ragflow / rag /app /naive.py
Kevin Hu
Light GraphRAG (#4585)
47ec63e
#
# Copyright 2025 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
from tika import parser
from io import BytesIO
from docx import Document
from timeit import default_timer as timer
import re
from deepdoc.parser.pdf_parser import PlainParser
from rag.nlp import rag_tokenizer, naive_merge, tokenize_table, tokenize_chunks, find_codec, concat_img, \
naive_merge_docx, tokenize_chunks_docx
from deepdoc.parser import PdfParser, ExcelParser, DocxParser, HtmlParser, JsonParser, MarkdownParser, TxtParser
from rag.utils import num_tokens_from_string
from PIL import Image
from functools import reduce
from markdown import markdown
from docx.image.exceptions import UnrecognizedImageError, UnexpectedEndOfFileError, InvalidImageStreamError
class Docx(DocxParser):
def __init__(self):
pass
def get_picture(self, document, paragraph):
img = paragraph._element.xpath('.//pic:pic')
if not img:
return None
img = img[0]
embed = img.xpath('.//a:blip/@r:embed')[0]
related_part = document.part.related_parts[embed]
try:
image_blob = related_part.image.blob
except UnrecognizedImageError:
logging.info("Unrecognized image format. Skipping image.")
return None
except UnexpectedEndOfFileError:
logging.info("EOF was unexpectedly encountered while reading an image stream. Skipping image.")
return None
except InvalidImageStreamError:
logging.info("The recognized image stream appears to be corrupted. Skipping image.")
return None
try:
image = Image.open(BytesIO(image_blob)).convert('RGB')
return image
except Exception:
return None
def __clean(self, line):
line = re.sub(r"\u3000", " ", line).strip()
return line
def __call__(self, filename, binary=None, from_page=0, to_page=100000):
self.doc = Document(
filename) if not binary else Document(BytesIO(binary))
pn = 0
lines = []
last_image = None
for p in self.doc.paragraphs:
if pn > to_page:
break
if from_page <= pn < to_page:
if p.text.strip():
if p.style and p.style.name == 'Caption':
former_image = None
if lines and lines[-1][1] and lines[-1][2] != 'Caption':
former_image = lines[-1][1].pop()
elif last_image:
former_image = last_image
last_image = None
lines.append((self.__clean(p.text), [former_image], p.style.name))
else:
current_image = self.get_picture(self.doc, p)
image_list = [current_image]
if last_image:
image_list.insert(0, last_image)
last_image = None
lines.append((self.__clean(p.text), image_list, p.style.name if p.style else ""))
else:
if current_image := self.get_picture(self.doc, p):
if lines:
lines[-1][1].append(current_image)
else:
last_image = current_image
for run in p.runs:
if 'lastRenderedPageBreak' in run._element.xml:
pn += 1
continue
if 'w:br' in run._element.xml and 'type="page"' in run._element.xml:
pn += 1
new_line = [(line[0], reduce(concat_img, line[1]) if line[1] else None) for line in lines]
tbls = []
for tb in self.doc.tables:
html = "<table>"
for r in tb.rows:
html += "<tr>"
i = 0
while i < len(r.cells):
span = 1
c = r.cells[i]
for j in range(i + 1, len(r.cells)):
if c.text == r.cells[j].text:
span += 1
i = j
i += 1
html += f"<td>{c.text}</td>" if span == 1 else f"<td colspan='{span}'>{c.text}</td>"
html += "</tr>"
html += "</table>"
tbls.append(((None, html), ""))
return new_line, tbls
class Pdf(PdfParser):
def __call__(self, filename, binary=None, from_page=0,
to_page=100000, zoomin=3, callback=None):
start = timer()
first_start = start
callback(msg="OCR started")
self.__images__(
filename if not binary else binary,
zoomin,
from_page,
to_page,
callback
)
callback(msg="OCR finished ({:.2f}s)".format(timer() - start))
logging.info("OCR({}~{}): {:.2f}s".format(from_page, to_page, timer() - start))
start = timer()
self._layouts_rec(zoomin)
callback(0.63, "Layout analysis ({:.2f}s)".format(timer() - start))
start = timer()
self._table_transformer_job(zoomin)
callback(0.65, "Table analysis ({:.2f}s)".format(timer() - start))
start = timer()
self._text_merge()
callback(0.67, "Text merged ({:.2f}s)".format(timer() - start))
tbls = self._extract_table_figure(True, zoomin, True, True)
# self._naive_vertical_merge()
self._concat_downward()
# self._filter_forpages()
logging.info("layouts cost: {}s".format(timer() - first_start))
return [(b["text"], self._line_tag(b, zoomin))
for b in self.boxes], tbls
class Markdown(MarkdownParser):
def __call__(self, filename, binary=None):
if binary:
encoding = find_codec(binary)
txt = binary.decode(encoding, errors="ignore")
else:
with open(filename, "r") as f:
txt = f.read()
remainder, tables = self.extract_tables_and_remainder(f'{txt}\n')
sections = []
tbls = []
for sec in remainder.split("\n"):
if num_tokens_from_string(sec) > 3 * self.chunk_token_num:
sections.append((sec[:int(len(sec) / 2)], ""))
sections.append((sec[int(len(sec) / 2):], ""))
else:
if sec.strip().find("#") == 0:
sections.append((sec, ""))
elif sections and sections[-1][0].strip().find("#") == 0:
sec_, _ = sections.pop(-1)
sections.append((sec_ + "\n" + sec, ""))
else:
sections.append((sec, ""))
for table in tables:
tbls.append(((None, markdown(table, extensions=['markdown.extensions.tables'])), ""))
return sections, tbls
def chunk(filename, binary=None, from_page=0, to_page=100000,
lang="Chinese", callback=None, **kwargs):
"""
Supported file formats are docx, pdf, excel, txt.
This method apply the naive ways to chunk files.
Successive text will be sliced into pieces using 'delimiter'.
Next, these successive pieces are merge into chunks whose token number is no more than 'Max token number'.
"""
is_english = lang.lower() == "english" # is_english(cks)
parser_config = kwargs.get(
"parser_config", {
"chunk_token_num": 128, "delimiter": "\n!?。;!?", "layout_recognize": "DeepDOC"})
doc = {
"docnm_kwd": filename,
"title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename))
}
doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
res = []
pdf_parser = None
if re.search(r"\.docx$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
sections, tables = Docx()(filename, binary)
res = tokenize_table(tables, doc, is_english) # just for table
callback(0.8, "Finish parsing.")
st = timer()
chunks, images = naive_merge_docx(
sections, int(parser_config.get(
"chunk_token_num", 128)), parser_config.get(
"delimiter", "\n!?。;!?"))
if kwargs.get("section_only", False):
return chunks
res.extend(tokenize_chunks_docx(chunks, doc, is_english, images))
logging.info("naive_merge({}): {}".format(filename, timer() - st))
return res
elif re.search(r"\.pdf$", filename, re.IGNORECASE):
pdf_parser = Pdf()
if parser_config.get("layout_recognize", "DeepDOC") == "Plain Text":
pdf_parser = PlainParser()
sections, tables = pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page,
callback=callback)
res = tokenize_table(tables, doc, is_english)
elif re.search(r"\.xlsx?$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
excel_parser = ExcelParser()
if parser_config.get("html4excel"):
sections = [(_, "") for _ in excel_parser.html(binary, 12) if _]
else:
sections = [(_, "") for _ in excel_parser(binary) if _]
elif re.search(r"\.(txt|py|js|java|c|cpp|h|php|go|ts|sh|cs|kt|sql)$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
sections = TxtParser()(filename, binary,
parser_config.get("chunk_token_num", 128),
parser_config.get("delimiter", "\n!?;。;!?"))
callback(0.8, "Finish parsing.")
elif re.search(r"\.(md|markdown)$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
sections, tables = Markdown(int(parser_config.get("chunk_token_num", 128)))(filename, binary)
res = tokenize_table(tables, doc, is_english)
callback(0.8, "Finish parsing.")
elif re.search(r"\.(htm|html)$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
sections = HtmlParser()(filename, binary)
sections = [(_, "") for _ in sections if _]
callback(0.8, "Finish parsing.")
elif re.search(r"\.json$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
chunk_token_num = int(parser_config.get("chunk_token_num", 128))
sections = JsonParser(chunk_token_num)(binary)
sections = [(_, "") for _ in sections if _]
callback(0.8, "Finish parsing.")
elif re.search(r"\.doc$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
binary = BytesIO(binary)
doc_parsed = parser.from_buffer(binary)
if doc_parsed.get('content', None) is not None:
sections = doc_parsed['content'].split('\n')
sections = [(_, "") for _ in sections if _]
callback(0.8, "Finish parsing.")
else:
callback(0.8, f"tika.parser got empty content from {filename}.")
logging.warning(f"tika.parser got empty content from {filename}.")
return []
else:
raise NotImplementedError(
"file type not supported yet(pdf, xlsx, doc, docx, txt supported)")
st = timer()
chunks = naive_merge(
sections, int(parser_config.get(
"chunk_token_num", 128)), parser_config.get(
"delimiter", "\n!?。;!?"))
if kwargs.get("section_only", False):
return chunks
res.extend(tokenize_chunks(chunks, doc, is_english, pdf_parser))
logging.info("naive_merge({}): {}".format(filename, timer() - st))
return res
if __name__ == "__main__":
import sys
def dummy(prog=None, msg=""):
pass
chunk(sys.argv[1], from_page=0, to_page=10, callback=dummy)