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DocsGPT/application/parser/file/markdown_parser.py

146 lines
5.0 KiB
Python

"""Markdown parser.
Contains parser for md files.
"""
import re
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union, cast
import tiktoken
from parser.file.base_parser import BaseParser
class MarkdownParser(BaseParser):
"""Markdown parser.
Extract text from markdown files.
Returns dictionary with keys as headers and values as the text between headers.
"""
def __init__(
self,
*args: Any,
remove_hyperlinks: bool = True,
remove_images: bool = True,
max_tokens: int = 2048,
# remove_tables: bool = True,
**kwargs: Any,
) -> None:
"""Init params."""
super().__init__(*args, **kwargs)
self._remove_hyperlinks = remove_hyperlinks
self._remove_images = remove_images
self._max_tokens = max_tokens
# self._remove_tables = remove_tables
def tups_chunk_append(self, tups: List[Tuple[Optional[str], str]], current_header: Optional[str],
current_text: str):
"""Append to tups chunk."""
num_tokens = len(tiktoken.get_encoding("cl100k_base").encode(current_text))
if num_tokens > self._max_tokens:
chunks = [current_text[i:i + self._max_tokens] for i in range(0, len(current_text), self._max_tokens)]
for chunk in chunks:
tups.append((current_header, chunk))
else:
tups.append((current_header, current_text))
return tups
def markdown_to_tups(self, markdown_text: str) -> List[Tuple[Optional[str], str]]:
"""Convert a markdown file to a dictionary.
The keys are the headers and the values are the text under each header.
"""
markdown_tups: List[Tuple[Optional[str], str]] = []
lines = markdown_text.split("\n")
current_header = None
current_text = ""
for line in lines:
header_match = re.match(r"^#+\s", line)
if header_match:
if current_header is not None:
if current_text == "" or None:
continue
markdown_tups = self.tups_chunk_append(markdown_tups, current_header, current_text)
current_header = line
current_text = ""
else:
current_text += line + "\n"
markdown_tups = self.tups_chunk_append(markdown_tups, current_header, current_text)
if current_header is not None:
# pass linting, assert keys are defined
markdown_tups = [
(re.sub(r"#", "", cast(str, key)).strip(), re.sub(r"<.*?>", "", value))
for key, value in markdown_tups
]
else:
markdown_tups = [
(key, re.sub("\n", "", value)) for key, value in markdown_tups
]
return markdown_tups
def remove_images(self, content: str) -> str:
"""Get a dictionary of a markdown file from its path."""
pattern = r"!{1}\[\[(.*)\]\]"
content = re.sub(pattern, "", content)
return content
# def remove_tables(self, content: str) -> List[List[str]]:
# """Convert markdown tables to nested lists."""
# table_rows_pattern = r"((\r?\n){2}|^)([^\r\n]*\|[^\r\n]*(\r?\n)?)+(?=(\r?\n){2}|$)"
# table_cells_pattern = r"([^\|\r\n]*)\|"
#
# table_rows = re.findall(table_rows_pattern, content, re.MULTILINE)
# table_lists = []
# for row in table_rows:
# cells = re.findall(table_cells_pattern, row[2])
# cells = [cell.strip() for cell in cells if cell.strip()]
# table_lists.append(cells)
# return str(table_lists)
def remove_hyperlinks(self, content: str) -> str:
"""Get a dictionary of a markdown file from its path."""
pattern = r"\[(.*?)\]\((.*?)\)"
content = re.sub(pattern, r"\1", content)
return content
def _init_parser(self) -> Dict:
"""Initialize the parser with the config."""
return {}
def parse_tups(
self, filepath: Path, errors: str = "ignore"
) -> List[Tuple[Optional[str], str]]:
"""Parse file into tuples."""
with open(filepath, "r") as f:
content = f.read()
if self._remove_hyperlinks:
content = self.remove_hyperlinks(content)
if self._remove_images:
content = self.remove_images(content)
# if self._remove_tables:
# content = self.remove_tables(content)
markdown_tups = self.markdown_to_tups(content)
return markdown_tups
def parse_file(
self, filepath: Path, errors: str = "ignore"
) -> Union[str, List[str]]:
"""Parse file into string."""
tups = self.parse_tups(filepath, errors=errors)
results = []
# TODO: don't include headers right now
for header, value in tups:
if header is None:
results.append(value)
else:
results.append(f"\n\n{header}\n{value}")
return results