代码文本分割器集成指南

支持的语言存储在 langchaintextsplitters.Language 枚举中。它们包括：

@[RecursiveCharacterTextSplitter] 包含了适用于 分割文本 的特定编程语言的预构建分隔符列表。

:::python 支持的语言存储在 langchain_text_splitters.Language 枚举中。它们包括：

"cpp",
"go",
"java",
"kotlin",
"js",
"ts",
"php",
"proto",
"python",
"rst",
"ruby",
"rust",
"scala",
"swift",
"markdown",
"latex",
"html",
"sol",
"csharp",
"cobol",
"c",
"lua",
"perl",
"haskell"

要查看给定语言的分隔符列表，将此枚举中的一个值传递给

RecursiveCharacterTextSplitter.get_separators_for_language

要实例化针对特定语言定制的分割器，将此枚举中的一个值传递给

RecursiveCharacterTextSplitter.from_language

:::

:::js 支持的语言保存在 SupportedTextSplitterLanguages 类型中。它们包括：

"cpp",
"go",
"java",
"js",
"php",
"proto",
"python",
"rst",
"ruby",
"rust",
"scala",
"swift",
"markdown",
"latex",
"html",
"sol",

要查看给定语言的分隔符列表，将此枚举中的一个值传递给

RecursiveCharacterTextSplitter.getSeparatorsForLanguage()

要实例化针对特定语言定制的分割器，将此枚举中的一个值传递给

RecursiveCharacterTextSplitter.fromLanguage()

:::

下面我们展示了各种语言的示例。

:::python

pip install -qU langchain-text-splitters

::: :::js

npm

npm install @langchain/textsplitters

:::

:::python

from langchain_text_splitters import (
    Language,
    RecursiveCharacterTextSplitter,
)

要查看支持的语言完整列表：

[e.value for e in Language]

['cpp',
 'go',
 'java',
 'kotlin',
 'js',
 'ts',
 'php',
 'proto',
 'python',
 'rst',
 'ruby',
 'rust',
 'scala',
 'swift',
 'markdown',
 'latex',
 'html',
 'sol',
 'csharp',
 'cobol',
 'c',
 'lua',
 'perl',
 'haskell',
 'elixir',
 'powershell',
 'visualbasic6']

您还可以查看给定语言使用的分隔符：

RecursiveCharacterTextSplitter.get_separators_for_language(Language.PYTHON)

['\nclass ', '\ndef ', '\n\tdef ', '\n\n', '\n', ' ', '']

:::

Python

:::python

这里是一个使用 PythonTextSplitter 的示例：

PYTHON_CODE = """
def hello_world():
    print("Hello, World!")

# Call the function
hello_world()
"""
python_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.PYTHON, chunk_size=50, chunk_overlap=0
)
python_docs = python_splitter.create_documents([PYTHON_CODE])
python_docs

[Document(metadata={}, page_content='def hello_world():\n    print("Hello, World!")'),
 Document(metadata={}, page_content='# Call the function\nhello_world()')]

::: :::js 这里是一个使用 python 文本分割器的示例：

const pythonSplitter = RecursiveCharacterTextSplitter.fromLanguage(
    "python",
    { chunkSize: 50, chunkOverlap: 0 }
);
const pythonDocs = pythonSplitter.createDocuments([{ pageContent: PYTHON_CODE }]);
console.log(pythonDocs);

[
    Document { metadata: {}, pageContent: 'def hello_world():\n    print("Hello, World!")' },
    Document { metadata: {}, pageContent: '# Call the function\nhello_world()' }
]

:::

JS

:::python 这里是一个使用 JS 文本分割器的示例：

JS_CODE = """
function helloWorld() {
  console.log("Hello, World!");
}

// Call the function
helloWorld();
"""

js_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.JS, chunk_size=60, chunk_overlap=0
)
js_docs = js_splitter.create_documents([JS_CODE])
js_docs

[Document(metadata={}, page_content='function helloWorld() {\n  console.log("Hello, World!");\n}'),
 Document(metadata={}, page_content='// Call the function\nhelloWorld();')]

::: :::js 这里是一个使用 JS 文本分割器的示例：

const JS_CODE = `
function helloWorld() {
  console.log("Hello, World!");
}

// Call the function
helloWorld();
`;

const jsSplitter = RecursiveCharacterTextSplitter.fromLanguage(
    "js",
    { chunkSize: 60, chunkOverlap: 0 }
);
const jsDocs = jsSplitter.createDocuments([{ pageContent: JS_CODE }]);
console.log(jsDocs);

[
    Document { metadata: {}, pageContent: 'function helloWorld() {\n  console.log("Hello, World!");\n}' },
    Document { metadata: {}, pageContent: '// Call the function\nhelloWorld()' }
]

:::

TS

:::python 这里是一个使用 typescript 文本分割器的示例：

TS_CODE = """
function helloWorld(): void {
  console.log("Hello, World!");
}

// Call the function
helloWorld();
"""

ts_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.TS, chunk_size=60, chunk_overlap=0
)
ts_docs = ts_splitter.create_documents([TS_CODE])
ts_docs

[Document(metadata={}, page_content='function helloWorld(): void {'),
 Document(metadata={}, page_content='console.log("Hello, World!");\n}'),
 Document(metadata={}, page_content='// Call the function\nhelloWorld();')]

::: :::js 这里是一个使用 typescript 文本分割器的示例：

const TS_CODE = `
function helloWorld(): void {
  console.log("Hello, World!");
}

// Call the function
helloWorld();
`;

const tsSplitter = RecursiveCharacterTextSplitter.fromLanguage(
    "ts",
    { chunkSize: 60, chunkOverlap: 0 }
);
const tsDocs = tsSplitter.createDocuments([{ pageContent: TS_CODE }]);
console.log(tsDocs);

[
    Document { metadata: {}, pageContent: 'function helloWorld(): void {' },
    Document { metadata: {}, pageContent: 'console.log("Hello, World!");\n}' },
    Document { metadata: {}, pageContent: '// Call the function\nhelloWorld()' }
]

:::

Markdown

:::python 这里是一个使用 Markdown 文本分割器的示例：

markdown_text = """
# 🦜️🔗 LangChain

⚡ Building applications with LLMs through composability ⚡

## What is LangChain?

# Hopefully this code block isn't split
LangChain is a framework for...

As an open-source project in a rapidly developing field, we are extremely open to contributions.
"""

md_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.MARKDOWN, chunk_size=60, chunk_overlap=0
)
md_docs = md_splitter.create_documents([markdown_text])
md_docs

[Document(metadata={}, page_content='# 🦜️🔗 LangChain'),
 Document(metadata={}, page_content='⚡ Building applications with LLMs through composability ⚡'),
 Document(metadata={}, page_content='## What is LangChain?'),
 Document(metadata={}, page_content="# Hopefully this code block isn't split"),
 Document(metadata={}, page_content='LangChain is a framework for...'),
 Document(metadata={}, page_content='As an open-source project in a rapidly developing field, we'),
 Document(metadata={}, page_content='are extremely open to contributions.')]

::: :::js 这里是一个使用 Markdown 文本分割器的示例：

const markdownText = `
# 🦜️🔗 LangChain

⚡ Building applications with LLMs through composability ⚡

## What is LangChain?

# Hopefully this code block isn't split
LangChain is a framework for...

As an open-source project in a rapidly developing field, we are extremely open to contributions.
`;

const mdSplitter = RecursiveCharacterTextSplitter.fromLanguage(
    "markdown",
    { chunkSize: 60, chunkOverlap: 0 }
);
const mdDocs = mdSplitter.createDocuments([ markdownText ]);
console.log(mdDocs);

[
    Document { metadata: {}, pageContent: '# 🦜️🔗 LangChain' },
    Document { metadata: {}, pageContent: '⚡ Building applications with LLMs through composability ⚡' },
    Document { metadata: {}, pageContent: '## What is LangChain?' },
    Document { metadata: {}, pageContent: "# Hopefully this code block isn't split" },
    Document { metadata: {}, pageContent: 'LangChain is a framework for...' },
    Document { metadata: {}, pageContent: 'As an open-source project in a rapidly developing field, we' },
    Document { metadata: {}, pageContent: 'are extremely open to contributions.' }
]

:::

Latex

:::python 这里是一个关于 Latex 文本的示例：

latex_text = """
\documentclass{article}

\begin{document}

\maketitle

\section{Introduction}
Large language models (LLMs) are a type of machine learning model that can be trained on vast amounts of text data to generate human-like language. In recent years, LLMs have made significant advances in a variety of natural language processing tasks, including language translation, text generation, and sentiment analysis.

\subsection{History of LLMs}
The earliest LLMs were developed in the 1980s and 1990s, but they were limited by the amount of data that could be processed and the computational power available at the time. In the past decade, however, advances in hardware and software have made it possible to train LLMs on massive datasets, leading to significant improvements in performance.

\subsection{Applications of LLMs}
LLMs have many applications in industry, including chatbots, content creation, and virtual assistants. They can also be used in academia for research in linguistics, psychology, and computational linguistics.

\end{document}
"""

latex_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.MARKDOWN, chunk_size=60, chunk_overlap=0
)
latex_docs = latex_splitter.create_documents([latex_text])
latex_docs

[Document(metadata={}, page_content='\\documentclass{article}\n\n\x08egin{document}\n\n\\maketitle'),
 Document(metadata={}, page_content='\\section{Introduction}'),
 Document(metadata={}, page_content='Large language models (LLMs) are a type of machine learning'),
 Document(metadata={}, page_content='model that can be trained on vast amounts of text data to'),
 Document(metadata={}, page_content='generate human-like language. In recent years, LLMs have'),
 Document(metadata={}, page_content='made significant advances in a variety of natural language'),
 Document(metadata={}, page_content='processing tasks, including language translation, text'),
 Document(metadata={}, page_content='generation, and sentiment analysis.'),
 Document(metadata={}, page_content='\\subsection{History of LLMs}'),
 Document(metadata={}, page_content='The earliest LLMs were developed in the 1980s and 1990s,'),
 Document(metadata={}, page_content='but they were limited by the amount of data that could be'),
 Document(metadata={}, page_content='processed and the computational power available at the'),
 Document(metadata={}, page_content='time. In the past decade, however, advances in hardware and'),
 Document(metadata={}, page_content='software have made it possible to train LLMs on massive'),
 Document(metadata={}, page_content='datasets, leading to significant improvements in'),
 Document(metadata={}, page_content='performance.'),
 Document(metadata={}, page_content='\\subsection{Applications of LLMs}'),
 Document(metadata={}, page_content='LLMs have many applications in industry, including'),
 Document(metadata={}, page_content='chatbots, content creation, and virtual assistants. They'),
 Document(metadata={}, page_content='can also be used in academia for research in linguistics,'),
 Document(metadata={}, page_content='psychology, and computational linguistics.'),
 Document(metadata={}, page_content='\\end{document}')]

::: :::js 这里是一个关于 Latex 文本的示例：

const latexText = `
\\documentclass{article}

\\begin{document}

\\maketitle

\\section{Introduction}
Large language models (LLMs) are a type of machine learning model that can be trained on vast amounts of text data to generate human-like language. In recent years, LLMs have made significant advances in a variety of natural language processing tasks, including language translation, text generation, and sentiment analysis.

\\subsection{History of LLMs}
The earliest LLMs were developed in the 1980s and 1990s, but they were limited by the amount of data that could be processed and the computational power available at the time. In the past decade, however, advances in hardware and software have made it possible to train LLMs on massive datasets, leading to significant improvements in performance.

\\subsection{Applications of LLMs}
LLMs have many applications in industry, including chatbots, content creation, and virtual assistants. They can also be used in academia for research in linguistics, psychology, and computational linguistics.

\\end{document}
`;

const latexSplitter = RecursiveCharacterTextSplitter.fromLanguage(
    "latex",
    { chunkSize: 60, chunkOverlap: 0 }
);
const latexDocs = latexSplitter.createDocuments([{ pageContent: latexText }]);
console.log(latexDocs);

[
    Document { metadata: {}, pageContent: '\\documentclass{article}\n\n\\begin{document}\n\n\\maketitle' },
    Document { metadata: {}, pageContent: '\\section{Introduction}' },
    Document { metadata: {}, pageContent: 'Large language models (LLMs) are a type of machine learning' },
    Document { metadata: {}, pageContent: 'model that can be trained on vast amounts of text data to' },
    Document { metadata: {}, pageContent: 'generate human-like language. In recent years, LLMs have' },
    Document { metadata: {}, pageContent: 'made significant advances in a variety of natural language' },
    Document { metadata: {}, pageContent: 'processing tasks, including language translation, text' },
    Document { metadata: {}, pageContent: 'generation, and sentiment analysis.' },
    Document { metadata: {}, pageContent: '\\subsection{History of LLMs}' },
    Document { metadata: {}, pageContent: 'The earliest LLMs were developed in the 1980s and 1990s,' },
    Document { metadata: {}, pageContent: 'but they were limited by the amount of data that could be' },
    Document { metadata: {}, pageContent: 'processed and the computational power available at the' },
    Document { metadata: {}, pageContent: 'time. In the past decade, however, advances in hardware and' },
    Document { metadata: {}, pageContent: 'software have made it possible to train LLMs on massive' },
    Document { metadata: {}, pageContent: 'datasets, leading to significant improvements in' },
    Document { metadata: {}, pageContent: 'performance.' },
    Document { metadata: {}, pageContent: '\\subsection{Applications of LLMs}' },
    Document { metadata: {}, pageContent: 'LLMs have many applications in industry, including' },
    Document { metadata: {}, pageContent: 'chatbots, content creation, and virtual assistants. They' },
    Document { metadata: {}, pageContent: 'can also be used in academia for research in linguistics,' },
    Document { metadata: {}, pageContent: 'psychology, and computational linguistics.' },
    Document { metadata: {}, pageContent: '\\end{document}' }
]

:::

HTML

:::python 这里是一个使用 HTML 文本分割器的示例：

html_text = """
<!DOCTYPE html>
<html>
    <head>
        <title>🦜️🔗 LangChain</title>
        <style>
            body {
                font-family: Arial, sans-serif;
            }
            h1 {
                color: darkblue;
            }
        </style>
    </head>
    <body>
        <div>
            <h1>🦜️🔗 LangChain</h1>
            <p>⚡ Building applications with LLMs through composability ⚡</p>
        </div>
        <div>
            As an open-source project in a rapidly developing field, we are extremely open to contributions.
        </div>
    </body>
</html>
"""

html_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.HTML, chunk_size=60, chunk_overlap=0
)
html_docs = html_splitter.create_documents([html_text])
html_docs

[Document(metadata={}, page_content='<!DOCTYPE html>\n<html>'),
 Document(metadata={}, page_content='<head>\n        <title>🦜️🔗 LangChain</title>'),
 Document(metadata={}, page_content='<style>\n            body {\n                font-family: Aria'),
 Document(metadata={}, page_content='l, sans-serif;\n            }\n            h1 {'),
 Document(metadata={}, page_content='color: darkblue;\n            }\n        </style>\n    </head'),
 Document(metadata={}, page_content='>'),
 Document(metadata={}, page_content='<body>'),
 Document(metadata={}, page_content='<div>\n            <h1>🦜️🔗 LangChain</h1>'),
 Document(metadata={}, page_content='<p>⚡ Building applications with LLMs through composability ⚡'),
 Document(metadata={}, page_content='</p>\n        </div>'),
 Document(metadata={}, page_content='<div>\n            As an open-source project in a rapidly dev'),
 Document(metadata={}, page_content='eloping field, we are extremely open to contributions.'),
 Document(metadata={}, page_content='</div>\n    </body>\n</html>')]

::: :::js 这里是一个使用 HTML 文本分割器的示例：

const htmlText = `
<!DOCTYPE html>
<html>
    <head>
        <title>🦜️🔗 LangChain</title>
        <style>
            body {
                font-family: Arial, sans-serif;
            }
            h1 {
                color: darkblue;
            }
        </style>
    </head>
    <body>
        <div>
            <h1>🦜️🔗 LangChain</h1>
            <p>⚡ Building applications with LLMs through composability ⚡</p>
        </div>
        <div>
            As an open-source project in a rapidly developing field, we are extremely open to contributions.
        </div>
    </body>
</html>
`;

const htmlSplitter = RecursiveCharacterTextSplitter.fromLanguage(
    "html",
    { chunkSize: 60, chunkOverlap: 0 }
);
const htmlDocs = htmlSplitter.createDocuments([{ pageContent: htmlText }]);
console.log(htmlDocs);

[
    Document { metadata: {}, pageContent: '<!DOCTYPE html>\n<html>' },
    Document { metadata: {}, pageContent: '<head>\n        <title>🦜️🔗 LangChain</title>' },
    Document { metadata: {}, pageContent: '<style>\n            body {\n                font-family: Aria' },
    Document { metadata: {}, pageContent: 'l, sans-serif;\n            }\n            h1 {' },
    Document { metadata: {}, pageContent: 'color: darkblue;\n            }\n        </style>\n    </head' },
    Document { metadata: {}, pageContent: '>' },
    Document { metadata: {}, pageContent: '<body>' },
    Document { metadata: {}, pageContent: '<div>\n            <h1>🦜️🔗 LangChain</h1>' },
    Document { metadata: {}, pageContent: '<p>⚡ Building applications with LLMs through composability ⚡' },
    Document { metadata: {}, pageContent: '</p>\n        </div>' },
    Document { metadata: {}, pageContent: '<div>\n            As an open-source project in a rapidly dev' },
    Document { metadata: {}, pageContent: 'eloping field, we are extremely open to contributions.' },
    Document { metadata: {}, pageContent: '</div>\n    </body>\n</html>' }
]

:::

Solidity

:::python 这里是一个使用 Solidity 文本分割器的示例：

SOL_CODE = """
pragma solidity ^0.8.20;
contract HelloWorld {
   function add(uint a, uint b) pure public returns(uint) {
       return a + b;
   }
}
"""

sol_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.SOL, chunk_size=128, chunk_overlap=0
)
sol_docs = sol_splitter.create_documents([SOL_CODE])
sol_docs

[Document(metadata={}, page_content='pragma solidity ^0.8.20;'),
 Document(metadata={}, page_content='contract HelloWorld {\n   function add(uint a, uint b) pure public returns(uint) {\n       return a + b;\n   }\n}')]

::: :::js 这里是一个使用 Solidity 文本分割器的示例：

const SOL_CODE = `
pragma solidity ^0.8.20;
contract HelloWorld {
   function add(uint a, uint b) pure public returns(uint) {
       return a + b;
   }
}
`;

const solSplitter = RecursiveCharacterTextSplitter.fromLanguage(
    "sol",
    { chunkSize: 128, chunkOverlap: 0 }
);
const solDocs = solSplitter.createDocuments([{ pageContent: SOL_CODE }]);
console.log(solDocs);

[
    Document { metadata: {}, pageContent: 'pragma solidity ^0.8.20;' },
    Document { metadata: {}, pageContent: 'contract HelloWorld {\n   function add(uint a, uint b) pure public returns(uint) {\n       return a + b;\n   }\n}' }
]

:::

C#

:::python 这里是一个使用 C# 文本分割器的示例：

C_CODE = """
using System;
class Program
{
    static void Main()
    {
        int age = 30; // Change the age value as needed

        // Categorize the age without any console output
        if (age < 18)
        {
            // Age is under 18
        }
        else if (age >= 18 && age < 65)
        {
            // Age is an adult
        }
        else
        {
            // Age is a senior citizen
        }
    }
}
"""
c_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.CSHARP, chunk_size=128, chunk_overlap=0
)
c_docs = c_splitter.create_documents([C_CODE])
c_docs

[Document(metadata={}, page_content='using System;'),
 Document(metadata={}, page_content='class Program\n{\n    static void Main()\n    {\n        int age = 30; // Change the age value as needed'),
 Document(metadata={}, page_content='// Categorize the age without any console output\n        if (age < 18)\n        {\n            // Age is under 18'),
 Document(metadata={}, page_content='}\n        else if (age >= 18 && age < 65)\n        {\n            // Age is an adult\n        }\n        else\n        {'),
 Document(metadata={}, page_content='// Age is a senior citizen\n        }\n    }\n}')]

::: :::js 这里是一个使用 C# 文本分割器的示例：

const C_CODE = `
using System;
class Program
{
    static void Main()
    {
        int age = 30; // Change the age value as needed

        // Categorize the age without any console output
        if (age < 18)
        {
            // Age is under 18
        }
        else if (age >= 18 && age < 65)
        {
            // Age is an adult
        }
        else
        {
            // Age is a senior citizen
        }
    }
}
`;

const csharpSplitter = RecursiveCharacterTextSplitter.fromLanguage(
    "csharp",
    { chunkSize: 128, chunkOverlap: 0 }
);
const csharpDocs = csharpSplitter.createDocuments([{ pageContent: C_CODE }]);
console.log(csharpDocs);

[
    Document { metadata: {}, pageContent: 'using System;' },
    Document { metadata: {}, pageContent: 'class Program\n{\n    static void Main()\n    {\n        int age = 30; // Change the age value as needed' },
    Document { metadata: {}, pageContent: '// Categorize the age without any console output\n        if (age < 18)\n        {\n            // Age is under 18' },
    Document { metadata: {}, pageContent: '}\n        else if (age >= 18 && age < 65)\n        {\n            // Age is an adult\n        }\n        else\n        {' },
    Document { metadata: {}, pageContent: '// Age is a senior citizen\n        }\n    }\n}' }
]

:::

Haskell

:::python 这里是一个使用 Haskell 文本分割器的示例：

HASKELL_CODE = """
main :: IO ()
main = do
    putStrLn "Hello, World!"
-- Some sample functions
add :: Int -> Int -> Int
add x y = x + y
"""
haskell_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.HASKELL, chunk_size=50, chunk_overlap=0
)
haskell_docs = haskell_splitter.create_documents([HASKELL_CODE])
haskell_docs

[Document(metadata={}, page_content='main :: IO ()'),
 Document(metadata={}, page_content='main = do\n    putStrLn "Hello, World!"\n-- Some'),
 Document(metadata={}, page_content='sample functions\nadd :: Int -> Int -> Int\nadd x y'),
 Document(metadata={}, page_content='= x + y')]

::: :::js 这里是一个使用 Haskell 文本分割器的示例：

const HASKELL_CODE = `
main :: IO ()
main = do
    putStrLn "Hello, World!"
-- Some sample functions
add :: Int -> Int -> Int
add x y = x + y
`;

const haskellSplitter = RecursiveCharacterTextSplitter.fromLanguage(
    "haskell",
    { chunkSize: 50, chunkOverlap: 0 }
);
const haskellDocs = haskellSplitter.createDocuments([{ pageContent: HASKELL_CODE }]);
console.log(haskellDocs);

[
    Document { metadata: {}, pageContent: 'main :: IO ()' },
    Document { metadata: {}, pageContent: 'main = do\n    putStrLn "Hello, World!"\n-- Some' },
    Document { metadata: {}, pageContent: 'sample functions\nadd :: Int -> Int -> Int\nadd x y' },
    Document { metadata: {}, pageContent: '= x + y' }
]

:::

PHP

:::python 这里是一个使用 PHP 文本分割器的示例：

PHP_CODE = """<?php
namespace foo;
class Hello {
    public function __construct() { }
}
function hello() {
    echo "Hello World!";
}
interface Human {
    public function breath();
}
trait Foo { }
enum Color
{
    case Red;
    case Blue;
}"""
php_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.PHP, chunk_size=50, chunk_overlap=0
)
php_docs = php_splitter.create_documents([PHP_CODE])
php_docs

[Document(metadata={}, page_content='<?php\nnamespace foo;'),
 Document(metadata={}, page_content='class Hello {'),
 Document(metadata={}, page_content='public function __construct() { }\n}'),
 Document(metadata={}, page_content='function hello() {\n    echo "Hello World!";\n}'),
 Document(metadata={}, page_content='interface Human {\n    public function breath();\n}'),
 Document(metadata={}, page_content='trait Foo { }\nenum Color\n{\n    case Red;'),
 Document(metadata={}, page_content='case Blue;\n}')]

::: :::js 这里是一个使用 PHP 文本分割器的示例：

const PHP_CODE = `<?php
namespace foo;
class Hello {
    public function __construct() { }
}
function hello() {
    echo "Hello World!";
}
interface Human {
    public function breath();
}
trait Foo { }
enum Color
{
    case Red;
    case Blue;
}`;

const phpSplitter = RecursiveCharacterTextSplitter.fromLanguage(
    "php",
    { chunkSize: 50, chunkOverlap: 0 }
);
const phpDocs = phpSplitter.createDocuments([{ pageContent: PHP_CODE }]);
console.log(phpDocs);

[
    Document { metadata: {}, pageContent: '<?php\nnamespace foo;' },
    Document { metadata: {}, pageContent: 'class Hello {' },
    Document { metadata: {}, pageContent: 'public function __construct() { }\n}' },
    Document { metadata: {}, pageContent: 'function hello() {\n    echo "Hello World!";\n}' },
    Document { metadata: {}, pageContent: 'interface Human {\n    public function breath();\n}' },
    Document { metadata: {}, pageContent: 'trait Foo { }\nenum Color\n{\n    case Red;' },
    Document { metadata: {}, pageContent: 'case Blue;\n}' }
]

:::

PowerShell

:::python 这里是一个使用 PowerShell 文本分割器的示例：

POWERSHELL_CODE = """
$directoryPath = Get-Location

$items = Get-ChildItem -Path $directoryPath

$files = $items | Where-Object { -not $_.PSIsContainer }

$sortedFiles = $files | Sort-Object LastWriteTime

foreach ($file in $sortedFiles) {
    Write-Output ("Name: " + $file.Name + " | Last Write Time: " + $file.LastWriteTime)
}
"""
powershell_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.POWERSHELL, chunk_size=100, chunk_overlap=0
)
powershell_docs = powershell_splitter.create_documents([POWERSHELL_CODE])
powershell_docs

[Document(metadata={}, page_content='$directoryPath = Get-Location\n\n$items = Get-ChildItem -Path $directoryPath'),
 Document(metadata={}, page_content='$files = $items | Where-Object { -not $_.PSIsContainer }'),
 Document(metadata={}, page_content='$sortedFiles = $files | Sort-Object LastWriteTime'),
 Document(metadata={}, page_content='foreach ($file in $sortedFiles) {'),
 Document(metadata={}, page_content='Write-Output ("Name: " + $file.Name + " | Last Write Time: " + $file.LastWriteTime)\n}')]

::: :::js 这里是一个使用 PowerShell 文本分割器的示例：

const POWERSHELL_CODE = `
$directoryPath = Get-Location

$items = Get-ChildItem -Path $directoryPath

$files = $items | Where-Object { -not $_.PSIsContainer }

$sortedFiles = $files | Sort-Object LastWriteTime

foreach ($file in $sortedFiles) {
    Write-Output ("Name: " + $file.Name + " | Last Write Time: " + $file.LastWriteTime)
}
`;

const powershellSplitter = RecursiveCharacterTextSplitter.fromLanguage(
    "powershell",
    { chunkSize: 100, chunkOverlap: 0 }
);
const powershellDocs = powershellSplitter.createDocuments([{ pageContent: POWERSHELL_CODE }]);
console.log(powershellDocs);

[
    Document { metadata: {}, pageContent: '$directoryPath = Get-Location\n\n$items = Get-ChildItem -Path $directoryPath' },
    Document { metadata: {}, pageContent: '$files = $items | Where-Object { -not $_.PSIsContainer }' },
    Document { metadata: {}, pageContent: '$sortedFiles = $files | Sort-Object LastWriteTime' },
    Document { metadata: {}, pageContent: 'foreach ($file in $sortedFiles) {' },
    Document { metadata: {}, pageContent: 'Write-Output ("Name: " + $file.Name + " | Last Write Time: " + $file.LastWriteTime)\n}' }
]

:::

Visual basic 6

:::python

VISUALBASIC6_CODE = """Option Explicit

Public Sub HelloWorld()
    MsgBox "Hello, World!"
End Sub

Private Function Add(a As Integer, b As Integer) As Integer
    Add = a + b
End Function
"""
visualbasic6_splitter = RecursiveCharacterTextSplitter.from_language(
    Language.VISUALBASIC6,
    chunk_size=128,
    chunk_overlap=0,
)
visualbasic6_docs = visualbasic6_splitter.create_documents([VISUALBASIC6_CODE])
visualbasic6_docs

[Document(metadata={}, page_content='Option Explicit'),
 Document(metadata={}, page_content='Public Sub HelloWorld()\n    MsgBox "Hello, World!"\nEnd Sub'),
 Document(metadata={}, page_content='Private Function Add(a As Integer, b As Integer) As Integer\n    Add = a + b\nEnd Function')]

::: :::js

const VISUALBASIC6_CODE = `Option Explicit

Public Sub HelloWorld()
    MsgBox "Hello, World!"
End Sub

Private Function Add(a As Integer, b As Integer) As Integer
    Add = a + b
End Function
`;

const visualbasic6Splitter = RecursiveCharacterTextSplitter.fromLanguage(
    "visualbasic6",
    { chunkSize: 128, chunkOverlap: 0 }
);
const visualbasic6Docs = visualbasic6Splitter.createDocuments([{ pageContent: VISUALBASIC6_CODE }]);
console.log(visualbasic6Docs);

[
    Document { metadata: {}, pageContent: 'Option Explicit' },
    Document { metadata: {}, pageContent: 'Public Sub HelloWorld()\n    MsgBox "Hello, World!"\nEnd Sub' },
    Document { metadata: {}, pageContent: 'Private Function Add(a As Integer, b As Integer) As Integer\n    Add = a + b\nEnd Function' }
]

:::

---

官方原文：src/oss/integrations/splitters/code_splitter.mdx · 非官方翻译，译文由 AI 生成，以英文原文为准。