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如何拆分 HTML

将 HTML 文档拆分成可管理的小块对于各种文本处理任务（如自然语言处理、搜索索引等）至关重要。在本指南中，我们将探讨 LangChain 提供的三种不同的文本拆分器，您可以使用它们有效地拆分 HTML 内容

这些拆分器各有独特的功能和用例。本指南将帮助您理解它们之间的区别、为什么您可能选择其中一个而不是其他，以及如何有效地使用它们。

%pip install -qU langchain-text-splitters

拆分器概述​

HTMLHeaderTextSplitter​

信息

当您想要根据文档的标题保留文档的层次结构时非常有用。

描述：根据标题标签（例如，<h1>、<h2>、<h3> 等）拆分 HTML 文本，并为每个与任何给定块相关的标题添加元数据。

功能:

  • 在 HTML 元素级别拆分文本。
  • 保留文档结构中编码的上下文丰富的信息。
  • 可以逐元素返回块，或将具有相同元数据的元素组合在一起。

HTMLSectionSplitter​

信息

当您想要将 HTML 文档拆分成更大的部分时非常有用，例如 <section>、<div> 或自定义定义的部分。

描述：类似于 HTMLHeaderTextSplitter，但侧重于基于指定的标签将 HTML 拆分成部分。

功能:

  • 使用 XSLT 转换来检测和拆分部分。
  • 内部对大型部分使用 RecursiveCharacterTextSplitter。
  • 考虑字体大小来确定部分。

HTMLSemanticPreservingSplitter​

信息

当您需要确保结构化元素不会跨块拆分，从而保留上下文相关性时，这是理想的选择。

描述：将 HTML 内容拆分成可管理的小块，同时保留重要元素的语义结构，如表格、列表和其他 HTML 组件。

功能:

  • 保留表格、列表和其他指定的 HTML 元素。
  • 允许为特定的 HTML 标签自定义处理程序。
  • 确保文档的语义含义得到维护。
  • 内置规范化和停用词删除

选择正确的拆分器​

  • 在以下情况下使用 HTMLHeaderTextSplitter：您需要根据 HTML 文档的标题层次结构拆分文档，并维护有关标题的元数据。
  • 在以下情况下使用 HTMLSectionSplitter：您需要将文档拆分成更大、更通用的部分，可能基于自定义标签或字体大小。
  • 在以下情况下使用 HTMLSemanticPreservingSplitter：您需要将文档拆分成小块，同时保留语义元素（如表格和列表），确保它们不会被拆分并且其上下文得到维护。
功能HTMLHeaderTextSplitterHTMLSectionSplitterHTMLSemanticPreservingSplitter
基于标题拆分是是是
保留语义元素（表格、列表）否否是
为标题添加元数据是是是
HTML 标签的自定义处理程序否否是
保留媒体（图像、视频）否否是
考虑字体大小否是否
使用 XSLT 转换否是否

示例 HTML 文档​

让我们使用以下 HTML 文档作为示例

html_string = """
<!DOCTYPE html>
  <html lang='en'>
  <head>
    <meta charset='UTF-8'>
    <meta name='viewport' content='width=device-width, initial-scale=1.0'>
    <title>Fancy Example HTML Page</title>
  </head>
  <body>
    <h1>Main Title</h1>
    <p>This is an introductory paragraph with some basic content.</p>
    
    <h2>Section 1: Introduction</h2>
    <p>This section introduces the topic. Below is a list:</p>
    <ul>
      <li>First item</li>
      <li>Second item</li>
      <li>Third item with <strong>bold text</strong> and <a href='#'>a link</a></li>
    </ul>
    
    <h3>Subsection 1.1: Details</h3>
    <p>This subsection provides additional details. Here's a table:</p>
    <table border='1'>
      <thead>
        <tr>
          <th>Header 1</th>
          <th>Header 2</th>
          <th>Header 3</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <td>Row 1, Cell 1</td>
          <td>Row 1, Cell 2</td>
          <td>Row 1, Cell 3</td>
        </tr>
        <tr>
          <td>Row 2, Cell 1</td>
          <td>Row 2, Cell 2</td>
          <td>Row 2, Cell 3</td>
        </tr>
      </tbody>
    </table>
    
    <h2>Section 2: Media Content</h2>
    <p>This section contains an image and a video:</p>
      <img src='example_image_link.mp4' alt='Example Image'>
      <video controls width='250' src='example_video_link.mp4' type='video/mp4'>
      Your browser does not support the video tag.
    </video>

    <h2>Section 3: Code Example</h2>
    <p>This section contains a code block:</p>
    <pre><code data-lang="html">
    &lt;div&gt;
      &lt;p&gt;This is a paragraph inside a div.&lt;/p&gt;
    &lt;/div&gt;
    </code></pre>

    <h2>Conclusion</h2>
    <p>This is the conclusion of the document.</p>
  </body>
  </html>
"""

使用 HTMLHeaderTextSplitter​

HTMLHeaderTextSplitter 是一个“结构感知”文本拆分器，它在 HTML 元素级别拆分文本，并为每个“相关”于任何给定块的标题添加元数据。它可以逐元素返回块，或将具有相同元数据的元素组合在一起，目的是 (a) 将相关文本（或多或少）语义地分组，以及 (b) 保留文档结构中编码的上下文丰富的信息。它可以与其他文本拆分器一起用作分块管道的一部分。

它类似于 markdown 文件的 MarkdownHeaderTextSplitter。

要指定要拆分的标题，请在实例化 HTMLHeaderTextSplitter 时指定 headers_to_split_on，如下所示。

from langchain_text_splitters import HTMLHeaderTextSplitter

headers_to_split_on = [
    ("h1", "Header 1"),
    ("h2", "Header 2"),
    ("h3", "Header 3"),
]

html_splitter = HTMLHeaderTextSplitter(headers_to_split_on)
html_header_splits = html_splitter.split_text(html_string)
html_header_splits
[Document(metadata={'Header 1': 'Main Title'}, page_content='This is an introductory paragraph with some basic content.'),
 Document(metadata={'Header 1': 'Main Title', 'Header 2': 'Section 1: Introduction'}, page_content='This section introduces the topic. Below is a list:  \nFirst item Second item Third item with bold text and a link'),
 Document(metadata={'Header 1': 'Main Title', 'Header 2': 'Section 1: Introduction', 'Header 3': 'Subsection 1.1: Details'}, page_content="This subsection provides additional details. Here's a table:"),
 Document(metadata={'Header 1': 'Main Title', 'Header 2': 'Section 2: Media Content'}, page_content='This section contains an image and a video:'),
 Document(metadata={'Header 1': 'Main Title', 'Header 2': 'Section 3: Code Example'}, page_content='This section contains a code block:'),
 Document(metadata={'Header 1': 'Main Title', 'Header 2': 'Conclusion'}, page_content='This is the conclusion of the document.')]

要返回每个元素及其关联的标题，请在实例化 HTMLHeaderTextSplitter 时指定 return_each_element=True

html_splitter = HTMLHeaderTextSplitter(
    headers_to_split_on,
    return_each_element=True,
)
html_header_splits_elements = html_splitter.split_text(html_string)

与上述元素按标题聚合的情况进行比较

for element in html_header_splits[:2]:
    print(element)
page_content='This is an introductory paragraph with some basic content.' metadata={'Header 1': 'Main Title'}
page_content='This section introduces the topic. Below is a list:  
First item Second item Third item with bold text and a link' metadata={'Header 1': 'Main Title', 'Header 2': 'Section 1: Introduction'}

现在，每个元素都作为不同的 Document 返回

for element in html_header_splits_elements[:3]:
    print(element)
page_content='This is an introductory paragraph with some basic content.' metadata={'Header 1': 'Main Title'}
page_content='This section introduces the topic. Below is a list:' metadata={'Header 1': 'Main Title', 'Header 2': 'Section 1: Introduction'}
page_content='First item Second item Third item with bold text and a link' metadata={'Header 1': 'Main Title', 'Header 2': 'Section 1: Introduction'}

如何从 URL 或 HTML 文件拆分：​

要直接从 URL 读取，请将 URL 字符串传递到 split_text_from_url 方法中。

类似地，本地 HTML 文件可以传递给 split_text_from_file 方法。

url = "https://plato.stanford.edu/entries/goedel/"

headers_to_split_on = [
    ("h1", "Header 1"),
    ("h2", "Header 2"),
    ("h3", "Header 3"),
    ("h4", "Header 4"),
]

html_splitter = HTMLHeaderTextSplitter(headers_to_split_on)

# for local file use html_splitter.split_text_from_file(<path_to_file>)
html_header_splits = html_splitter.split_text_from_url(url)

如何约束块大小：​

HTMLHeaderTextSplitter 基于 HTML 标题进行拆分，可以与另一个基于字符长度约束拆分的拆分器（如 RecursiveCharacterTextSplitter）组合使用。

这可以使用第二个拆分器的 .split_documents 方法来完成

from langchain_text_splitters import RecursiveCharacterTextSplitter

chunk_size = 500
chunk_overlap = 30
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=chunk_size, chunk_overlap=chunk_overlap
)

# Split
splits = text_splitter.split_documents(html_header_splits)
splits[80:85]
[Document(metadata={'Header 1': 'Kurt Gödel', 'Header 2': '2. Gödel’s Mathematical Work', 'Header 3': '2.2 The Incompleteness Theorems', 'Header 4': '2.2.1 The First Incompleteness Theorem'}, page_content='We see that Gödel first tried to reduce the consistency problem for analysis to that of arithmetic. This seemed to require a truth definition for arithmetic, which in turn led to paradoxes, such as the Liar paradox (“This sentence is false”) and Berry’s paradox (“The least number not defined by an expression consisting of just fourteen English words”). Gödel then noticed that such paradoxes would not necessarily arise if truth were replaced by provability. But this means that arithmetic truth'),
 Document(metadata={'Header 1': 'Kurt Gödel', 'Header 2': '2. Gödel’s Mathematical Work', 'Header 3': '2.2 The Incompleteness Theorems', 'Header 4': '2.2.1 The First Incompleteness Theorem'}, page_content='means that arithmetic truth and arithmetic provability are not co-extensive — whence the First Incompleteness Theorem.'),
 Document(metadata={'Header 1': 'Kurt Gödel', 'Header 2': '2. Gödel’s Mathematical Work', 'Header 3': '2.2 The Incompleteness Theorems', 'Header 4': '2.2.1 The First Incompleteness Theorem'}, page_content='This account of Gödel’s discovery was told to Hao Wang very much after the fact; but in Gödel’s contemporary correspondence with Bernays and Zermelo, essentially the same description of his path to the theorems is given. (See Gödel 2003a and Gödel 2003b respectively.) From those accounts we see that the undefinability of truth in arithmetic, a result credited to Tarski, was likely obtained in some form by Gödel by 1931. But he neither publicized nor published the result; the biases logicians'),
 Document(metadata={'Header 1': 'Kurt Gödel', 'Header 2': '2. Gödel’s Mathematical Work', 'Header 3': '2.2 The Incompleteness Theorems', 'Header 4': '2.2.1 The First Incompleteness Theorem'}, page_content='result; the biases logicians had expressed at the time concerning the notion of truth, biases which came vehemently to the fore when Tarski announced his results on the undefinability of truth in formal systems 1935, may have served as a deterrent to Gödel’s publication of that theorem.'),
 Document(metadata={'Header 1': 'Kurt Gödel', 'Header 2': '2. Gödel’s Mathematical Work', 'Header 3': '2.2 The Incompleteness Theorems', 'Header 4': '2.2.2 The proof of the First Incompleteness Theorem'}, page_content='We now describe the proof of the two theorems, formulating Gödel’s results in Peano arithmetic. Gödel himself used a system related to that defined in Principia Mathematica, but containing Peano arithmetic. In our presentation of the First and Second Incompleteness Theorems we refer to Peano arithmetic as P, following Gödel’s notation.')]

局限性​

从一个 HTML 文档到另一个 HTML 文档，可能存在相当多的结构变化，虽然 HTMLHeaderTextSplitter 将尝试将所有“相关”标题附加到任何给定块，但有时可能会遗漏某些标题。例如，该算法假设信息层次结构，其中标题始终位于相关文本“上方”的节点，即先前的兄弟节点、祖先节点及其组合。在以下新闻文章中（在撰写本文档时），文档的结构使得顶级标题的文本（标记为“h1”）与我们期望它“上方”的文本元素位于不同的子树中——因此我们可以观察到“h1”元素及其关联的文本没有出现在块元数据中（但是，在适用的情况下，我们确实看到了“h2”及其关联的文本）

url = "https://www.cnn.com/2023/09/25/weather/el-nino-winter-us-climate/index.html"

headers_to_split_on = [
    ("h1", "Header 1"),
    ("h2", "Header 2"),
]

html_splitter = HTMLHeaderTextSplitter(headers_to_split_on)
html_header_splits = html_splitter.split_text_from_url(url)
print(html_header_splits[1].page_content[:500])
No two El Niño winters are the same, but many have temperature and precipitation trends in common.  
Average conditions during an El Niño winter across the continental US.  
One of the major reasons is the position of the jet stream, which often shifts south during an El Niño winter. This shift typically brings wetter and cooler weather to the South while the North becomes drier and warmer, according to NOAA.  
Because the jet stream is essentially a river of air that storms flow through, they c

使用 HTMLSectionSplitter​

与 HTMLHeaderTextSplitter 概念相似，HTMLSectionSplitter 是一个“结构感知”文本拆分器，它在元素级别拆分文本，并为每个“相关”于任何给定块的标题添加元数据。它允许您按部分拆分 HTML。

它可以逐元素返回块，或将具有相同元数据的元素组合在一起，目的是 (a) 将相关文本（或多或少）语义地分组，以及 (b) 保留文档结构中编码的上下文丰富的信息。

使用 xslt_path 提供转换 HTML 的绝对路径，以便它可以根据提供的标签检测部分。默认情况下，使用 data_connection/document_transformers 目录中的 converting_to_header.xslt 文件。这是为了将 html 转换为更易于检测部分的格式/布局。例如，可以根据字体大小将基于 span 的内容转换为标题标签，以便检测为部分。

如何拆分 HTML 字符串：​

from langchain_text_splitters import HTMLSectionSplitter

headers_to_split_on = [
    ("h1", "Header 1"),
    ("h2", "Header 2"),
]

html_splitter = HTMLSectionSplitter(headers_to_split_on)
html_header_splits = html_splitter.split_text(html_string)
html_header_splits
API 参考：HTMLSectionSplitter
[Document(metadata={'Header 1': 'Main Title'}, page_content='Main Title \n This is an introductory paragraph with some basic content.'),
 Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content="Section 1: Introduction \n This section introduces the topic. Below is a list: \n \n First item \n Second item \n Third item with  bold text  and  a link \n \n \n Subsection 1.1: Details \n This subsection provides additional details. Here's a table: \n \n \n \n Header 1 \n Header 2 \n Header 3 \n \n \n \n \n Row 1, Cell 1 \n Row 1, Cell 2 \n Row 1, Cell 3 \n \n \n Row 2, Cell 1 \n Row 2, Cell 2 \n Row 2, Cell 3"),
 Document(metadata={'Header 2': 'Section 2: Media Content'}, page_content='Section 2: Media Content \n This section contains an image and a video: \n \n \n      Your browser does not support the video tag.'),
 Document(metadata={'Header 2': 'Section 3: Code Example'}, page_content='Section 3: Code Example \n This section contains a code block: \n \n    <div>\n      <p>This is a paragraph inside a div.</p>\n    </div>'),
 Document(metadata={'Header 2': 'Conclusion'}, page_content='Conclusion \n This is the conclusion of the document.')]

如何约束块大小：​

HTMLSectionSplitter 可以与其他文本拆分器一起用作分块管道的一部分。在内部，当部分大小大于块大小时，它使用 RecursiveCharacterTextSplitter。它还考虑文本的字体大小，以根据确定的字体大小阈值来确定它是否为部分。

from langchain_text_splitters import RecursiveCharacterTextSplitter

headers_to_split_on = [
    ("h1", "Header 1"),
    ("h2", "Header 2"),
    ("h3", "Header 3"),
]

html_splitter = HTMLSectionSplitter(headers_to_split_on)

html_header_splits = html_splitter.split_text(html_string)

chunk_size = 50
chunk_overlap = 5
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=chunk_size, chunk_overlap=chunk_overlap
)

# Split
splits = text_splitter.split_documents(html_header_splits)
splits
[Document(metadata={'Header 1': 'Main Title'}, page_content='Main Title'),
 Document(metadata={'Header 1': 'Main Title'}, page_content='This is an introductory paragraph with some'),
 Document(metadata={'Header 1': 'Main Title'}, page_content='some basic content.'),
 Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content='Section 1: Introduction'),
 Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content='This section introduces the topic. Below is a'),
 Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content='is a list:'),
 Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content='First item \n Second item'),
 Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content='Third item with  bold text  and  a link'),
 Document(metadata={'Header 3': 'Subsection 1.1: Details'}, page_content='Subsection 1.1: Details'),
 Document(metadata={'Header 3': 'Subsection 1.1: Details'}, page_content='This subsection provides additional details.'),
 Document(metadata={'Header 3': 'Subsection 1.1: Details'}, page_content="Here's a table:"),
 Document(metadata={'Header 3': 'Subsection 1.1: Details'}, page_content='Header 1 \n Header 2 \n Header 3'),
 Document(metadata={'Header 3': 'Subsection 1.1: Details'}, page_content='Row 1, Cell 1 \n Row 1, Cell 2'),
 Document(metadata={'Header 3': 'Subsection 1.1: Details'}, page_content='Row 1, Cell 3 \n \n \n Row 2, Cell 1'),
 Document(metadata={'Header 3': 'Subsection 1.1: Details'}, page_content='Row 2, Cell 2 \n Row 2, Cell 3'),
 Document(metadata={'Header 2': 'Section 2: Media Content'}, page_content='Section 2: Media Content'),
 Document(metadata={'Header 2': 'Section 2: Media Content'}, page_content='This section contains an image and a video:'),
 Document(metadata={'Header 2': 'Section 2: Media Content'}, page_content='Your browser does not support the video'),
 Document(metadata={'Header 2': 'Section 2: Media Content'}, page_content='tag.'),
 Document(metadata={'Header 2': 'Section 3: Code Example'}, page_content='Section 3: Code Example'),
 Document(metadata={'Header 2': 'Section 3: Code Example'}, page_content='This section contains a code block: \n \n    <div>'),
 Document(metadata={'Header 2': 'Section 3: Code Example'}, page_content='<p>This is a paragraph inside a div.</p>'),
 Document(metadata={'Header 2': 'Section 3: Code Example'}, page_content='</div>'),
 Document(metadata={'Header 2': 'Conclusion'}, page_content='Conclusion'),
 Document(metadata={'Header 2': 'Conclusion'}, page_content='This is the conclusion of the document.')]

使用 HTMLSemanticPreservingSplitter​

HTMLSemanticPreservingSplitter 旨在将 HTML 内容拆分成可管理的小块，同时保留重要元素的语义结构，如表格、列表和其他 HTML 组件。这确保了此类元素不会跨块拆分，从而避免了上下文相关性（如表格标题、列表标题等）的丢失。

此拆分器的核心设计目的是创建上下文相关的块。使用 HTMLHeaderTextSplitter 进行通用递归拆分可能会导致表格、列表和其他结构化元素在中间被拆分，从而丢失重要的上下文并创建不良的块。

HTMLSemanticPreservingSplitter 对于拆分包含结构化元素（如表格和列表）的 HTML 内容至关重要，尤其是在保持这些元素的完整性至关重要时。此外，它为特定的 HTML 标签定义自定义处理程序的能力使其成为处理复杂 HTML 文档的多功能工具。

重要提示：max_chunk_size 不是块的明确最大大小，最大大小的计算发生在保留内容不属于块时，以确保它不被拆分。当我们将保留的数据添加回块中时，块大小有可能超过 max_chunk_size。这对于确保我们维护原始文档的结构至关重要

信息

注意

  1. 我们定义了一个自定义处理程序来重新格式化代码块的内容
  2. 我们为特定的 html 元素定义了一个拒绝列表，以在预处理中分解它们及其内容
  3. 我们有意设置了一个小块大小，以演示元素的不拆分性
# BeautifulSoup is required to use the custom handlers
from bs4 import Tag
from langchain_text_splitters import HTMLSemanticPreservingSplitter

headers_to_split_on = [
    ("h1", "Header 1"),
    ("h2", "Header 2"),
]


def code_handler(element: Tag) -> str:
    data_lang = element.get("data-lang")
    code_format = f"<code:{data_lang}>{element.get_text()}</code>"

    return code_format


splitter = HTMLSemanticPreservingSplitter(
    headers_to_split_on=headers_to_split_on,
    separators=["\n\n", "\n", ". ", "! ", "? "],
    max_chunk_size=50,
    preserve_images=True,
    preserve_videos=True,
    elements_to_preserve=["table", "ul", "ol", "code"],
    denylist_tags=["script", "style", "head"],
    custom_handlers={"code": code_handler},
)

documents = splitter.split_text(html_string)
documents
[Document(metadata={'Header 1': 'Main Title'}, page_content='This is an introductory paragraph with some basic content.'),
 Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content='This section introduces the topic'),
 Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content='. Below is a list: First item Second item Third item with bold text and a link Subsection 1.1: Details This subsection provides additional details'),
 Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content=". Here's a table: Header 1 Header 2 Header 3 Row 1, Cell 1 Row 1, Cell 2 Row 1, Cell 3 Row 2, Cell 1 Row 2, Cell 2 Row 2, Cell 3"),
 Document(metadata={'Header 2': 'Section 2: Media Content'}, page_content='This section contains an image and a video: ![image:example_image_link.mp4](example_image_link.mp4) ![video:example_video_link.mp4](example_video_link.mp4)'),
 Document(metadata={'Header 2': 'Section 3: Code Example'}, page_content='This section contains a code block: <code:html> <div> <p>This is a paragraph inside a div.</p> </div> </code>'),
 Document(metadata={'Header 2': 'Conclusion'}, page_content='This is the conclusion of the document.')]

保留表格和列表​

在此示例中，我们将演示 HTMLSemanticPreservingSplitter 如何在 HTML 文档中保留表格和大型列表。块大小将设置为 50 个字符，以说明拆分器如何确保这些元素不会被拆分，即使它们超过了定义的最大块大小。

from langchain_text_splitters import HTMLSemanticPreservingSplitter

html_string = """
<!DOCTYPE html>
<html>
    <body>
        <div>
            <h1>Section 1</h1>
            <p>This section contains an important table and list that should not be split across chunks.</p>
            <table>
                <tr>
                    <th>Item</th>
                    <th>Quantity</th>
                    <th>Price</th>
                </tr>
                <tr>
                    <td>Apples</td>
                    <td>10</td>
                    <td>$1.00</td>
                </tr>
                <tr>
                    <td>Oranges</td>
                    <td>5</td>
                    <td>$0.50</td>
                </tr>
                <tr>
                    <td>Bananas</td>
                    <td>50</td>
                    <td>$1.50</td>
                </tr>
            </table>
            <h2>Subsection 1.1</h2>
            <p>Additional text in subsection 1.1 that is separated from the table and list.</p>
            <p>Here is a detailed list:</p>
            <ul>
                <li>Item 1: Description of item 1, which is quite detailed and important.</li>
                <li>Item 2: Description of item 2, which also contains significant information.</li>
                <li>Item 3: Description of item 3, another item that we don't want to split across chunks.</li>
            </ul>
        </div>
    </body>
</html>
"""

headers_to_split_on = [("h1", "Header 1"), ("h2", "Header 2")]

splitter = HTMLSemanticPreservingSplitter(
    headers_to_split_on=headers_to_split_on,
    max_chunk_size=50,
    elements_to_preserve=["table", "ul"],
)

documents = splitter.split_text(html_string)
print(documents)
[Document(metadata={'Header 1': 'Section 1'}, page_content='This section contains an important table and list'), Document(metadata={'Header 1': 'Section 1'}, page_content='that should not be split across chunks.'), Document(metadata={'Header 1': 'Section 1'}, page_content='Item Quantity Price Apples 10 $1.00 Oranges 5 $0.50 Bananas 50 $1.50'), Document(metadata={'Header 2': 'Subsection 1.1'}, page_content='Additional text in subsection 1.1 that is'), Document(metadata={'Header 2': 'Subsection 1.1'}, page_content='separated from the table and list. Here is a'), Document(metadata={'Header 2': 'Subsection 1.1'}, page_content="detailed list: Item 1: Description of item 1, which is quite detailed and important. Item 2: Description of item 2, which also contains significant information. Item 3: Description of item 3, another item that we don't want to split across chunks.")]

解释​

在此示例中，HTMLSemanticPreservingSplitter 确保整个表格和无序列表 (<ul>) 保留在它们各自的块中。即使块大小设置为 50 个字符，拆分器也认识到这些元素不应拆分并保持完整。

当处理数据表或列表时，这尤其重要，因为拆分内容可能会导致上下文丢失或混淆。生成的 Document 对象保留了这些元素的完整结构，确保了信息的上下文相关性得到维护。

使用自定义处理程序​

HTMLSemanticPreservingSplitter 允许您为特定的 HTML 元素定义自定义处理程序。某些平台具有 BeautifulSoup 本身无法解析的自定义 HTML 标签，发生这种情况时，您可以利用自定义处理程序轻松添加格式化逻辑。

这对于需要特殊处理的元素（如 <iframe> 标签或特定的“data-”元素）尤其有用。在此示例中，我们将为 iframe 标签创建一个自定义处理程序，将其转换为类似 Markdown 的链接。

def custom_iframe_extractor(iframe_tag):
    iframe_src = iframe_tag.get("src", "")
    return f"[iframe:{iframe_src}]({iframe_src})"


splitter = HTMLSemanticPreservingSplitter(
    headers_to_split_on=headers_to_split_on,
    max_chunk_size=50,
    separators=["\n\n", "\n", ". "],
    elements_to_preserve=["table", "ul", "ol"],
    custom_handlers={"iframe": custom_iframe_extractor},
)

html_string = """
<!DOCTYPE html>
<html>
    <body>
        <div>
            <h1>Section with Iframe</h1>
            <iframe src="https://example.com/embed"></iframe>
            <p>Some text after the iframe.</p>
            <ul>
                <li>Item 1: Description of item 1, which is quite detailed and important.</li>
                <li>Item 2: Description of item 2, which also contains significant information.</li>
                <li>Item 3: Description of item 3, another item that we don't want to split across chunks.</li>
            </ul>
        </div>
    </body>
</html>
"""

documents = splitter.split_text(html_string)
print(documents)
[Document(metadata={'Header 1': 'Section with Iframe'}, page_content='[iframe:https://example.com/embed](https://example.com/embed) Some text after the iframe'), Document(metadata={'Header 1': 'Section with Iframe'}, page_content=". Item 1: Description of item 1, which is quite detailed and important. Item 2: Description of item 2, which also contains significant information. Item 3: Description of item 3, another item that we don't want to split across chunks.")]

解释​

在此示例中，我们为 iframe 标签定义了一个自定义处理程序，将其转换为类似 Markdown 的链接。当拆分器处理 HTML 内容时，它使用此自定义处理程序来转换 iframe 标签，同时保留表格和列表等其他元素。生成的 Document 对象显示了如何根据您提供的自定义逻辑处理 iframe。

重要提示：在保留链接等项目时，您应注意不要在分隔符中包含 .，或将分隔符留空。RecursiveCharacterTextSplitter 在句点处拆分，这会将链接切成两半。确保您提供包含 . 的分隔符列表。

使用自定义处理程序通过 LLM 分析图像​

通过自定义处理程序，我们还可以覆盖任何元素的默认处理。一个很好的例子是在文档中图像的语义分析直接插入到分块流程中。

由于我们的函数在发现标签时被调用，我们可以覆盖 <img> 标签并关闭 preserve_images 以插入我们想要嵌入到块中的任何内容。

"""This example assumes you have helper methods `load_image_from_url` and an LLM agent `llm` that can process image data."""

from langchain.agents import AgentExecutor

# This example needs to be replaced with your own agent
llm = AgentExecutor(...)


# This method is a placeholder for loading image data from a URL and is not implemented here
def load_image_from_url(image_url: str) -> bytes:
    # Assuming this method fetches the image data from the URL
    return b"image_data"


html_string = """
<!DOCTYPE html>
<html>
    <body>
        <div>
            <h1>Section with Image and Link</h1>
            <p>
                <img src="https://example.com/image.jpg" alt="An example image" />
                Some text after the image.
            </p>
            <ul>
                <li>Item 1: Description of item 1, which is quite detailed and important.</li>
                <li>Item 2: Description of item 2, which also contains significant information.</li>
                <li>Item 3: Description of item 3, another item that we don't want to split across chunks.</li>
            </ul>
        </div>
    </body>
</html>
"""


def custom_image_handler(img_tag) -> str:
    img_src = img_tag.get("src", "")
    img_alt = img_tag.get("alt", "No alt text provided")

    image_data = load_image_from_url(img_src)
    semantic_meaning = llm.invoke(image_data)

    markdown_text = f"[Image Alt Text: {img_alt} | Image Source: {img_src} | Image Semantic Meaning: {semantic_meaning}]"

    return markdown_text


splitter = HTMLSemanticPreservingSplitter(
    headers_to_split_on=headers_to_split_on,
    max_chunk_size=50,
    separators=["\n\n", "\n", ". "],
    elements_to_preserve=["ul"],
    preserve_images=False,
    custom_handlers={"img": custom_image_handler},
)

documents = splitter.split_text(html_string)

print(documents)
API 参考：AgentExecutor
[Document(metadata={'Header 1': 'Section with Image and Link'}, page_content='[Image Alt Text: An example image | Image Source: https://example.com/image.jpg | Image Semantic Meaning: semantic-meaning] Some text after the image'), 
Document(metadata={'Header 1': 'Section with Image and Link'}, page_content=". Item 1: Description of item 1, which is quite detailed and important. Item 2: Description of item 2, which also contains significant information. Item 3: Description of item 3, another item that we don't want to split across chunks.")]

解释：​

通过我们编写的自定义处理程序来提取 HTML 中 <img> 元素中的特定字段，我们可以使用我们的 agent 进一步处理数据，并将结果直接插入到我们的块中。重要的是要确保 preserve_images 设置为 False，否则将进行 <img> 字段的默认处理。

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