#
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import logging
import random
from collections import Counter
from common.token_utils import num_tokens_from_string
import re
import copy
import roman_numbers as r
from word2number import w2n
from cn2an import cn2an
from PIL import Image
import chardet
__all__ = ['rag_tokenizer']
all_codecs = [
'utf-8', 'gb2312', 'gbk', 'utf_16', 'ascii', 'big5', 'big5hkscs',
'cp037', 'cp273', 'cp424', 'cp437',
'cp500', 'cp720', 'cp737', 'cp775', 'cp850', 'cp852', 'cp855', 'cp856', 'cp857',
'cp858', 'cp860', 'cp861', 'cp862', 'cp863', 'cp864', 'cp865', 'cp866', 'cp869',
'cp874', 'cp875', 'cp932', 'cp949', 'cp950', 'cp1006', 'cp1026', 'cp1125',
'cp1140', 'cp1250', 'cp1251', 'cp1252', 'cp1253', 'cp1254', 'cp1255', 'cp1256',
'cp1257', 'cp1258', 'euc_jp', 'euc_jis_2004', 'euc_jisx0213', 'euc_kr',
'gb18030', 'hz', 'iso2022_jp', 'iso2022_jp_1', 'iso2022_jp_2',
'iso2022_jp_2004', 'iso2022_jp_3', 'iso2022_jp_ext', 'iso2022_kr', 'latin_1',
'iso8859_2', 'iso8859_3', 'iso8859_4', 'iso8859_5', 'iso8859_6', 'iso8859_7',
'iso8859_8', 'iso8859_9', 'iso8859_10', 'iso8859_11', 'iso8859_13',
'iso8859_14', 'iso8859_15', 'iso8859_16', 'johab', 'koi8_r', 'koi8_t', 'koi8_u',
'kz1048', 'mac_cyrillic', 'mac_greek', 'mac_iceland', 'mac_latin2', 'mac_roman',
'mac_turkish', 'ptcp154', 'shift_jis', 'shift_jis_2004', 'shift_jisx0213',
'utf_32', 'utf_32_be', 'utf_32_le', 'utf_16_be', 'utf_16_le', 'utf_7', 'windows-1250', 'windows-1251',
'windows-1252', 'windows-1253', 'windows-1254', 'windows-1255', 'windows-1256',
'windows-1257', 'windows-1258', 'latin-2'
]
def find_codec(blob):
detected = chardet.detect(blob[:1024])
if detected['confidence'] > 0.5:
if detected['encoding'] == "ascii":
return "utf-8"
for c in all_codecs:
try:
blob[:1024].decode(c)
return c
except Exception:
pass
try:
blob.decode(c)
return c
except Exception:
pass
return "utf-8"
QUESTION_PATTERN = [
r"第([零一二三四五六七八九十百0-9]+)问",
r"第([零一二三四五六七八九十百0-9]+)条",
r"[\(（]([零一二三四五六七八九十百]+)[\)）]",
r"第([0-9]+)问",
r"第([0-9]+)条",
r"([0-9]{1,2})[\. 、]",
r"([零一二三四五六七八九十百]+)[ 、]",
r"[\(（]([0-9]{1,2})[\)）]",
r"QUESTION (ONE|TWO|THREE|FOUR|FIVE|SIX|SEVEN|EIGHT|NINE|TEN)",
r"QUESTION (I+V?|VI*|XI|IX|X)",
r"QUESTION ([0-9]+)",
]
def has_qbullet(reg, box, last_box, last_index, last_bull, bull_x0_list):
section, last_section = box['text'], last_box['text']
q_reg = r'(\w|\W)*?(?:？|\?|\n|$)+'
full_reg = reg + q_reg
has_bull = re.match(full_reg, section)
index_str = None
if has_bull:
if 'x0' not in last_box:
last_box['x0'] = box['x0']
if 'top' not in last_box:
last_box['top'] = box['top']
if last_bull and box['x0'] - last_box['x0'] > 10:
return None, last_index
if not last_bull and box['x0'] >= last_box['x0'] and box['top'] - last_box['top'] < 20:
return None, last_index
avg_bull_x0 = 0
if bull_x0_list:
avg_bull_x0 = sum(bull_x0_list) / len(bull_x0_list)
else:
avg_bull_x0 = box['x0']
if box['x0'] - avg_bull_x0 > 10:
return None, last_index
index_str = has_bull.group(1)
index = index_int(index_str)
if last_section[-1] == ':' or last_section[-1] == '：':
return None, last_index
if not last_index or index >= last_index:
bull_x0_list.append(box['x0'])
return has_bull, index
if section[-1] == '?' or section[-1] == '？':
bull_x0_list.append(box['x0'])
return has_bull, index
if box['layout_type'] == 'title':
bull_x0_list.append(box['x0'])
return has_bull, index
pure_section = section.lstrip(re.match(reg, section).group()).lower()
ask_reg = r'(what|when|where|how|why|which|who|whose|为什么|为啥|哪)'
if re.match(ask_reg, pure_section):
bull_x0_list.append(box['x0'])
return has_bull, index
return None, last_index
def index_int(index_str):
res = -1
try:
res = int(index_str)
except ValueError:
try:
res = w2n.word_to_num(index_str)
except ValueError:
try:
res = cn2an(index_str)
except ValueError:
try:
res = r.number(index_str)
except ValueError:
return -1
return res
def qbullets_category(sections):
global QUESTION_PATTERN
hits = [0] * len(QUESTION_PATTERN)
for i, pro in enumerate(QUESTION_PATTERN):
for sec in sections:
if re.match(pro, sec) and not not_bullet(sec):
hits[i] += 1
break
maximum = 0
res = -1
for i, h in enumerate(hits):
if h <= maximum:
continue
res = i
maximum = h
return res, QUESTION_PATTERN[res]
BULLET_PATTERN = [[
r"第[零一二三四五六七八九十百0-9]+(分?编|部分)",
r"第[零一二三四五六七八九十百0-9]+章",
r"第[零一二三四五六七八九十百0-9]+节",
r"第[零一二三四五六七八九十百0-9]+条",
r"[\(（][零一二三四五六七八九十百]+[\)）]",
], [
r"第[0-9]+章",
r"第[0-9]+节",
r"[0-9]{,2}[\. 、]",
r"[0-9]{,2}\.[0-9]{,2}[^a-zA-Z/%~-]",
r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}",
r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}",
], [
r"第[零一二三四五六七八九十百0-9]+章",
r"第[零一二三四五六七八九十百0-9]+节",
r"[零一二三四五六七八九十百]+[ 、]",
r"[\(（][零一二三四五六七八九十百]+[\)）]",
r"[\(（][0-9]{,2}[\)）]",
], [
r"PART (ONE|TWO|THREE|FOUR|FIVE|SIX|SEVEN|EIGHT|NINE|TEN)",
r"Chapter (I+V?|VI*|XI|IX|X)",
r"Section [0-9]+",
r"Article [0-9]+"
], [
r"^#[^#]",
r"^##[^#]",
r"^###.*",
r"^####.*",
r"^#####.*",
r"^######.*",
]
]
def random_choices(arr, k):
k = min(len(arr), k)
return random.choices(arr, k=k)
def not_bullet(line):
patt = [
r"0", r"[0-9]+ +[0-9~个只-]", r"[0-9]+\.{2,}"
]
return any([re.match(r, line) for r in patt])
def bullets_category(sections):
global BULLET_PATTERN
hits = [0] * len(BULLET_PATTERN)
for i, pro in enumerate(BULLET_PATTERN):
for sec in sections:
sec = sec.strip()
for p in pro:
if re.match(p, sec) and not not_bullet(sec):
hits[i] += 1
break
maximum = 0
res = -1
for i, h in enumerate(hits):
if h <= maximum:
continue
res = i
maximum = h
return res
def is_english(texts):
if not texts:
return False
pattern = re.compile(r"[`a-zA-Z0-9\s.,':;/\"?<>!\(\)\-]")
if isinstance(texts, str):
texts = list(texts)
elif isinstance(texts, list):
texts = [t for t in texts if isinstance(t, str) and t.strip()]
else:
return False
if not texts:
return False
eng = sum(1 for t in texts if pattern.fullmatch(t.strip()))
return (eng / len(texts)) > 0.8
def is_chinese(text):
if not text:
return False
chinese = 0
for ch in text:
if '\u4e00' <= ch <= '\u9fff':
chinese += 1
if chinese / len(text) > 0.2:
return True
return False
def tokenize(d, txt, eng):
from . import rag_tokenizer
d["content_with_weight"] = txt
t = re.sub(r"]{0,12})?>", " ", txt)
d["content_ltks"] = rag_tokenizer.tokenize(t)
d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
def split_with_pattern(d, pattern:str, content:str, eng) -> list:
docs = []
txts = [txt for txt in re.split(r"(%s)" % pattern, content, flags=re.DOTALL)]
for j in range(0, len(txts), 2):
txt = txts[j]
if not txt:
continue
if j + 1 < len(txts):
txt += txts[j+1]
dd = copy.deepcopy(d)
tokenize(dd, txt, eng)
docs.append(dd)
return docs
def tokenize_chunks(chunks, doc, eng, pdf_parser=None, child_delimiters_pattern=None):
res = []
# wrap up as es documents
for ii, ck in enumerate(chunks):
if len(ck.strip()) == 0:
continue
logging.debug("-- {}".format(ck))
d = copy.deepcopy(doc)
if pdf_parser:
try:
d["image"], poss = pdf_parser.crop(ck, need_position=True)
add_positions(d, poss)
ck = pdf_parser.remove_tag(ck)
except NotImplementedError:
pass
else:
add_positions(d, [[ii]*5])
if child_delimiters_pattern:
d["mom_with_weight"] = ck
res.extend(split_with_pattern(d, child_delimiters_pattern, ck, eng))
continue
tokenize(d, ck, eng)
res.append(d)
return res
def tokenize_chunks_with_images(chunks, doc, eng, images, child_delimiters_pattern=None):
res = []
# wrap up as es documents
for ii, (ck, image) in enumerate(zip(chunks, images)):
if len(ck.strip()) == 0:
continue
logging.debug("-- {}".format(ck))
d = copy.deepcopy(doc)
d["image"] = image
add_positions(d, [[ii]*5])
if child_delimiters_pattern:
d["mom_with_weight"] = ck
res.extend(split_with_pattern(d, child_delimiters_pattern, ck, eng))
continue
tokenize(d, ck, eng)
res.append(d)
return res
def tokenize_table(tbls, doc, eng, batch_size=10):
res = []
# add tables
for (img, rows), poss in tbls:
if not rows:
continue
if isinstance(rows, str):
d = copy.deepcopy(doc)
tokenize(d, rows, eng)
d["content_with_weight"] = rows
d["doc_type_kwd"] = "table"
if img:
d["image"] = img
if d["content_with_weight"].find("") < 0:
d["doc_type_kwd"] = "image"
if poss:
add_positions(d, poss)
res.append(d)
continue
de = "; " if eng else "； "
for i in range(0, len(rows), batch_size):
d = copy.deepcopy(doc)
r = de.join(rows[i:i + batch_size])
tokenize(d, r, eng)
d["doc_type_kwd"] = "table"
if img:
d["image"] = img
if d["content_with_weight"].find("
") < 0:
d["doc_type_kwd"] = "image"
add_positions(d, poss)
res.append(d)
return res
def attach_media_context(chunks, table_context_size=0, image_context_size=0):
"""
Attach surrounding text chunk content to media chunks (table/image).
Best-effort ordering: if positional info exists on any chunk, use it to
order chunks before collecting context; otherwise keep original order.
"""
from . import rag_tokenizer
if not chunks or (table_context_size <= 0 and image_context_size <= 0):
return chunks
def is_image_chunk(ck):
if ck.get("doc_type_kwd") == "image":
return True
text_val = ck.get("content_with_weight") if isinstance(ck.get("content_with_weight"), str) else ck.get("text")
has_text = isinstance(text_val, str) and text_val.strip()
return bool(ck.get("image")) and not has_text
def is_table_chunk(ck):
return ck.get("doc_type_kwd") == "table"
def is_text_chunk(ck):
return not is_image_chunk(ck) and not is_table_chunk(ck)
def get_text(ck):
if isinstance(ck.get("content_with_weight"), str):
return ck["content_with_weight"]
if isinstance(ck.get("text"), str):
return ck["text"]
return ""
def split_sentences(text):
pattern = r"([.。！？!?；;：:\n])"
parts = re.split(pattern, text)
sentences = []
buf = ""
for p in parts:
if not p:
continue
if re.fullmatch(pattern, p):
buf += p
sentences.append(buf)
buf = ""
else:
buf += p
if buf:
sentences.append(buf)
return sentences
def trim_to_tokens(text, token_budget, from_tail=False):
if token_budget <= 0 or not text:
return ""
sentences = split_sentences(text)
if not sentences:
return ""
collected = []
remaining = token_budget
seq = reversed(sentences) if from_tail else sentences
for s in seq:
tks = num_tokens_from_string(s)
if tks <= 0:
continue
if tks > remaining:
collected.append(s)
break
collected.append(s)
remaining -= tks
if from_tail:
collected = list(reversed(collected))
return "".join(collected)
def extract_position(ck):
pn = None
top = None
left = None
try:
if ck.get("page_num_int"):
pn = ck["page_num_int"][0]
elif ck.get("page_number") is not None:
pn = ck.get("page_number")
if ck.get("top_int"):
top = ck["top_int"][0]
elif ck.get("top") is not None:
top = ck.get("top")
if ck.get("position_int"):
left = ck["position_int"][0][1]
elif ck.get("x0") is not None:
left = ck.get("x0")
except Exception:
pn = top = left = None
return pn, top, left
indexed = list(enumerate(chunks))
positioned_indices = []
unpositioned_indices = []
for idx, ck in indexed:
pn, top, left = extract_position(ck)
if pn is not None and top is not None:
positioned_indices.append((idx, pn, top, left if left is not None else 0))
else:
unpositioned_indices.append(idx)
if positioned_indices:
positioned_indices.sort(key=lambda x: (int(x[1]), int(x[2]), int(x[3]), x[0]))
ordered_indices = [i for i, _, _, _ in positioned_indices] + unpositioned_indices
else:
ordered_indices = [idx for idx, _ in indexed]
total = len(ordered_indices)
for sorted_pos, idx in enumerate(ordered_indices):
ck = chunks[idx]
token_budget = image_context_size if is_image_chunk(ck) else table_context_size if is_table_chunk(ck) else 0
if token_budget <= 0:
continue
prev_ctx = []
remaining_prev = token_budget
for prev_idx in range(sorted_pos - 1, -1, -1):
if remaining_prev <= 0:
break
neighbor_idx = ordered_indices[prev_idx]
if not is_text_chunk(chunks[neighbor_idx]):
break
txt = get_text(chunks[neighbor_idx])
if not txt:
continue
tks = num_tokens_from_string(txt)
if tks <= 0:
continue
if tks > remaining_prev:
txt = trim_to_tokens(txt, remaining_prev, from_tail=True)
tks = num_tokens_from_string(txt)
prev_ctx.append(txt)
remaining_prev -= tks
prev_ctx.reverse()
next_ctx = []
remaining_next = token_budget
for next_idx in range(sorted_pos + 1, total):
if remaining_next <= 0:
break
neighbor_idx = ordered_indices[next_idx]
if not is_text_chunk(chunks[neighbor_idx]):
break
txt = get_text(chunks[neighbor_idx])
if not txt:
continue
tks = num_tokens_from_string(txt)
if tks <= 0:
continue
if tks > remaining_next:
txt = trim_to_tokens(txt, remaining_next, from_tail=False)
tks = num_tokens_from_string(txt)
next_ctx.append(txt)
remaining_next -= tks
if not prev_ctx and not next_ctx:
continue
self_text = get_text(ck)
pieces = [*prev_ctx]
if self_text:
pieces.append(self_text)
pieces.extend(next_ctx)
combined = "\n".join(pieces)
original = ck.get("content_with_weight")
if "content_with_weight" in ck:
ck["content_with_weight"] = combined
elif "text" in ck:
original = ck.get("text")
ck["text"] = combined
if combined != original:
if "content_ltks" in ck:
ck["content_ltks"] = rag_tokenizer.tokenize(combined)
if "content_sm_ltks" in ck:
ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck.get("content_ltks", rag_tokenizer.tokenize(combined)))
if positioned_indices:
chunks[:] = [chunks[i] for i in ordered_indices]
return chunks
def add_positions(d, poss):
if not poss:
return
page_num_int = []
position_int = []
top_int = []
for pn, left, right, top, bottom in poss:
page_num_int.append(int(pn + 1))
top_int.append(int(top))
position_int.append((int(pn + 1), int(left), int(right), int(top), int(bottom)))
d["page_num_int"] = page_num_int
d["position_int"] = position_int
d["top_int"] = top_int
def remove_contents_table(sections, eng=False):
i = 0
while i < len(sections):
def get(i):
nonlocal sections
return (sections[i] if isinstance(sections[i],
type("")) else sections[i][0]).strip()
if not re.match(r"(contents|目录|目次|table of contents|致谢|acknowledge)$",
re.sub(r"( | |\u3000)+", "", get(i).split("@@")[0], flags=re.IGNORECASE)):
i += 1
continue
sections.pop(i)
if i >= len(sections):
break
prefix = get(i)[:3] if not eng else " ".join(get(i).split()[:2])
while not prefix:
sections.pop(i)
if i >= len(sections):
break
prefix = get(i)[:3] if not eng else " ".join(get(i).split()[:2])
sections.pop(i)
if i >= len(sections) or not prefix:
break
for j in range(i, min(i + 128, len(sections))):
if not re.match(prefix, get(j)):
continue
for _ in range(i, j):
sections.pop(i)
break
def make_colon_as_title(sections):
if not sections:
return []
if isinstance(sections[0], type("")):
return sections
i = 0
while i < len(sections):
txt, layout = sections[i]
i += 1
txt = txt.split("@")[0].strip()
if not txt:
continue
if txt[-1] not in ":：":
continue
txt = txt[::-1]
arr = re.split(r"([。？！!?;；]| \.)", txt)
if len(arr) < 2 or len(arr[1]) < 32:
continue
sections.insert(i - 1, (arr[0][::-1], "title"))
i += 1
def title_frequency(bull, sections):
bullets_size = len(BULLET_PATTERN[bull])
levels = [bullets_size + 1 for _ in range(len(sections))]
if not sections or bull < 0:
return bullets_size + 1, levels
for i, (txt, layout) in enumerate(sections):
for j, p in enumerate(BULLET_PATTERN[bull]):
if re.match(p, txt.strip()) and not not_bullet(txt):
levels[i] = j
break
else:
if re.search(r"(title|head)", layout) and not not_title(txt.split("@")[0]):
levels[i] = bullets_size
most_level = bullets_size + 1
for level, c in sorted(Counter(levels).items(), key=lambda x: x[1] * -1):
if level <= bullets_size:
most_level = level
break
return most_level, levels
def not_title(txt):
if re.match(r"第[零一二三四五六七八九十百0-9]+条", txt):
return False
if len(txt.split()) > 12 or (txt.find(" ") < 0 and len(txt) >= 32):
return True
return re.search(r"[,;，。；！!]", txt)
def tree_merge(bull, sections, depth):
if not sections or bull < 0:
return sections
if isinstance(sections[0], type("")):
sections = [(s, "") for s in sections]
# filter out position information in pdf sections
sections = [(t, o) for t, o in sections if
t and len(t.split("@")[0].strip()) > 1 and not re.match(r"[0-9]+$", t.split("@")[0].strip())]
def get_level(bull, section):
text, layout = section
text = re.sub(r"\u3000", " ", text).strip()
for i, title in enumerate(BULLET_PATTERN[bull]):
if re.match(title, text.strip()):
return i+1, text
else:
if re.search(r"(title|head)", layout) and not not_title(text):
return len(BULLET_PATTERN[bull])+1, text
else:
return len(BULLET_PATTERN[bull])+2, text
level_set = set()
lines = []
for section in sections:
level, text = get_level(bull, section)
if not text.strip("\n"):
continue
lines.append((level, text))
level_set.add(level)
sorted_levels = sorted(list(level_set))
if depth <= len(sorted_levels):
target_level = sorted_levels[depth - 1]
else:
target_level = sorted_levels[-1]
if target_level == len(BULLET_PATTERN[bull]) + 2:
target_level = sorted_levels[-2] if len(sorted_levels) > 1 else sorted_levels[0]
root = Node(level=0, depth=target_level, texts=[])
root.build_tree(lines)
return [element for element in root.get_tree() if element]
def hierarchical_merge(bull, sections, depth):
if not sections or bull < 0:
return []
if isinstance(sections[0], type("")):
sections = [(s, "") for s in sections]
sections = [(t, o) for t, o in sections if
t and len(t.split("@")[0].strip()) > 1 and not re.match(r"[0-9]+$", t.split("@")[0].strip())]
bullets_size = len(BULLET_PATTERN[bull])
levels = [[] for _ in range(bullets_size + 2)]
for i, (txt, layout) in enumerate(sections):
for j, p in enumerate(BULLET_PATTERN[bull]):
if re.match(p, txt.strip()):
levels[j].append(i)
break
else:
if re.search(r"(title|head)", layout) and not not_title(txt):
levels[bullets_size].append(i)
else:
levels[bullets_size + 1].append(i)
sections = [t for t, _ in sections]
# for s in sections: print("--", s)
def binary_search(arr, target):
if not arr:
return -1
if target > arr[-1]:
return len(arr) - 1
if target < arr[0]:
return -1
s, e = 0, len(arr)
while e - s > 1:
i = (e + s) // 2
if target > arr[i]:
s = i
continue
elif target < arr[i]:
e = i
continue
else:
assert False
return s
cks = []
readed = [False] * len(sections)
levels = levels[::-1]
for i, arr in enumerate(levels[:depth]):
for j in arr:
if readed[j]:
continue
readed[j] = True
cks.append([j])
if i + 1 == len(levels) - 1:
continue
for ii in range(i + 1, len(levels)):
jj = binary_search(levels[ii], j)
if jj < 0:
continue
if levels[ii][jj] > cks[-1][-1]:
cks[-1].pop(-1)
cks[-1].append(levels[ii][jj])
for ii in cks[-1]:
readed[ii] = True
if not cks:
return cks
for i in range(len(cks)):
cks[i] = [sections[j] for j in cks[i][::-1]]
logging.debug("\n* ".join(cks[i]))
res = [[]]
num = [0]
for ck in cks:
if len(ck) == 1:
n = num_tokens_from_string(re.sub(r"@@[0-9]+.*", "", ck[0]))
if n + num[-1] < 218:
res[-1].append(ck[0])
num[-1] += n
continue
res.append(ck)
num.append(n)
continue
res.append(ck)
num.append(218)
return res
def naive_merge(sections: str | list, chunk_token_num=128, delimiter="\n。；！？", overlapped_percent=0):
from deepdoc.parser.pdf_parser import RAGFlowPdfParser
if not sections:
return []
if isinstance(sections, str):
sections = [sections]
if isinstance(sections[0], str):
sections = [(s, "") for s in sections]
cks = [""]
tk_nums = [0]
def add_chunk(t, pos):
nonlocal cks, tk_nums, delimiter
tnum = num_tokens_from_string(t)
if not pos:
pos = ""
if tnum < 8:
pos = ""
# Ensure that the length of the merged chunk does not exceed chunk_token_num
if cks[-1] == "" or tk_nums[-1] > chunk_token_num * (100 - overlapped_percent)/100.:
if cks:
overlapped = RAGFlowPdfParser.remove_tag(cks[-1])
t = overlapped[int(len(overlapped)*(100-overlapped_percent)/100.):] + t
if t.find(pos) < 0:
t += pos
cks.append(t)
tk_nums.append(tnum)
else:
if cks[-1].find(pos) < 0:
t += pos
cks[-1] += t
tk_nums[-1] += tnum
custom_delimiters = [m.group(1) for m in re.finditer(r"`([^`]+)`", delimiter)]
has_custom = bool(custom_delimiters)
if has_custom:
custom_pattern = "|".join(re.escape(t) for t in sorted(set(custom_delimiters), key=len, reverse=True))
cks, tk_nums = [], []
for sec, pos in sections:
split_sec = re.split(r"(%s)" % custom_pattern, sec, flags=re.DOTALL)
for sub_sec in split_sec:
if re.fullmatch(custom_pattern, sub_sec or ""):
continue
text = "\n" + sub_sec
local_pos = pos
if num_tokens_from_string(text) < 8:
local_pos = ""
if local_pos and text.find(local_pos) < 0:
text += local_pos
cks.append(text)
tk_nums.append(num_tokens_from_string(text))
return cks
for sec, pos in sections:
add_chunk("\n"+sec, pos)
return cks
def naive_merge_with_images(texts, images, chunk_token_num=128, delimiter="\n。；！？", overlapped_percent=0):
from deepdoc.parser.pdf_parser import RAGFlowPdfParser
if not texts or len(texts) != len(images):
return [], []
cks = [""]
result_images = [None]
tk_nums = [0]
def add_chunk(t, image, pos=""):
nonlocal cks, result_images, tk_nums, delimiter
tnum = num_tokens_from_string(t)
if not pos:
pos = ""
if tnum < 8:
pos = ""
# Ensure that the length of the merged chunk does not exceed chunk_token_num
if cks[-1] == "" or tk_nums[-1] > chunk_token_num * (100 - overlapped_percent)/100.:
if cks:
overlapped = RAGFlowPdfParser.remove_tag(cks[-1])
t = overlapped[int(len(overlapped)*(100-overlapped_percent)/100.):] + t
if t.find(pos) < 0:
t += pos
cks.append(t)
result_images.append(image)
tk_nums.append(tnum)
else:
if cks[-1].find(pos) < 0:
t += pos
cks[-1] += t
if result_images[-1] is None:
result_images[-1] = image
else:
result_images[-1] = concat_img(result_images[-1], image)
tk_nums[-1] += tnum
custom_delimiters = [m.group(1) for m in re.finditer(r"`([^`]+)`", delimiter)]
has_custom = bool(custom_delimiters)
if has_custom:
custom_pattern = "|".join(re.escape(t) for t in sorted(set(custom_delimiters), key=len, reverse=True))
cks, result_images, tk_nums = [], [], []
for text, image in zip(texts, images):
text_str = text[0] if isinstance(text, tuple) else text
text_pos = text[1] if isinstance(text, tuple) and len(text) > 1 else ""
split_sec = re.split(r"(%s)" % custom_pattern, text_str)
for sub_sec in split_sec:
if re.fullmatch(custom_pattern, sub_sec or ""):
continue
text_seg = "\n" + sub_sec
local_pos = text_pos
if num_tokens_from_string(text_seg) < 8:
local_pos = ""
if local_pos and text_seg.find(local_pos) < 0:
text_seg += local_pos
cks.append(text_seg)
result_images.append(image)
tk_nums.append(num_tokens_from_string(text_seg))
return cks, result_images
for text, image in zip(texts, images):
# if text is tuple, unpack it
if isinstance(text, tuple):
text_str = text[0]
text_pos = text[1] if len(text) > 1 else ""
add_chunk("\n"+text_str, image, text_pos)
else:
add_chunk("\n"+text, image)
return cks, result_images
def docx_question_level(p, bull=-1):
txt = re.sub(r"\u3000", " ", p.text).strip()
if p.style.name.startswith('Heading'):
return int(p.style.name.split(' ')[-1]), txt
else:
if bull < 0:
return 0, txt
for j, title in enumerate(BULLET_PATTERN[bull]):
if re.match(title, txt):
return j + 1, txt
return len(BULLET_PATTERN[bull])+1, txt
def concat_img(img1, img2):
if img1 and not img2:
return img1
if not img1 and img2:
return img2
if not img1 and not img2:
return None
if img1 is img2:
return img1
if isinstance(img1, Image.Image) and isinstance(img2, Image.Image):
pixel_data1 = img1.tobytes()
pixel_data2 = img2.tobytes()
if pixel_data1 == pixel_data2:
return img1
width1, height1 = img1.size
width2, height2 = img2.size
new_width = max(width1, width2)
new_height = height1 + height2
new_image = Image.new('RGB', (new_width, new_height))
new_image.paste(img1, (0, 0))
new_image.paste(img2, (0, height1))
return new_image
def naive_merge_docx(sections, chunk_token_num=128, delimiter="\n。；！？"):
if not sections:
return [], []
cks = []
images = []
tk_nums = []
def add_chunk(t, image, pos=""):
nonlocal cks, images, tk_nums
tnum = num_tokens_from_string(t)
if tnum < 8:
pos = ""
if not cks or tk_nums[-1] > chunk_token_num:
# new chunk
if pos and t.find(pos) < 0:
t += pos
cks.append(t)
images.append(image)
tk_nums.append(tnum)
else:
# add to last chunk
if pos and cks[-1].find(pos) < 0:
t += pos
cks[-1] += t
images[-1] = concat_img(images[-1], image)
tk_nums[-1] += tnum
custom_delimiters = [m.group(1) for m in re.finditer(r"`([^`]+)`", delimiter)]
has_custom = bool(custom_delimiters)
if has_custom:
custom_pattern = "|".join(re.escape(t) for t in sorted(set(custom_delimiters), key=len, reverse=True))
cks, images, tk_nums = [], [], []
pattern = r"(%s)" % custom_pattern
for sec, image in sections:
split_sec = re.split(pattern, sec)
for sub_sec in split_sec:
if not sub_sec or re.fullmatch(custom_pattern, sub_sec):
continue
text_seg = "\n" + sub_sec
cks.append(text_seg)
images.append(image)
tk_nums.append(num_tokens_from_string(text_seg))
return cks, images
for sec, image in sections:
add_chunk("\n" + sec, image, "")
return cks, images
def extract_between(text: str, start_tag: str, end_tag: str) -> list[str]:
pattern = re.escape(start_tag) + r"(.*?)" + re.escape(end_tag)
return re.findall(pattern, text, flags=re.DOTALL)
def get_delimiters(delimiters: str):
dels = []
s = 0
for m in re.finditer(r"`([^`]+)`", delimiters, re.I):
f, t = m.span()
dels.append(m.group(1))
dels.extend(list(delimiters[s: f]))
s = t
if s < len(delimiters):
dels.extend(list(delimiters[s:]))
dels.sort(key=lambda x: -len(x))
dels = [re.escape(d) for d in dels if d]
dels = [d for d in dels if d]
dels_pattern = "|".join(dels)
return dels_pattern
class Node:
def __init__(self, level, depth=-1, texts=None):
self.level = level
self.depth = depth
self.texts = texts or []
self.children = []
def add_child(self, child_node):
self.children.append(child_node)
def get_children(self):
return self.children
def get_level(self):
return self.level
def get_texts(self):
return self.texts
def set_texts(self, texts):
self.texts = texts
def add_text(self, text):
self.texts.append(text)
def clear_text(self):
self.texts = []
def __repr__(self):
return f"Node(level={self.level}, texts={self.texts}, children={len(self.children)})"
def build_tree(self, lines):
stack = [self]
for level, text in lines:
if self.depth != -1 and level > self.depth:
# Beyond target depth: merge content into the current leaf instead of creating deeper nodes
stack[-1].add_text(text)
continue
# Move up until we find the proper parent whose level is strictly smaller than current
while len(stack) > 1 and level <= stack[-1].get_level():
stack.pop()
node = Node(level=level, texts=[text])
# Attach as child of current parent and descend
stack[-1].add_child(node)
stack.append(node)
return self
def get_tree(self):
tree_list = []
self._dfs(self, tree_list, [])
return tree_list
def _dfs(self, node, tree_list, titles):
level = node.get_level()
texts = node.get_texts()
child = node.get_children()
if level == 0 and texts:
tree_list.append("\n".join(titles+texts))
# Titles within configured depth are accumulated into the current path
if 1 <= level <= self.depth:
path_titles = titles + texts
else:
path_titles = titles
# Body outside the depth limit becomes its own chunk under the current title path
if level > self.depth and texts:
tree_list.append("\n".join(path_titles + texts))
# A leaf title within depth emits its title path as a chunk (header-only section)
elif not child and (1 <= level <= self.depth):
tree_list.append("\n".join(path_titles))
# Recurse into children with the updated title path
for c in child:
self._dfs(c, tree_list, path_titles)