Source code for langchain_text_splitters.html
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
[docs]
class ElementType(TypedDict):
"""Element type as typed dict."""
url: str
xpath: str
content: str
metadata: Dict[str, str]
[docs]
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."
"""
[docs]
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.
"""
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
[docs]
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))
[docs]
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 _header_level(self, tag_name: str) -> int:
"""Determine the heading level of a tag."""
if tag_name.lower() in ["h1", "h2", "h3", "h4", "h5", "h6"]:
return int(tag_name[1])
# Returns high level if it isn't a header
return 9999
def _dom_depth(self, element: Any) -> int:
"""Determine the DOM depth of an element by counting its parents."""
depth = 0
for _ in element.parents:
depth += 1
return depth
def _get_elements(self, html_content: str) -> List[Any]:
"""Parse HTML content and return a list of BeautifulSoup elements.
This helper function takes HTML content as input,
parses it using BeautifulSoup4, and returns all HTML elements
found in the document body. If no body tag exists,
it returns all elements in the full document.
Args:
html_content: Raw HTML content to be parsed.
Returns:
List[Any]: A list of BeautifulSoup elements found in the HTML document.
Raises:
ImportError: If the BeautifulSoup4 package is not installed.
"""
try:
from bs4 import BeautifulSoup # type: ignore[import-untyped]
except ImportError as e:
raise ImportError(
"Unable to import BeautifulSoup/PageElement, \
please install with `pip install \
bs4`."
) from e
soup = BeautifulSoup(html_content, "html.parser")
body = soup.body if soup.body else soup
return body.find_all()
[docs]
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()
elements = self._get_elements(html_content)
documents: List[Document] = []
active_headers: Dict[str, Tuple[str, int, int]] = {}
current_chunk: List[str] = []
chunk_dom_depth = 0
def finalize_chunk() -> None:
if current_chunk:
final_meta = {
key: content
for key, (content, level, dom_depth) in active_headers.items()
if chunk_dom_depth >= dom_depth
}
combined_text = " \n".join(
line for line in current_chunk if line.strip()
)
if combined_text.strip():
documents.append(
Document(page_content=combined_text, metadata=final_meta)
)
current_chunk.clear()
for element in elements:
tag = element.name
if not tag:
continue
text = " ".join(
t
for t in element.find_all(string=True, recursive=False)
if isinstance(t, str)
).strip()
if not text:
continue
level = self._header_level(tag)
dom_depth = self._dom_depth(element)
if tag in self.header_tags:
if not self.return_each_element:
finalize_chunk()
# Remove headers at same or deeper level
headers_to_remove = [
key for key, (_, lvl, _) in active_headers.items() if lvl >= level
]
for key in headers_to_remove:
del active_headers[key]
header_key = self.header_mapping[tag]
active_headers[header_key] = (text, level, dom_depth)
# Produce a document for the header itself
header_meta = {
key: content
for key, (content, lvl, dd) in active_headers.items()
if dom_depth >= dd
}
documents.append(Document(page_content=text, metadata=header_meta))
# After encountering a header,
# no immediate content goes to current_chunk
# (if return_each_element is False, we wait for next content)
# (if return_each_element is True, we create docs per element anyway)
else:
# Non-header element logic
# Remove headers that don't apply if dom_depth < their dom_depth
headers_to_remove = [
key for key, (_, _, dd) in active_headers.items() if dom_depth < dd
]
for key in headers_to_remove:
del active_headers[key]
if self.return_each_element:
# Produce a doc for this element immediately
element_meta = {
key: content
for key, (content, lvl, dd) in active_headers.items()
if dom_depth >= dd
}
if text.strip():
documents.append(
Document(page_content=text, metadata=element_meta)
)
else:
# Accumulate content in current_chunk
if text.strip():
current_chunk.append(text)
chunk_dom_depth = max(chunk_dom_depth, dom_depth)
if not self.return_each_element:
# finalize any remaining chunk
finalize_chunk()
# If no headers were found at all and return_each_element=False, behavior is:
# The entire content should be in one document.
# The logic above naturally handles it:
# If no recognized headers, we never split; we ended up just accumulating text
# in current_chunk and finalizing once at the end.
return documents
[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_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
[docs]
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)
[docs]
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
]
[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)
[docs]
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""
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", "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
# %%