from __future__ import annotations
import copy
import pathlib
import re
from io import BytesIO, StringIO
from typing import (
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Sequence,
Tuple,
TypedDict,
cast,
)
import requests
from langchain_core._api import beta
from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_text_splitters.character import RecursiveCharacterTextSplitter
[docs]
class ElementType(TypedDict):
"""Element type as typed dict."""
url: str
xpath: str
content: str
metadata: Dict[str, str]
[docs]
class HTMLSectionSplitter:
"""Splitting HTML files based on specified tag and font sizes.
Requires lxml package.
"""
[docs]
def __init__(
self,
headers_to_split_on: List[Tuple[str, str]],
xslt_path: Optional[str] = None,
**kwargs: Any,
) -> None:
"""Create a new HTMLSectionSplitter.
Args:
headers_to_split_on: list of tuples of headers we want to track mapped to
(arbitrary) keys for metadata. Allowed header values: h1, h2, h3, h4,
h5, h6 e.g. [("h1", "Header 1"), ("h2", "Header 2"].
xslt_path: path to xslt file for document transformation.
Uses a default if not passed.
Needed for html contents that using different format and layouts.
**kwargs (Any): Additional optional arguments for customizations.
"""
self.headers_to_split_on = dict(headers_to_split_on)
if xslt_path is None:
self.xslt_path = (
pathlib.Path(__file__).parent / "xsl/converting_to_header.xslt"
).absolute()
else:
self.xslt_path = pathlib.Path(xslt_path).absolute()
self.kwargs = kwargs
[docs]
def split_documents(self, documents: Iterable[Document]) -> List[Document]:
"""Split documents."""
texts, metadatas = [], []
for doc in documents:
texts.append(doc.page_content)
metadatas.append(doc.metadata)
results = self.create_documents(texts, metadatas=metadatas)
text_splitter = RecursiveCharacterTextSplitter(**self.kwargs)
return text_splitter.split_documents(results)
[docs]
def split_text(self, text: str) -> List[Document]:
"""Split HTML text string.
Args:
text: HTML text
"""
return self.split_text_from_file(StringIO(text))
[docs]
def create_documents(
self, texts: List[str], metadatas: Optional[List[dict]] = None
) -> List[Document]:
"""Create documents from a list of texts."""
_metadatas = metadatas or [{}] * len(texts)
documents = []
for i, text in enumerate(texts):
for chunk in self.split_text(text):
metadata = copy.deepcopy(_metadatas[i])
for key in chunk.metadata.keys():
if chunk.metadata[key] == "#TITLE#":
chunk.metadata[key] = metadata["Title"]
metadata = {**metadata, **chunk.metadata}
new_doc = Document(page_content=chunk.page_content, metadata=metadata)
documents.append(new_doc)
return documents
[docs]
def split_text_from_file(self, file: Any) -> List[Document]:
"""Split HTML file.
Args:
file: HTML file
"""
file_content = file.getvalue()
file_content = self.convert_possible_tags_to_header(file_content)
sections = self.split_html_by_headers(file_content)
return [
Document(
cast(str, section["content"]),
metadata={
self.headers_to_split_on[str(section["tag_name"])]: section[
"header"
]
},
)
for section in sections
]
[docs]
@beta()
class HTMLSemanticPreservingSplitter(BaseDocumentTransformer):
"""Split HTML content preserving semantic structure.
Splits HTML content by headers into generalized chunks, preserving semantic
structure. If chunks exceed the maximum chunk size, it uses
RecursiveCharacterTextSplitter for further splitting.
The splitter preserves full HTML elements (e.g., <table>, <ul>) and converts
links to Markdown-like links. It can also preserve images, videos, and audio
elements by converting them into Markdown format. Note that some chunks may
exceed the maximum size to maintain semantic integrity.
.. versionadded: 0.3.5
Args:
headers_to_split_on (List[Tuple[str, str]]): HTML headers (e.g., "h1", "h2")
that define content sections.
max_chunk_size (int): Maximum size for each chunk, with allowance for
exceeding this limit to preserve semantics.
chunk_overlap (int): Number of characters to overlap between chunks to ensure
contextual continuity.
separators (List[str]): Delimiters used by RecursiveCharacterTextSplitter for
further splitting.
elements_to_preserve (List[str]): HTML tags (e.g., <table>, <ul>) to remain
intact during splitting.
preserve_links (bool): Converts <a> tags to Markdown links ([text](url)).
preserve_images (bool): Converts <img> tags to Markdown images ().
preserve_videos (bool): Converts <video> tags to Markdown
video links ().
preserve_audio (bool): Converts <audio> tags to Markdown
audio links ().
custom_handlers (Dict[str, Callable[[Any], str]]): Optional custom handlers for
specific HTML tags, allowing tailored extraction or processing.
stopword_removal (bool): Optionally remove stopwords from the text.
stopword_lang (str): The language of stopwords to remove.
normalize_text (bool): Optionally normalize text
(e.g., lowercasing, removing punctuation).
external_metadata (Optional[Dict[str, str]]): Additional metadata to attach to
the Document objects.
allowlist_tags (Optional[List[str]]): Only these tags will be retained in
the HTML.
denylist_tags (Optional[List[str]]): These tags will be removed from the HTML.
preserve_parent_metadata (bool): Whether to pass through parent document
metadata to split documents when calling
``transform_documents/atransform_documents()``.
Example:
.. code-block:: python
from langchain_text_splitters.html import HTMLSemanticPreservingSplitter
def custom_iframe_extractor(iframe_tag):
```
Custom handler function to extract the 'src' attribute from an <iframe> tag.
Converts the iframe to a Markdown-like link: [iframe:<src>](src).
Args:
iframe_tag (bs4.element.Tag): The <iframe> tag to be processed.
Returns:
str: A formatted string representing the iframe in Markdown-like format.
```
iframe_src = iframe_tag.get('src', '')
return f"[iframe:{iframe_src}]({iframe_src})"
text_splitter = HTMLSemanticPreservingSplitter(
headers_to_split_on=[("h1", "Header 1"), ("h2", "Header 2")],
max_chunk_size=500,
preserve_links=True,
preserve_images=True,
custom_handlers={"iframe": custom_iframe_extractor}
)
""" # noqa: E501, D214
[docs]
def __init__(
self,
headers_to_split_on: List[Tuple[str, str]],
*,
max_chunk_size: int = 1000,
chunk_overlap: int = 0,
separators: Optional[List[str]] = None,
elements_to_preserve: Optional[List[str]] = None,
preserve_links: bool = False,
preserve_images: bool = False,
preserve_videos: bool = False,
preserve_audio: bool = False,
custom_handlers: Optional[Dict[str, Callable[[Any], str]]] = None,
stopword_removal: bool = False,
stopword_lang: str = "english",
normalize_text: bool = False,
external_metadata: Optional[Dict[str, str]] = None,
allowlist_tags: Optional[List[str]] = None,
denylist_tags: Optional[List[str]] = None,
preserve_parent_metadata: bool = False,
):
"""Initialize splitter."""
try:
from bs4 import BeautifulSoup, Tag
self._BeautifulSoup = BeautifulSoup
self._Tag = Tag
except ImportError:
raise ImportError(
"Could not import BeautifulSoup. "
"Please install it with 'pip install bs4'."
)
self._headers_to_split_on = sorted(headers_to_split_on)
self._max_chunk_size = max_chunk_size
self._elements_to_preserve = elements_to_preserve or []
self._preserve_links = preserve_links
self._preserve_images = preserve_images
self._preserve_videos = preserve_videos
self._preserve_audio = preserve_audio
self._custom_handlers = custom_handlers or {}
self._stopword_removal = stopword_removal
self._stopword_lang = stopword_lang
self._normalize_text = normalize_text
self._external_metadata = external_metadata or {}
self._allowlist_tags = allowlist_tags
self._preserve_parent_metadata = preserve_parent_metadata
if allowlist_tags:
self._allowlist_tags = list(
set(allowlist_tags + [header[0] for header in headers_to_split_on])
)
self._denylist_tags = denylist_tags
if denylist_tags:
self._denylist_tags = [
tag
for tag in denylist_tags
if tag not in [header[0] for header in headers_to_split_on]
]
if separators:
self._recursive_splitter = RecursiveCharacterTextSplitter(
separators=separators,
chunk_size=max_chunk_size,
chunk_overlap=chunk_overlap,
)
else:
self._recursive_splitter = RecursiveCharacterTextSplitter(
chunk_size=max_chunk_size, chunk_overlap=chunk_overlap
)
if self._stopword_removal:
try:
import nltk # type: ignore
from nltk.corpus import stopwords # type: ignore
nltk.download("stopwords")
self._stopwords = set(stopwords.words(self._stopword_lang))
except ImportError:
raise ImportError(
"Could not import nltk. Please install it with 'pip install nltk'."
)
[docs]
def split_text(self, text: str) -> List[Document]:
"""Splits the provided HTML text into smaller chunks based on the configuration.
Args:
text (str): The HTML content to be split.
Returns:
List[Document]: A list of Document objects containing the split content.
"""
soup = self._BeautifulSoup(text, "html.parser")
self._process_media(soup)
if self._preserve_links:
self._process_links(soup)
if self._allowlist_tags or self._denylist_tags:
self._filter_tags(soup)
return self._process_html(soup)
def _process_media(self, soup: Any) -> None:
"""Processes the media elements.
Process elements in the HTML content by wrapping them in a <media-wrapper> tag
and converting them to Markdown format.
Args:
soup (Any): Parsed HTML content using BeautifulSoup.
"""
if self._preserve_images:
for img_tag in soup.find_all("img"):
img_src = img_tag.get("src", "")
markdown_img = f""
wrapper = soup.new_tag("media-wrapper")
wrapper.string = markdown_img
img_tag.replace_with(wrapper)
if self._preserve_videos:
for video_tag in soup.find_all("video"):
video_src = video_tag.get("src", "")
markdown_video = f""
wrapper = soup.new_tag("media-wrapper")
wrapper.string = markdown_video
video_tag.replace_with(wrapper)
if self._preserve_audio:
for audio_tag in soup.find_all("audio"):
audio_src = audio_tag.get("src", "")
markdown_audio = f""
wrapper = soup.new_tag("media-wrapper")
wrapper.string = markdown_audio
audio_tag.replace_with(wrapper)
def _process_links(self, soup: Any) -> None:
"""Processes the links in the HTML content.
Args:
soup (Any): Parsed HTML content using BeautifulSoup.
"""
for a_tag in soup.find_all("a"):
a_href = a_tag.get("href", "")
a_text = a_tag.get_text(strip=True)
markdown_link = f"[{a_text}]({a_href})"
wrapper = soup.new_tag("link-wrapper")
wrapper.string = markdown_link
a_tag.replace_with(markdown_link)
def _filter_tags(self, soup: Any) -> None:
"""Filters the HTML content based on the allowlist and denylist tags.
Args:
soup (Any): Parsed HTML content using BeautifulSoup.
"""
if self._allowlist_tags:
for tag in soup.find_all(True):
if tag.name not in self._allowlist_tags:
tag.decompose()
if self._denylist_tags:
for tag in soup.find_all(self._denylist_tags):
tag.decompose()
def _normalize_and_clean_text(self, text: str) -> str:
"""Normalizes the text by removing extra spaces and newlines.
Args:
text (str): The text to be normalized.
Returns:
str: The normalized text.
"""
if self._normalize_text:
text = text.lower()
text = re.sub(r"[^\w\s]", "", text)
text = re.sub(r"\s+", " ", text).strip()
if self._stopword_removal:
text = " ".join(
[word for word in text.split() if word not in self._stopwords]
)
return text
def _process_html(self, soup: Any) -> List[Document]:
"""Processes the HTML content using BeautifulSoup and splits it using headers.
Args:
soup (Any): Parsed HTML content using BeautifulSoup.
Returns:
List[Document]: A list of Document objects containing the split content.
"""
documents: List[Document] = []
current_headers: Dict[str, str] = {}
current_content: List[str] = []
preserved_elements: Dict[str, str] = {}
placeholder_count: int = 0
def _get_element_text(element: Any) -> str:
"""Recursively extracts and processes the text of an element.
Applies custom handlers where applicable, and ensures correct spacing.
Args:
element (Any): The HTML element to process.
Returns:
str: The processed text of the element.
"""
if element.name in self._custom_handlers:
return self._custom_handlers[element.name](element)
text = ""
if element.name is not None:
for child in element.children:
child_text = _get_element_text(child).strip()
if text and child_text:
text += " "
text += child_text
elif element.string:
text += element.string
return self._normalize_and_clean_text(text)
elements = soup.find_all(recursive=False)
def _process_element(
element: List[Any],
documents: List[Document],
current_headers: Dict[str, str],
current_content: List[str],
preserved_elements: Dict[str, str],
placeholder_count: int,
) -> Tuple[List[Document], Dict[str, str], List[str], Dict[str, str], int]:
for elem in element:
if elem.name.lower() in ["html", "body", "div"]:
children = elem.find_all(recursive=False)
(
documents,
current_headers,
current_content,
preserved_elements,
placeholder_count,
) = _process_element(
children,
documents,
current_headers,
current_content,
preserved_elements,
placeholder_count,
)
continue
if elem.name in [h[0] for h in self._headers_to_split_on]:
if current_content:
documents.extend(
self._create_documents(
current_headers,
" ".join(current_content),
preserved_elements,
)
)
current_content.clear()
preserved_elements.clear()
header_name = elem.get_text(strip=True)
current_headers = {
dict(self._headers_to_split_on)[elem.name]: header_name
}
elif elem.name in self._elements_to_preserve:
placeholder = f"PRESERVED_{placeholder_count}"
preserved_elements[placeholder] = _get_element_text(elem)
current_content.append(placeholder)
placeholder_count += 1
else:
content = _get_element_text(elem)
if content:
current_content.append(content)
return (
documents,
current_headers,
current_content,
preserved_elements,
placeholder_count,
)
# Process the elements
(
documents,
current_headers,
current_content,
preserved_elements,
placeholder_count,
) = _process_element(
elements,
documents,
current_headers,
current_content,
preserved_elements,
placeholder_count,
)
# Handle any remaining content
if current_content:
documents.extend(
self._create_documents(
current_headers, " ".join(current_content), preserved_elements
)
)
return documents
def _create_documents(
self, headers: dict, content: str, preserved_elements: dict
) -> List[Document]:
"""Creates Document objects from the provided headers, content, and elements.
Args:
headers (dict): The headers to attach as metadata to the Document.
content (str): The content of the Document.
preserved_elements (dict): Preserved elements to be reinserted
into the content.
Returns:
List[Document]: A list of Document objects.
"""
content = re.sub(r"\s+", " ", content).strip()
metadata = {**headers, **self._external_metadata}
if len(content) <= self._max_chunk_size:
page_content = self._reinsert_preserved_elements(
content, preserved_elements
)
return [Document(page_content=page_content, metadata=metadata)]
else:
return self._further_split_chunk(content, metadata, preserved_elements)
def _further_split_chunk(
self, content: str, metadata: dict, preserved_elements: dict
) -> List[Document]:
"""Further splits the content into smaller chunks.
Args:
content (str): The content to be split.
metadata (dict): Metadata to attach to each chunk.
preserved_elements (dict): Preserved elements
to be reinserted into each chunk.
Returns:
List[Document]: A list of Document objects containing the split content.
"""
splits = self._recursive_splitter.split_text(content)
result = []
for split in splits:
split_with_preserved = self._reinsert_preserved_elements(
split, preserved_elements
)
if split_with_preserved.strip():
result.append(
Document(
page_content=split_with_preserved.strip(), metadata=metadata
)
)
return result
def _reinsert_preserved_elements(
self, content: str, preserved_elements: dict
) -> str:
"""Reinserts preserved elements into the content into their original positions.
Args:
content (str): The content where placeholders need to be replaced.
preserved_elements (dict): Preserved elements to be reinserted.
Returns:
str: The content with placeholders replaced by preserved elements.
"""
for placeholder, preserved_content in preserved_elements.items():
content = content.replace(placeholder, preserved_content.strip())
return content
