gpt-academic / crazy_functions /crazy_utils.py
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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_chatgpt 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() + '```'
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 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}:\n\n"
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_chatgpt 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
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('<br/>', '.....').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:
print('what the fuck ?')
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 breakdown_txt_to_satisfy_token_limit_for_pdf(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)
cnt = 0
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:
# print('what the fuck ? 存在一行极长的文本!')
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:
try:
return cut(txt, must_break_at_empty_line=False)
except RuntimeError:
# 这个中文的句号是故意的,作为一个标识而存在
res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False)
return [r.replace('。\n', '.') for r in res]
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