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Master 4.0 (#2210)

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* stage academic conversation

* stage document conversation

* fix buggy gradio version

* file dynamic load

* merge more academic plugins

* accelerate nltk

* feat: 为predict函数添加文件和URL读取功能
- 添加URL检测和网页内容提取功能，支持自动提取网页文本
- 添加文件路径识别和文件内容读取功能，支持private_upload路径格式
- 集成WebTextExtractor处理网页内容提取
- 集成TextContentLoader处理本地文件读取
- 支持文件路径与问题组合的智能处理

* back

* block unstable

---------

Co-authored-by: XiaoBoAI <liuboyin2019@ia.ac.cn>
This commit is contained in:
binary-husky
2025-08-23 15:59:22 +08:00
committed by GitHub
parent 65a4cf59c2
commit 8042750d41
79 changed files with 20850 additions and 57 deletions
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crazy_functions/pdf_fns/breakdown_pdf_txt.py Normal file
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from crazy_functions.ipc_fns.mp import run_in_subprocess_with_timeout
from loguru import logger
import time
import re
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
logger.info(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)
def cut_new(limit, get_token_fn, txt_tocut, must_break_at_empty_line, must_break_at_one_empty_line=False, break_anyway=False):
""" 文本切分
"""
res = []
res_empty_line = []
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)
empty=0
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)
res_empty_line.append(empty)
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
if must_break_at_empty_line or must_break_at_one_empty_line:
empty=1
prev = "\n".join(lines[:cnt])
post = "\n".join(lines[cnt:])
if get_token_fn(prev) < limit :
break
# empty=0
if get_token_fn(prev)>limit:
if '.' not in prev or '。' not in prev:
# empty = 0
break
# if cnt
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)
empty =0
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)
res_empty_line.append(empty)
# 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
logger.info(f'正在文本切分 {int(process*100)}%')
if len(remain_txt_to_cut.strip()) == 0:
break
return res,res_empty_line
def breakdown_text_to_satisfy_token_limit_new_(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
res, empty_line =cut_new(limit, get_token_fn, txt, must_break_at_empty_line=True)
return res,empty_line
except RuntimeError:
try:
# 第2次尝试，将单空行（\n）作为切分点
res, _ = cut_new(limit, get_token_fn, txt, must_break_at_empty_line=False,must_break_at_one_empty_line=True)
return res, _
except RuntimeError:
try:
# 第3次尝试，将英文句号（.）作为切分点
res, _ = cut_new(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_new(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
res, _ = cut_new(limit, get_token_fn, txt, must_break_at_empty_line=False, break_anyway=True)
return res,_
breakdown_text_to_satisfy_token_limit_new = run_in_subprocess_with_timeout(breakdown_text_to_satisfy_token_limit_new_, timeout=60)
def cut_from_end_to_satisfy_token_limit_(txt, limit, reserve_token=500, llm_model="gpt-3.5-turbo"):
"""从后往前裁剪文本，以论文为单位进行裁剪
参数：
txt: 要处理的文本（格式化后的论文列表字符串）
limit: token数量上限
reserve_token: 需要预留的token数量，默认500
llm_model: 使用的模型名称
返回：
裁剪后的文本
"""
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=()))
# 计算当前文本的token数
current_tokens = get_token_fn(txt)
target_limit = limit - reserve_token
# 如果当前token数已经在限制范围内，直接返回
if current_tokens <= target_limit:
return txt
# 按论文编号分割文本
papers = re.split(r'\n(?=\d+\. \*\*)', txt)
if not papers:
return txt
# 从前往后累加论文，直到达到token限制
result = papers[0] # 保留第一篇
current_tokens = get_token_fn(result)
for paper in papers[1:]:
paper_tokens = get_token_fn(paper)
if current_tokens + paper_tokens <= target_limit:
result += "\n" + paper
current_tokens += paper_tokens
else:
break
return result
# 添加超时保护
cut_from_end_to_satisfy_token_limit = run_in_subprocess_with_timeout(cut_from_end_to_satisfy_token_limit_, timeout=20)
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
logger.info(len(file_content))
TOKEN_LIMIT_PER_FRAGMENT = 2500
res = breakdown_text_to_satisfy_token_limit(file_content, TOKEN_LIMIT_PER_FRAGMENT)