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langchain/libs/text-splitters/langchain_text_splitters/html.py
Ahmed Tammaa f23c3e2444
text-splitters[patch]: Refactor HTMLHeaderTextSplitter for Enhanced Maintainability and Readability (#29397) 
Please see PR #27678 for context

## Overview

This pull request presents a refactor of the `HTMLHeaderTextSplitter`
class aimed at improving its maintainability and readability. The
primary enhancements include simplifying the internal structure by
consolidating multiple private helper functions into a single private
method, thereby reducing complexity and making the codebase easier to
understand and extend. Importantly, all existing functionalities and
public interfaces remain unchanged.

## PR Goals

1. **Simplify Internal Logic**:
- **Consolidation of Private Methods**: The original implementation
utilized multiple private helper functions (`_header_level`,
`_dom_depth`, `_get_elements`) to manage different aspects of HTML
parsing and document generation. This fragmentation increased cognitive
load and potential maintenance overhead.
- **Streamlined Processing**: By merging these functionalities into a
single private method (`_generate_documents`), the class now offers a
more straightforward flow, making it easier for developers to trace and
understand the processing steps. (Thanks to @eyurtsev)

2. **Enhance Readability**:
- **Clearer Method Responsibilities**: With fewer private methods, each
method now has a more focused responsibility. The primary logic resides
within `_generate_documents`, which handles both HTML traversal and
document creation in a cohesive manner.
- **Reduced Redundancy**: Eliminating redundant checks and consolidating
logic reduces the code's verbosity, making it more concise without
sacrificing clarity.

3. **Improve Maintainability**:
- **Easier Debugging and Extension**: A simplified internal structure
allows for quicker identification of issues and easier implementation of
future enhancements or feature additions.
- **Consistent Header Management**: The new implementation ensures that
headers are managed consistently within a single context, reducing the
likelihood of bugs related to header scope and hierarchy.

4. **Maintain Backward Compatibility**:
- **Unchanged Public Interface**: All public methods (`split_text`,
`split_text_from_url`, `split_text_from_file`) and their signatures
remain unchanged, ensuring that existing integrations and usage patterns
are unaffected.
- **Preserved Docstrings**: Comprehensive docstrings are retained,
providing clear documentation for users and developers alike.

## Detailed Changes

1. **Removed Redundant Private Methods**:
- **Eliminated `_header_level`, `_dom_depth`, and `_get_elements`**:
These methods were merged into the `_generate_documents` method,
centralizing the logic for HTML parsing and document generation.

2. **Consolidated Document Generation Logic**:
- **Single Private Method `_generate_documents`**: This method now
handles the entire process of parsing HTML, tracking active headers,
managing document chunks, and yielding `Document` instances. This
consolidation reduces the number of moving parts and simplifies the
overall processing flow.

3. **Simplified Header Management**:
- **Immediate Header Scope Handling**: Headers are now managed within
the traversal loop of `_generate_documents`, ensuring that headers are
added or removed from the active headers dictionary in real-time based
on their DOM depth and hierarchy.
- **Removed `chunk_dom_depth` Attribute**: The need to track chunk DOM
depth separately has been eliminated, as header scopes are now directly
managed within the traversal logic.

4. **Streamlined Chunk Finalization**:
- **Enhanced `finalize_chunk` Function**: The chunk finalization process
has been simplified to directly yield a single `Document` when needed,
without maintaining an intermediate list. This change reduces
unnecessary list operations and makes the logic more straightforward.

5. **Improved Variable Naming and Flow**:
- **Descriptive Variable Names**: Variables such as `current_chunk` and
`node_text` provide clear insights into their roles within the
processing logic.
- **Direct Header Removal Logic**: Headers that are out of scope are
removed immediately during traversal, ensuring that the active headers
dictionary remains accurate and up-to-date.

6. **Preserved Comprehensive Docstrings**:
- **Unchanged Documentation**: All existing docstrings, including
class-level and method-level documentation, remain intact. This ensures
that users and developers continue to have access to detailed usage
instructions and method explanations.

## Testing

All existing test cases from `test_html_header_text_splitter.py` have
been executed against the refactored code. The results confirm that:

- **Functionality Remains Intact**: The splitter continues to accurately
parse HTML content, respect header hierarchies, and produce the expected
`Document` objects with correct metadata.
- **Backward Compatibility is Maintained**: No changes were required in
the test cases, and all tests pass without modifications, demonstrating
that the refactor does not introduce any regressions or alter existing
behaviors.


This example remains fully operational and behaves as before, returning
a list of `Document` objects with the expected metadata and content
splits.

## Conclusion

This refactor achieves a more maintainable and readable codebase by
simplifying the internal structure of the `HTMLHeaderTextSplitter`
class. By consolidating multiple private methods into a single, cohesive
private method, the class becomes easier to understand, debug, and
extend. All existing functionalities are preserved, and comprehensive
tests confirm that the refactor maintains the expected behavior. These
changes align with LangChain’s standards for clean, maintainable, and
efficient code.

---

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
2025-03-28 15:36:00 -04:00

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from __future__ import annotations
import copy
import pathlib
import re
from io import 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
class ElementType(TypedDict):
"""Element type as typed dict."""
url: str
xpath: str
content: str
metadata: Dict[str, str]
class HTMLHeaderTextSplitter:
"""Split HTML content into structured Documents based on specified headers.
Splits HTML content by detecting specified header tags (e.g., <h1>, <h2>) and
creating hierarchical Document objects that reflect the semantic structure
of the original content. For each identified section, the splitter associates
the extracted text with metadata corresponding to the encountered headers.
If no specified headers are found, the entire content is returned as a single
Document. This allows for flexible handling of HTML input, ensuring that
information is organized according to its semantic headers.
The splitter provides the option to return each HTML element as a separate
Document or aggregate them into semantically meaningful chunks. It also
gracefully handles multiple levels of nested headers, creating a rich,
hierarchical representation of the content.
Args:
headers_to_split_on (List[Tuple[str, str]]): A list of (header_tag,
header_name) pairs representing the headers that define splitting
boundaries. For example, [("h1", "Header 1"), ("h2", "Header 2")]
will split content by <h1> and <h2> tags, assigning their textual
content to the Document metadata.
return_each_element (bool): If True, every HTML element encountered
(including headers, paragraphs, etc.) is returned as a separate
Document. If False, content under the same header hierarchy is
aggregated into fewer Documents.
Returns:
List[Document]: A list of Document objects. Each Document contains
`page_content` holding the extracted text and `metadata` that maps
the header hierarchy to their corresponding titles.
Example:
.. code-block:: python
from langchain_text_splitters.html_header_text_splitter import (
HTMLHeaderTextSplitter,
)
# Define headers for splitting on h1 and h2 tags.
headers_to_split_on = [("h1", "Main Topic"), ("h2", "Sub Topic")]
splitter = HTMLHeaderTextSplitter(
headers_to_split_on=headers_to_split_on,
return_each_element=False
)
html_content = \"\"\"
<html>
<body>
<h1>Introduction</h1>
<p>Welcome to the introduction section.</p>
<h2>Background</h2>
<p>Some background details here.</p>
<h1>Conclusion</h1>
<p>Final thoughts.</p>
</body>
</html>
\"\"\"
documents = splitter.split_text(html_content)
# 'documents' now contains Document objects reflecting the hierarchy:
# - Document with metadata={"Main Topic": "Introduction"} and
# content="Introduction"
# - Document with metadata={"Main Topic": "Introduction"} and
# content="Welcome to the introduction section."
# - Document with metadata={"Main Topic": "Introduction",
# "Sub Topic": "Background"} and content="Background"
# - Document with metadata={"Main Topic": "Introduction",
# "Sub Topic": "Background"} and content="Some background details here."
# - Document with metadata={"Main Topic": "Conclusion"} and
# content="Conclusion"
# - Document with metadata={"Main Topic": "Conclusion"} and
# content="Final thoughts."
"""
def __init__(
self,
headers_to_split_on: List[Tuple[str, str]],
return_each_element: bool = False,
) -> None:
"""Initialize with headers to split on.
Args:
headers_to_split_on: A list of tuples where
each tuple contains a header tag and its corresponding value.
return_each_element: Whether to return each HTML
element as a separate Document. Defaults to False.
"""
# Sort headers by their numeric level so that h1 < h2 < h3...
self.headers_to_split_on = sorted(
headers_to_split_on, key=lambda x: int(x[0][1:])
)
self.header_mapping = dict(self.headers_to_split_on)
self.header_tags = [tag for tag, _ in self.headers_to_split_on]
self.return_each_element = return_each_element
def split_text(self, text: str) -> List[Document]:
"""Split the given text into a list of Document objects.
Args:
text: The HTML text to split.
Returns:
A list of split Document objects.
"""
return self.split_text_from_file(StringIO(text))
def split_text_from_url(
self, url: str, timeout: int = 10, **kwargs: Any
) -> List[Document]:
"""Fetch text content from a URL and split it into documents.
Args:
url: The URL to fetch content from.
timeout: Timeout for the request. Defaults to 10.
**kwargs: Additional keyword arguments for the request.
Returns:
A list of split Document objects.
Raises:
requests.RequestException: If the HTTP request fails.
"""
kwargs.setdefault("timeout", timeout)
response = requests.get(url, **kwargs)
response.raise_for_status()
return self.split_text(response.text)
def split_text_from_file(self, file: Any) -> List[Document]:
"""Split HTML content from a file into a list of Document objects.
Args:
file: A file path or a file-like object containing HTML content.
Returns:
A list of split Document objects.
"""
if isinstance(file, str):
with open(file, "r", encoding="utf-8") as f:
html_content = f.read()
else:
html_content = file.read()
return list(self._generate_documents(html_content))
def _generate_documents(self, html_content: str) -> Any:
"""Private method that performs a DFS traversal over the DOM and yields.
Document objects on-the-fly. This approach maintains the same splitting
logic (headers vs. non-headers, chunking, etc.) while walking the DOM
explicitly in code.
Args:
html_content: The raw HTML content.
Yields:
Document objects as they are created.
"""
try:
from bs4 import BeautifulSoup
except ImportError as e:
raise ImportError(
"Unable to import BeautifulSoup. Please install via `pip install bs4`."
) from e
soup = BeautifulSoup(html_content, "html.parser")
body = soup.body if soup.body else soup
# Dictionary of active headers:
# key = user-defined header name (e.g. "Header 1")
# value = (header_text, level, dom_depth)
active_headers: Dict[str, Tuple[str, int, int]] = {}
current_chunk: List[str] = []
def finalize_chunk() -> Optional[Document]:
"""Finalize the accumulated chunk into a single Document."""
if not current_chunk:
return None
final_text = " \n".join(line for line in current_chunk if line.strip())
current_chunk.clear()
if not final_text.strip():
return None
final_meta = {k: v[0] for k, v in active_headers.items()}
return Document(page_content=final_text, metadata=final_meta)
# We'll use a stack for DFS traversal
stack = [body]
while stack:
node = stack.pop()
children = list(node.children)
from bs4.element import Tag
for child in reversed(children):
if isinstance(child, Tag):
stack.append(child)
tag = getattr(node, "name", None)
if not tag:
continue
text_elements = [
str(child).strip()
for child in node.find_all(string=True, recursive=False)
]
node_text = " ".join(elem for elem in text_elements if elem)
if not node_text:
continue
dom_depth = len(list(node.parents))
# If this node is one of our headers
if tag in self.header_tags:
# If we're aggregating, finalize whatever chunk we had
if not self.return_each_element:
doc = finalize_chunk()
if doc:
yield doc
# Determine numeric level (h1->1, h2->2, etc.)
try:
level = int(tag[1:])
except ValueError:
level = 9999
# Remove any active headers that are at or deeper than this new level
headers_to_remove = [
k for k, (_, lvl, d) in active_headers.items() if lvl >= level
]
for key in headers_to_remove:
del active_headers[key]
# Add/Update the active header
header_name = self.header_mapping[tag]
active_headers[header_name] = (node_text, level, dom_depth)
# Always yield a Document for the header
header_meta = {k: v[0] for k, v in active_headers.items()}
yield Document(page_content=node_text, metadata=header_meta)
else:
headers_out_of_scope = [
k for k, (_, _, d) in active_headers.items() if dom_depth < d
]
for key in headers_out_of_scope:
del active_headers[key]
if self.return_each_element:
# Yield each element's text as its own Document
meta = {k: v[0] for k, v in active_headers.items()}
yield Document(page_content=node_text, metadata=meta)
else:
# Accumulate text in our chunk
current_chunk.append(node_text)
# If we're aggregating and have leftover chunk, yield it
if not self.return_each_element:
doc = finalize_chunk()
if doc:
yield doc
class HTMLSectionSplitter:
"""Splitting HTML files based on specified tag and font sizes.
Requires lxml package.
"""
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
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)
def split_text(self, text: str) -> List[Document]:
"""Split HTML text string.
Args:
text: HTML text
"""
return self.split_text_from_file(StringIO(text))
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
def split_html_by_headers(self, html_doc: str) -> List[Dict[str, Optional[str]]]:
"""Split an HTML document into sections based on specified header tags.
This method uses BeautifulSoup to parse the HTML content and divides it into
sections based on headers defined in `headers_to_split_on`. Each section
contains the header text, content under the header, and the tag name.
Args:
html_doc (str): The HTML document to be split into sections.
Returns:
List[Dict[str, Optional[str]]]: A list of dictionaries representing
sections.
Each dictionary contains:
- 'header': The header text or a default title for the first section.
- 'content': The content under the header.
- 'tag_name': The name of the header tag (e.g., "h1", "h2").
"""
try:
from bs4 import (
BeautifulSoup, # type: ignore[import-untyped]
PageElement,
)
except ImportError as e:
raise ImportError(
"Unable to import BeautifulSoup/PageElement, \
please install with `pip install \
bs4`."
) from e
soup = BeautifulSoup(html_doc, "html.parser")
headers = list(self.headers_to_split_on.keys())
sections: list[dict[str, str | None]] = []
headers = soup.find_all(["body"] + headers) # type: ignore[assignment]
for i, header in enumerate(headers):
header_element = cast(PageElement, header)
if i == 0:
current_header = "#TITLE#"
current_header_tag = "h1"
section_content: List = []
else:
current_header = header_element.text.strip()
current_header_tag = header_element.name # type: ignore[attr-defined]
section_content = []
for element in header_element.next_elements:
if i + 1 < len(headers) and element == headers[i + 1]:
break
if isinstance(element, str):
section_content.append(element)
content = " ".join(section_content).strip()
if content != "":
sections.append(
{
"header": current_header,
"content": content,
"tag_name": current_header_tag,
}
)
return sections
def convert_possible_tags_to_header(self, html_content: str) -> str:
"""Convert specific HTML tags to headers using an XSLT transformation.
This method uses an XSLT file to transform the HTML content, converting
certain tags into headers for easier parsing. If no XSLT path is provided,
the HTML content is returned unchanged.
Args:
html_content (str): The HTML content to be transformed.
Returns:
str: The transformed HTML content as a string.
"""
if self.xslt_path is None:
return html_content
try:
from lxml import etree
except ImportError as e:
raise ImportError(
"Unable to import lxml, please install with `pip install lxml`."
) from e
# use lxml library to parse html document and return xml ElementTree
parser = etree.HTMLParser()
tree = etree.parse(StringIO(html_content), parser)
xslt_tree = etree.parse(self.xslt_path)
transform = etree.XSLT(xslt_tree)
result = transform(tree)
return str(result)
def split_text_from_file(self, file: Any) -> List[Document]:
"""Split HTML content from a file into a list of Document objects.
Args:
file: A file path or a file-like object containing HTML content.
Returns:
A list of split Document objects.
"""
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
]
@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 (![alt](src)).
preserve_videos (bool): Converts <video> tags to Markdown
video links (![video](src)).
preserve_audio (bool): Converts <audio> tags to Markdown
audio links (![audio](src)).
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
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'."
)
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 transform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> List[Document]:
"""Transform sequence of documents by splitting them."""
transformed = []
for doc in documents:
splits = self.split_text(doc.page_content)
if self._preserve_parent_metadata:
splits = [
Document(
page_content=split_doc.page_content,
metadata={**doc.metadata, **split_doc.metadata},
)
for split_doc in splits
]
transformed.extend(splits)
return transformed
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"![image:{img_src}]({img_src})"
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"![video:{video_src}]({video_src})"
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"![audio:{audio_src}]({audio_src})"
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", "main"]:
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
# %%
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