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from crazy_functions.ipc_fns.mp import run_in_subprocess_with_timeout
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 maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage):
""" 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
当 remain_txt_to_cut < `_min` 时，我们再把 remain_txt_to_cut_storage 中的部分文字取出
"""
_min = int(5e4)
_max = int(1e5)
# print(len(remain_txt_to_cut), len(remain_txt_to_cut_storage))
if len(remain_txt_to_cut) < _min and len(remain_txt_to_cut_storage) > 0:
remain_txt_to_cut = remain_txt_to_cut + remain_txt_to_cut_storage
remain_txt_to_cut_storage = ""
if len(remain_txt_to_cut) > _max:
remain_txt_to_cut_storage = remain_txt_to_cut[_max:] + remain_txt_to_cut_storage
remain_txt_to_cut = remain_txt_to_cut[:_max]
return remain_txt_to_cut, remain_txt_to_cut_storage
def cut(limit, get_token_fn, txt_tocut, must_break_at_empty_line, break_anyway=False):
""" 文本切分
"""
res = []
total_len = len(txt_tocut)
fin_len = 0
remain_txt_to_cut = txt_tocut
remain_txt_to_cut_storage = ""
# 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
while True:
if get_token_fn(remain_txt_to_cut) <= limit:
# 如果剩余文本的token数小于限制，那么就不用切了
res.append(remain_txt_to_cut); fin_len+=len(remain_txt_to_cut)
break
else:
# 如果剩余文本的token数大于限制，那么就切
lines = remain_txt_to_cut.split('\n')
# 估计一个切分点
estimated_line_cut = limit / get_token_fn(remain_txt_to_cut) * len(lines)
estimated_line_cut = int(estimated_line_cut)
# 开始查找合适切分点的偏移（cnt）
cnt = 0
for cnt in reversed(range(estimated_line_cut)):
if must_break_at_empty_line:
# 首先尝试用双空行（\n\n）作为切分点
if lines[cnt] != "":
continue
prev = "\n".join(lines[:cnt])
post = "\n".join(lines[cnt:])
if get_token_fn(prev) < limit:
break
if cnt == 0:
# 如果没有找到合适的切分点
if break_anyway:
# 是否允许暴力切分
prev, post = force_breakdown(remain_txt_to_cut, limit, get_token_fn)
else:
# 不允许直接报错
raise RuntimeError(f"存在一行极长的文本！{remain_txt_to_cut}")
# 追加列表
res.append(prev); fin_len+=len(prev)
# 准备下一次迭代
remain_txt_to_cut = post
remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
process = fin_len/total_len
print(f'正在文本切分 {int(process*100)}%')
if len(remain_txt_to_cut.strip()) == 0:
break
return res
def breakdown_text_to_satisfy_token_limit_(txt, limit, llm_model="gpt-3.5-turbo"):
""" 使用多种方式尝试切分文本，以满足 token 限制
"""
from request_llms.bridge_all import model_info
enc = model_info[llm_model]['tokenizer']
def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
try:
# 第1次尝试，将双空行（\n\n）作为切分点
return cut(limit, get_token_fn, 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(limit, get_token_fn, 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(limit, get_token_fn, 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(limit, get_token_fn, 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，随便切一下吧
return cut(limit, get_token_fn, txt, must_break_at_empty_line=False, break_anyway=True)
breakdown_text_to_satisfy_token_limit = run_in_subprocess_with_timeout(breakdown_text_to_satisfy_token_limit_, timeout=60)
if __name__ == '__main__':
from crazy_functions.crazy_utils import read_and_clean_pdf_text
file_content, page_one = read_and_clean_pdf_text("build/assets/at.pdf")
from request_llms.bridge_all import model_info
for i in range(5):
file_content += file_content
print(len(file_content))
TOKEN_LIMIT_PER_FRAGMENT = 2500
res = breakdown_text_to_satisfy_token_limit(file_content, TOKEN_LIMIT_PER_FRAGMENT)
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