mirror of
https://github.com/infiniflow/ragflow.git
synced 2025-12-08 20:42:30 +08:00
Format file format from Windows/dos to Unix (#1949)
### What problem does this PR solve? Related source file is in Windows/DOS format, they are format to Unix format. ### Type of change - [x] Refactoring Signed-off-by: Jin Hai <haijin.chn@gmail.com>
This commit is contained in:
Jin Hai
318
rag/app/book.py
318
rag/app/book.py
@ -1,159 +1,159 @@
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import copy
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from tika import parser
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import re
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from io import BytesIO
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from rag.nlp import bullets_category, is_english, tokenize, remove_contents_table, \
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hierarchical_merge, make_colon_as_title, naive_merge, random_choices, tokenize_table, add_positions, \
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tokenize_chunks, find_codec
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from rag.nlp import rag_tokenizer
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from deepdoc.parser import PdfParser, DocxParser, PlainParser, HtmlParser
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class Pdf(PdfParser):
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def __call__(self, filename, binary=None, from_page=0,
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to_page=100000, zoomin=3, callback=None):
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callback(msg="OCR is running...")
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self.__images__(
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filename if not binary else binary,
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zoomin,
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from_page,
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to_page,
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callback)
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callback(msg="OCR finished")
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from timeit import default_timer as timer
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start = timer()
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self._layouts_rec(zoomin)
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callback(0.67, "Layout analysis finished")
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print("layouts:", timer() - start)
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self._table_transformer_job(zoomin)
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callback(0.68, "Table analysis finished")
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self._text_merge()
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tbls = self._extract_table_figure(True, zoomin, True, True)
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self._naive_vertical_merge()
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self._filter_forpages()
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self._merge_with_same_bullet()
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callback(0.75, "Text merging finished.")
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callback(0.8, "Text extraction finished")
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return [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno", ""))
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for b in self.boxes], tbls
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def chunk(filename, binary=None, from_page=0, to_page=100000,
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lang="Chinese", callback=None, **kwargs):
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"""
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Supported file formats are docx, pdf, txt.
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Since a book is long and not all the parts are useful, if it's a PDF,
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please setup the page ranges for every book in order eliminate negative effects and save elapsed computing time.
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"""
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doc = {
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"docnm_kwd": filename,
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"title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename))
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}
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doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
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pdf_parser = None
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sections, tbls = [], []
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if re.search(r"\.docx$", filename, re.IGNORECASE):
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callback(0.1, "Start to parse.")
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doc_parser = DocxParser()
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# TODO: table of contents need to be removed
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sections, tbls = doc_parser(
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binary if binary else filename, from_page=from_page, to_page=to_page)
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remove_contents_table(sections, eng=is_english(
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random_choices([t for t, _ in sections], k=200)))
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tbls = [((None, lns), None) for lns in tbls]
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callback(0.8, "Finish parsing.")
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elif re.search(r"\.pdf$", filename, re.IGNORECASE):
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pdf_parser = Pdf() if kwargs.get(
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"parser_config", {}).get(
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"layout_recognize", True) else PlainParser()
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sections, tbls = pdf_parser(filename if not binary else binary,
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from_page=from_page, to_page=to_page, callback=callback)
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elif re.search(r"\.txt$", filename, re.IGNORECASE):
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callback(0.1, "Start to parse.")
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txt = ""
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if binary:
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encoding = find_codec(binary)
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txt = binary.decode(encoding, errors="ignore")
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else:
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with open(filename, "r") as f:
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while True:
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l = f.readline()
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if not l:
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break
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txt += l
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sections = txt.split("\n")
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sections = [(l, "") for l in sections if l]
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remove_contents_table(sections, eng=is_english(
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random_choices([t for t, _ in sections], k=200)))
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callback(0.8, "Finish parsing.")
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elif re.search(r"\.(htm|html)$", filename, re.IGNORECASE):
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callback(0.1, "Start to parse.")
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sections = HtmlParser()(filename, binary)
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sections = [(l, "") for l in sections if l]
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remove_contents_table(sections, eng=is_english(
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random_choices([t for t, _ in sections], k=200)))
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callback(0.8, "Finish parsing.")
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elif re.search(r"\.doc$", filename, re.IGNORECASE):
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callback(0.1, "Start to parse.")
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binary = BytesIO(binary)
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doc_parsed = parser.from_buffer(binary)
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sections = doc_parsed['content'].split('\n')
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sections = [(l, "") for l in sections if l]
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remove_contents_table(sections, eng=is_english(
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random_choices([t for t, _ in sections], k=200)))
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callback(0.8, "Finish parsing.")
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else:
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raise NotImplementedError(
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"file type not supported yet(doc, docx, pdf, txt supported)")
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make_colon_as_title(sections)
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bull = bullets_category(
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[t for t in random_choices([t for t, _ in sections], k=100)])
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if bull >= 0:
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chunks = ["\n".join(ck)
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for ck in hierarchical_merge(bull, sections, 5)]
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else:
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sections = [s.split("@") for s, _ in sections]
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sections = [(pr[0], "@" + pr[1]) if len(pr) == 2 else (pr[0], '') for pr in sections ]
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chunks = naive_merge(
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sections, kwargs.get(
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"chunk_token_num", 256), kwargs.get(
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"delimer", "\n。；！？"))
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# is it English
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# is_english(random_choices([t for t, _ in sections], k=218))
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eng = lang.lower() == "english"
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res = tokenize_table(tbls, doc, eng)
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res.extend(tokenize_chunks(chunks, doc, eng, pdf_parser))
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return res
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if __name__ == "__main__":
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import sys
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def dummy(prog=None, msg=""):
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pass
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chunk(sys.argv[1], from_page=1, to_page=10, callback=dummy)
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import copy
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from tika import parser
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import re
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from io import BytesIO
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from rag.nlp import bullets_category, is_english, tokenize, remove_contents_table, \
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hierarchical_merge, make_colon_as_title, naive_merge, random_choices, tokenize_table, add_positions, \
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tokenize_chunks, find_codec
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from rag.nlp import rag_tokenizer
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from deepdoc.parser import PdfParser, DocxParser, PlainParser, HtmlParser
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class Pdf(PdfParser):
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def __call__(self, filename, binary=None, from_page=0,
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to_page=100000, zoomin=3, callback=None):
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callback(msg="OCR is running...")
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self.__images__(
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filename if not binary else binary,
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zoomin,
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from_page,
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to_page,
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callback)
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callback(msg="OCR finished")
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from timeit import default_timer as timer
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start = timer()
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self._layouts_rec(zoomin)
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callback(0.67, "Layout analysis finished")
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print("layouts:", timer() - start)
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self._table_transformer_job(zoomin)
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callback(0.68, "Table analysis finished")
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self._text_merge()
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tbls = self._extract_table_figure(True, zoomin, True, True)
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self._naive_vertical_merge()
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self._filter_forpages()
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self._merge_with_same_bullet()
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callback(0.75, "Text merging finished.")
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callback(0.8, "Text extraction finished")
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return [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno", ""))
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for b in self.boxes], tbls
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def chunk(filename, binary=None, from_page=0, to_page=100000,
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lang="Chinese", callback=None, **kwargs):
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"""
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Supported file formats are docx, pdf, txt.
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Since a book is long and not all the parts are useful, if it's a PDF,
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please setup the page ranges for every book in order eliminate negative effects and save elapsed computing time.
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"""
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doc = {
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"docnm_kwd": filename,
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"title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename))
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}
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doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
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pdf_parser = None
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sections, tbls = [], []
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if re.search(r"\.docx$", filename, re.IGNORECASE):
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callback(0.1, "Start to parse.")
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doc_parser = DocxParser()
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# TODO: table of contents need to be removed
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sections, tbls = doc_parser(
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binary if binary else filename, from_page=from_page, to_page=to_page)
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remove_contents_table(sections, eng=is_english(
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random_choices([t for t, _ in sections], k=200)))
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tbls = [((None, lns), None) for lns in tbls]
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callback(0.8, "Finish parsing.")
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elif re.search(r"\.pdf$", filename, re.IGNORECASE):
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pdf_parser = Pdf() if kwargs.get(
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"parser_config", {}).get(
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"layout_recognize", True) else PlainParser()
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sections, tbls = pdf_parser(filename if not binary else binary,
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from_page=from_page, to_page=to_page, callback=callback)
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elif re.search(r"\.txt$", filename, re.IGNORECASE):
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callback(0.1, "Start to parse.")
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txt = ""
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if binary:
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encoding = find_codec(binary)
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txt = binary.decode(encoding, errors="ignore")
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else:
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with open(filename, "r") as f:
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while True:
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l = f.readline()
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if not l:
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break
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txt += l
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sections = txt.split("\n")
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sections = [(l, "") for l in sections if l]
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remove_contents_table(sections, eng=is_english(
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random_choices([t for t, _ in sections], k=200)))
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callback(0.8, "Finish parsing.")
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elif re.search(r"\.(htm|html)$", filename, re.IGNORECASE):
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callback(0.1, "Start to parse.")
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sections = HtmlParser()(filename, binary)
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sections = [(l, "") for l in sections if l]
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remove_contents_table(sections, eng=is_english(
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random_choices([t for t, _ in sections], k=200)))
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callback(0.8, "Finish parsing.")
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elif re.search(r"\.doc$", filename, re.IGNORECASE):
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callback(0.1, "Start to parse.")
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binary = BytesIO(binary)
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doc_parsed = parser.from_buffer(binary)
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sections = doc_parsed['content'].split('\n')
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sections = [(l, "") for l in sections if l]
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remove_contents_table(sections, eng=is_english(
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random_choices([t for t, _ in sections], k=200)))
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callback(0.8, "Finish parsing.")
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else:
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raise NotImplementedError(
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"file type not supported yet(doc, docx, pdf, txt supported)")
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make_colon_as_title(sections)
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bull = bullets_category(
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[t for t in random_choices([t for t, _ in sections], k=100)])
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if bull >= 0:
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chunks = ["\n".join(ck)
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for ck in hierarchical_merge(bull, sections, 5)]
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else:
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sections = [s.split("@") for s, _ in sections]
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sections = [(pr[0], "@" + pr[1]) if len(pr) == 2 else (pr[0], '') for pr in sections ]
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chunks = naive_merge(
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sections, kwargs.get(
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"chunk_token_num", 256), kwargs.get(
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"delimer", "\n。；！？"))
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# is it English
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# is_english(random_choices([t for t, _ in sections], k=218))
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eng = lang.lower() == "english"
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res = tokenize_table(tbls, doc, eng)
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res.extend(tokenize_chunks(chunks, doc, eng, pdf_parser))
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return res
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if __name__ == "__main__":
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import sys
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def dummy(prog=None, msg=""):
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pass
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chunk(sys.argv[1], from_page=1, to_page=10, callback=dummy)
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