函数功能** 读取pdf文件并清理其中的文本内容，清理规则包括： - 提取所有块元的文本信息，并合并为一个字符串 - 去除短块（字符数小于100）并替换为回车符 - 清理多余的空行 - 合并小写字母开头的段落块并替换为空格 - 清除重复的换行 - 将每个换行符替换为两个换行符，使每个段落之间有两个换行符分隔 """ import fitz, copy import re import numpy as np from colorful import print亮黄, print亮绿 fc = 0 # Index 0 文本 fs = 1 # Index 1 字体 fb = 2 # Index 2 框框 REMOVE_FOOT_NOTE = True # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等） REMOVE_FOOT_FFSIZE_PERCENT = 0.95 # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化） def primary_ffsize(l): """ 提取文本块主字体 """ fsize_statiscs = {} for wtf in l['spans']: if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0 fsize_statiscs[wtf['size']] += len(wtf['text']) return max(fsize_statiscs, key=fsize_statiscs.get) def ffsize_same(a,b): """ 提取字体大小是否近似相等 """ return abs((a-b)/max(a,b)) < 0.02 with fitz.open(fp) as doc: meta_txt = [] meta_font = [] meta_line = [] meta_span = [] ############################## <第 1 步，搜集初始信息> ################################## for index, page in enumerate(doc): # file_content += page.get_text() text_areas = page.get_text("dict") # 获取页面上的文本信息 for t in text_areas['blocks']: if 'lines' in t: pf = 998 for l in t['lines']: txt_line = "".join([wtf['text'] for wtf in l['spans']]) pf = primary_ffsize(l) meta_line.append([txt_line, pf, l['bbox'], l]) for wtf in l['spans']: # for l in t['lines']: meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])]) # meta_line.append(["NEW_BLOCK", pf]) # 块元提取 for each word segment with in line for each line cross-line words for each block meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace( '- ', '') for t in text_areas['blocks'] if 'lines' in t]) meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']]) for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t]) if index == 0: page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace( '- ', '') for t in text_areas['blocks'] if 'lines' in t] ############################## <第 2 步，获取正文主字体> ################################## fsize_statiscs = {} for span in meta_span: if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0 fsize_statiscs[span[1]] += span[2] main_fsize = max(fsize_statiscs, key=fsize_statiscs.get) if REMOVE_FOOT_NOTE: give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT ############################## <第 3 步，切分和重新整合> ################################## mega_sec = [] sec = [] for index, line in enumerate(meta_line): if index == 0: sec.append(line[fc]) continue if REMOVE_FOOT_NOTE: if meta_line[index][fs] <= give_up_fize_threshold: continue if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]): # 尝试识别段落 if meta_line[index][fc].endswith('.') and\ (meta_line[index-1][fc] != 'NEW_BLOCK') and \ (meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7: sec[-1] += line[fc] sec[-1] += "\n\n" else: sec[-1] += " " sec[-1] += line[fc] else: if (index+1 < len(meta_line)) and \ meta_line[index][fs] > main_fsize: # 单行 + 字体大 mega_sec.append(copy.deepcopy(sec)) sec = [] sec.append("# " + line[fc]) else: # 尝试识别section if meta_line[index-1][fs] > meta_line[index][fs]: sec.append("\n" + line[fc]) else: sec.append(line[fc]) mega_sec.append(copy.deepcopy(sec)) finals = [] for ms in mega_sec: final = " ".join(ms) final = final.replace('- ', ' ') finals.append(final) meta_txt = finals ############################## <第 4 步，乱七八糟的后处理> ################################## def 把字符太少的块清除为回车(meta_txt): for index, block_txt in enumerate(meta_txt): if len(block_txt) < 100: meta_txt[index] = '\n' return meta_txt meta_txt = 把字符太少的块清除为回车(meta_txt) def 清理多余的空行(meta_txt): for index in reversed(range(1, len(meta_txt))): if meta_txt[index] == '\n' and meta_txt[index-1] == '\n': meta_txt.pop(index) return meta_txt meta_txt = 清理多余的空行(meta_txt) def 合并小写开头的段落块(meta_txt): def starts_with_lowercase_word(s): pattern = r"^[a-z]+" match = re.match(pattern, s) if match: return True else: return False for _ in range(100): for index, block_txt in enumerate(meta_txt): if starts_with_lowercase_word(block_txt): if meta_txt[index-1] != '\n': meta_txt[index-1] += ' ' else: meta_txt[index-1] = '' meta_txt[index-1] += meta_txt[index] meta_txt[index] = '\n' return meta_txt meta_txt = 合并小写开头的段落块(meta_txt) meta_txt = 清理多余的空行(meta_txt) meta_txt = '\n'.join(meta_txt) # 清除重复的换行 for _ in range(5): meta_txt = meta_txt.replace('\n\n', '\n') # 换行 -> 双换行 meta_txt = meta_txt.replace('\n', '\n\n') ############################## <第 5 步，展示分割效果> ################################## for f in finals: print亮黄(f) print亮绿('***************************') return meta_txt, page_one_meta