Add get_txt function (#2639)

### What problem does this PR solve?

Add get_txt function to reduce duplicate code

### Type of change

- [x] Refactoring

---------

Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
This commit is contained in:
yqkcn 2024-09-29 10:29:56 +08:00 committed by GitHub
parent 57237634f1
commit aea553c3a8
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GPG Key ID: B5690EEEBB952194
8 changed files with 52 additions and 88 deletions

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@ -10,28 +10,18 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
from deepdoc.parser.utils import get_txt
from rag.nlp import num_tokens_from_string
from rag.nlp import find_codec,num_tokens_from_string
import re
class RAGFlowTxtParser:
def __call__(self, fnm, binary=None, chunk_token_num=128, delimiter="\n!?;。;!?"):
txt = ""
if binary:
encoding = find_codec(binary)
txt = binary.decode(encoding, errors="ignore")
else:
with open(fnm, "r") as f:
while True:
l = f.readline()
if not l:
break
txt += l
txt = get_txt(fnm, binary)
return self.parser_txt(txt, chunk_token_num, delimiter)
@classmethod
def parser_txt(cls, txt, chunk_token_num=128, delimiter="\n!?;。;!?"):
if type(txt) != str:
if not isinstance(txt, str):
raise TypeError("txt type should be str!")
cks = [""]
tk_nums = [0]

29
deepdoc/parser/utils.py Normal file
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@ -0,0 +1,29 @@
# 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.
#
from rag.nlp import find_codec
def get_txt(fnm: str, binary=None) -> str:
txt = ""
if binary:
encoding = find_codec(binary)
txt = binary.decode(encoding, errors="ignore")
else:
with open(fnm, "r") as f:
while True:
line = f.readline()
if not line:
break
txt += line
return txt

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@ -15,6 +15,7 @@ from tika import parser
import re
from io import BytesIO
from deepdoc.parser.utils import get_text
from rag.nlp import bullets_category, is_english, tokenize, remove_contents_table, \
hierarchical_merge, make_colon_as_title, naive_merge, random_choices, tokenize_table, add_positions, \
tokenize_chunks, find_codec
@ -88,17 +89,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
elif re.search(r"\.txt$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
txt = ""
if binary:
encoding = find_codec(binary)
txt = binary.decode(encoding, errors="ignore")
else:
with open(filename, "r") as f:
while True:
l = f.readline()
if not l:
break
txt += l
txt = get_text(filename, binary)
sections = txt.split("\n")
sections = [(l, "") for l in sections if l]
remove_contents_table(sections, eng=is_english(

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@ -17,6 +17,7 @@ from io import BytesIO
from docx import Document
from api.db import ParserType
from deepdoc.parser.utils import get_txt
from rag.nlp import bullets_category, is_english, tokenize, remove_contents_table, hierarchical_merge, \
make_colon_as_title, add_positions, tokenize_chunks, find_codec, docx_question_level
from rag.nlp import rag_tokenizer
@ -165,17 +166,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
elif re.search(r"\.txt$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
txt = ""
if binary:
encoding = find_codec(binary)
txt = binary.decode(encoding, errors="ignore")
else:
with open(filename, "r") as f:
while True:
l = f.readline()
if not l:
break
txt += l
txt = get_txt(filename, binary)
sections = txt.split("\n")
sections = [l for l in sections if l]
callback(0.8, "Finish parsing.")

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@ -169,7 +169,6 @@ class Markdown(MarkdownParser):
return sections, tbls
def chunk(filename, binary=None, from_page=0, to_page=100000,
lang="Chinese", callback=None, **kwargs):
"""
@ -190,7 +189,6 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
res = []
pdf_parser = None
sections = []
if re.search(r"\.docx$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
sections, tbls = Docx()(filename, binary)
@ -222,13 +220,13 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
callback(0.1, "Start to parse.")
excel_parser = ExcelParser()
if parser_config.get("html4excel"):
sections = [(l, "") for l in excel_parser.html(binary, 12) if l]
sections = [(_, "") for _ in excel_parser.html(binary, 12) if _]
else:
sections = [(l, "") for l in excel_parser(binary) if l]
sections = [(_, "") for _ in excel_parser(binary) if _]
elif re.search(r"\.(txt|py|js|java|c|cpp|h|php|go|ts|sh|cs|kt|sql)$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
sections = TxtParser()(filename,binary,
sections = TxtParser()(filename, binary,
parser_config.get("chunk_token_num", 128),
parser_config.get("delimiter", "\n!?;。;!?"))
callback(0.8, "Finish parsing.")
@ -242,13 +240,13 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
elif re.search(r"\.(htm|html)$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
sections = HtmlParser()(filename, binary)
sections = [(l, "") for l in sections if l]
sections = [(_, "") for _ in sections if _]
callback(0.8, "Finish parsing.")
elif re.search(r"\.json$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
sections = JsonParser(int(parser_config.get("chunk_token_num", 128)))(binary)
sections = [(l, "") for l in sections if l]
sections = [(_, "") for _ in sections if _]
callback(0.8, "Finish parsing.")
elif re.search(r"\.doc$", filename, re.IGNORECASE):
@ -256,7 +254,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
binary = BytesIO(binary)
doc_parsed = parser.from_buffer(binary)
sections = doc_parsed['content'].split('\n')
sections = [(l, "") for l in sections if l]
sections = [(_, "") for _ in sections if _]
callback(0.8, "Finish parsing.")
else:

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@ -13,6 +13,8 @@
from tika import parser
from io import BytesIO
import re
from deepdoc.parser.utils import get_txt
from rag.app import laws
from rag.nlp import rag_tokenizer, tokenize, find_codec
from deepdoc.parser import PdfParser, ExcelParser, PlainParser, HtmlParser
@ -82,17 +84,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
elif re.search(r"\.(txt|md|markdown)$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
txt = ""
if binary:
encoding = find_codec(binary)
txt = binary.decode(encoding, errors="ignore")
else:
with open(filename, "r") as f:
while True:
l = f.readline()
if not l:
break
txt += l
txt = get_txt(filename, binary)
sections = txt.split("\n")
sections = [s for s in sections if s]
callback(0.8, "Finish parsing.")

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@ -16,6 +16,8 @@ from io import BytesIO
from timeit import default_timer as timer
from nltk import word_tokenize
from openpyxl import load_workbook
from deepdoc.parser.utils import get_txt
from rag.nlp import is_english, random_choices, find_codec, qbullets_category, add_positions, has_qbullet, docx_question_level
from rag.nlp import rag_tokenizer, tokenize_table, concat_img
from rag.settings import cron_logger
@ -305,17 +307,7 @@ def chunk(filename, binary=None, lang="Chinese", callback=None, **kwargs):
return res
elif re.search(r"\.(txt|csv)$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
txt = ""
if binary:
encoding = find_codec(binary)
txt = binary.decode(encoding, errors="ignore")
else:
with open(filename, "r") as f:
while True:
l = f.readline()
if not l:
break
txt += l
txt = get_txt(filename, binary)
lines = txt.split("\n")
comma, tab = 0, 0
for l in lines:
@ -358,17 +350,7 @@ def chunk(filename, binary=None, lang="Chinese", callback=None, **kwargs):
return res
elif re.search(r"\.(md|markdown)$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
txt = ""
if binary:
encoding = find_codec(binary)
txt = binary.decode(encoding, errors="ignore")
else:
with open(filename, "r") as f:
while True:
l = f.readline()
if not l:
break
txt += l
txt = get_txt(filename, binary)
lines = txt.split("\n")
last_question, last_answer = "", ""
question_stack, level_stack = [], []

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@ -20,6 +20,7 @@ from openpyxl import load_workbook
from dateutil.parser import parse as datetime_parse
from api.db.services.knowledgebase_service import KnowledgebaseService
from deepdoc.parser.utils import get_text
from rag.nlp import rag_tokenizer, is_english, tokenize, find_codec
from deepdoc.parser import ExcelParser
@ -146,17 +147,7 @@ def chunk(filename, binary=None, from_page=0, to_page=10000000000,
callback=callback)
elif re.search(r"\.(txt|csv)$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
txt = ""
if binary:
encoding = find_codec(binary)
txt = binary.decode(encoding, errors="ignore")
else:
with open(filename, "r") as f:
while True:
l = f.readline()
if not l:
break
txt += l
txt = get_text(filename, binary)
lines = txt.split("\n")
fails = []
headers = lines[0].split(kwargs.get("delimiter", "\t"))