跳到主要内容
将 HTML 文档分割成可管理的小块对于各种文本处理任务至关重要，例如自然语言处理、搜索索引等。本指南将探讨 LangChain 提供的三种不同的文本分割器，您可以使用它们有效地分割 HTML 内容。 这些分割器中的每一个都具有独特的功能和用例。本指南将帮助您了解它们之间的差异，为什么您可能会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

​
分割器概述

​
HTMLHeaderTextSplitter

当您希望根据文档的标题保留其分层结构时很有用。
描述：根据标题标签（例如，<h1>、<h2>、<h3> 等）分割 HTML 文本，并为与任何给定块相关的每个标题添加元数据。 功能：
  • 在 HTML 元素级别分割文本。
  • 保留文档结构中编码的富含上下文的信息。
  • 可以逐个元素返回块，或将具有相同元数据的元素组合在一起。

​
HTMLSectionSplitter

当您希望将 HTML 文档分割成更大的部分时很有用，例如 <section>、<div> 或自定义定义的部分。
描述：与 HTMLHeaderTextSplitter 类似，但侧重于根据指定标签将 HTML 分割成多个部分。 功能：
  • 使用 XSLT 转换来检测和分割部分。
  • 内部对大段内容使用 RecursiveCharacterTextSplitter。
  • 考虑字体大小来确定部分。

​
HTMLSemanticPreservingSplitter

**当您需要确保结构化元素不会跨块分割，从而保留上下文相关性时，这是理想选择。**
描述：将 HTML 内容分割成可管理的小块，同时保留表格、列表和其他 HTML 组件等重要元素的语义结构。 功能：
  • 保留表格、列表和其他指定的 HTML 元素。
  • 允许为特定的 HTML 标签使用自定义处理器。
  • 确保文档的语义含义得以维护。
  • 内置归一化和停用词去除

​
选择合适的分割器

  • 在以下情况下使用 HTMLHeaderTextSplitter：您需要根据 HTML 文档的标题层级分割它，并维护有关标题的元数据。
  • 在以下情况下使用 HTMLSectionSplitter：您需要将文档分割成更大、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：您需要将文档分割成小块，同时保留表格和列表等语义元素，确保它们不被分割并保持其上下文。
功能HTMLHeaderTextSplitterHTMLSectionSplitterHTMLSemanticPreservingSplitter
根据标题分割是是是
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 标签的自定义处理器否否是
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

​
HTML 文档示例

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”文本分割器，它在 HTML 元素级别分割文本，并为任何给定块的“相关”标题添加元数据。它可以逐个元素返回块，也可以将具有相同元数据的元素组合起来，目标是 (a) 将相关文本（或多或少）语义分组，以及 (b) 保留文档结构中编码的富含上下文的信息。它可以与其他文本分割器一起作为分块管道的一部分使用。 它类似于 Markdown 文件的MarkdownHeaderTextSplitter。 要指定要分割的标题，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)
与上面按标题聚合元素的比较 
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一个独立的 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 文件分割

要直接从 URL 读取，请将 URL 字符串传递给 split_text_from_url 方法。 同样，可以将本地 HTML 文件传递给 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)

​
如何限制块大小

HTMLHeaderTextSplitter 根据 HTML 标题进行分割，可以与另一个根据字符长度限制分割的分割器（例如 RecursiveCharacterTextSplitter）组合使用。 这可以通过第二个分割器的 .split_documents 方法完成：
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.')]

​
限制

不同的 HTML 文档在结构上可能存在很大差异，虽然 HTMLHeaderTextSplitter 会尝试将所有“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，该算法假设一个信息层次结构，其中标题始终位于相关文本“上方”的节点，即先前的兄弟元素、祖先元素及其组合。在下面的新闻文章中（截至本文档撰写时），文档的结构使得顶级标题的文本虽然被标记为“h1”，但与我们期望它“上方”的文本元素位于一个*不同的*子树中——所以我们可以观察到“h1”元素及其相关文本没有出现在块元数据中（但，在适用情况下，我们确实看到了“h2”及其相关文本） 
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

HTMLSectionSplitter 在概念上类似于 HTMLHeaderTextSplitter，它是一个“结构感知”文本分割器，它在元素级别分割文本，并为任何给定块的“相关”标题添加元数据。它允许您按部分分割 HTML。 它可以逐个元素返回块，也可以将具有相同元数据的元素组合起来，目标是 (a) 将相关文本（或多或少）语义分组，以及 (b) 保留文档结构中编码的富含上下文的信息。 使用 xslt_path 提供一个绝对路径来转换 HTML，以便它可以根据提供的标签检测部分。默认是使用 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，根据字体大小的 span 可以转换为标题标签以被检测为部分。

​
如何分割 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

[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.')]

​
如何限制块大小

HTMLSectionSplitter 可以与其他文本分割器一起作为分块管道的一部分使用。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判断它是否是一个部分。 
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 内容分割成可管理的小块，同时保留表格、列表和其他 HTML 组件等重要元素的语义结构。这确保了此类元素不会跨块分割，从而导致上下文相关性丢失，例如表头、列表头等。 这个分割器从根本上讲是为了创建上下文相关的块。使用 HTMLHeaderTextSplitter 进行通用递归分割可能会导致表格、列表和其他结构化元素在中间被分割，从而丢失重要的上下文并创建糟糕的块。 HTMLSemanticPreservingSplitter 对于分割包含表格和列表等结构化元素的 HTML 内容至关重要，尤其是在保持这些元素完整性至关重要时。此外，其为特定 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 并非块的绝对最大大小，最大大小的计算发生在保留内容不属于块时，以确保它不会被分割。当我们再次将保留数据添加回块时，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。
备注
  1. 我们定义了一个自定义处理器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，以在预处理之前分解它们及其内容
  3. 我们特意设置了一个小的块大小，以演示元素不被分割
# 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，以说明分割器如何确保这些元素不被分割，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>）在其各自的块中得到保留。即使块大小设置为 50 个字符，分割器也会识别这些元素不应被分割并保持其完整性。 这在处理数据表或列表时尤其重要，因为分割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以维持。

​
使用自定义处理器

HTMLSemanticPreservingSplitter 允许您为特定的 HTML 元素定义自定义处理器。某些平台具有 BeautifulSoup 无法原生解析的自定义 HTML 标签，在这种情况下，您可以使用自定义处理器轻松添加格式化逻辑。 这对于需要特殊处理的元素（例如 <iframe> 标签或特定的“data-”元素）特别有用。在此示例中，我们将为 iframe 标签创建一个自定义处理器，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器，将其转换为类似 Markdown 的链接。当分割器处理 HTML 内容时，它使用此自定义处理器来转换 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 iframe 如何根据您提供的自定义逻辑进行处理。 重要提示：在保留链接等项目时，您应该注意不要在分隔符中包含 .，或将分隔符留空。RecursiveCharacterTextSplitter 会在句号处分割，这将导致链接被截断。请确保提供一个包含 . 的分隔符列表。

​
使用自定义处理器通过 LLM 分析图像

通过自定义处理器，我们还可以覆盖任何元素的默认处理。一个很好的例子是，在分块流程中直接插入文档中图像的语义分析。 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 以插入我们希望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)

[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

编写自定义处理器以从 HTML 中的 <img> 元素中提取特定字段后，我们可以进一步使用我们的代理处理数据，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，否则将执行 <img> 字段的默认处理。
在 GitHub 上编辑此页面源文件。
以编程方式连接这些文档到 Claude、VSCode 等，通过 MCP 获取实时答案。
© . This site is unofficial and not affiliated with LangChain, Inc.