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如何拆分代码

RecursiveCharacterTextSplitter 包含了预定义的分隔符列表，这些分隔符对于分割文本在特定编程语言中非常有用。

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，请将此枚举中的一个值传递到

RecursiveCharacterTextSplitter.get_separators_for_language`

要实例化一个针对特定语言定制的分割器，请将枚举中的一个值传递给

RecursiveCharacterTextSplitter.from_language

下面我们展示了各种语言的示例。

%pip install -qU langchain-text-splitters
from langchain_text_splitters import (
    Language,
    RecursiveCharacterTextSplitter,
)

查看支持语言的完整列表：

[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']

您还可以查看用于给定语言的分隔符：

RecursiveCharacterTextSplitter.get_separators_for_language(Language.PYTHON)
['\nclass ', '\ndef ', '\n\tdef ', '\n\n', '\n', ' ', '']

Python​

这是一个使用 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(page_content='def hello_world():\n    print("Hello, World!")'),
 Document(page_content='# Call the function\nhello_world()')]

JS​

这是一个使用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(page_content='function helloWorld() {\n  console.log("Hello, World!");\n}'),
 Document(page_content='// Call the function\nhelloWorld();')]

TS​

这是一个使用TS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(page_content='function helloWorld(): void {'),
 Document(page_content='console.log("Hello, World!");\n}'),
 Document(page_content='// Call the function\nhelloWorld();')]

Markdown​

这是一个使用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.')]

Latex​

这是一个关于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(page_content='\\documentclass{article}\n\n\x08egin{document}\n\n\\maketitle'),
 Document(page_content='\\section{Introduction}'),
 Document(page_content='Large language models (LLMs) are a type of machine learning'),
 Document(page_content='model that can be trained on vast amounts of text data to'),
 Document(page_content='generate human-like language. In recent years, LLMs have'),
 Document(page_content='made significant advances in a variety of natural language'),
 Document(page_content='processing tasks, including language translation, text'),
 Document(page_content='generation, and sentiment analysis.'),
 Document(page_content='\\subsection{History of LLMs}'),
 Document(page_content='The earliest LLMs were developed in the 1980s and 1990s,'),
 Document(page_content='but they were limited by the amount of data that could be'),
 Document(page_content='processed and the computational power available at the'),
 Document(page_content='time. In the past decade, however, advances in hardware and'),
 Document(page_content='software have made it possible to train LLMs on massive'),
 Document(page_content='datasets, leading to significant improvements in'),
 Document(page_content='performance.'),
 Document(page_content='\\subsection{Applications of LLMs}'),
 Document(page_content='LLMs have many applications in industry, including'),
 Document(page_content='chatbots, content creation, and virtual assistants. They'),
 Document(page_content='can also be used in academia for research in linguistics,'),
 Document(page_content='psychology, and computational linguistics.'),
 Document(page_content='\\end{document}')]

HTML​

这是一个使用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(page_content='<!DOCTYPE html>\n<html>'),
 Document(page_content='<head>\n        <title>🦜️🔗 LangChain</title>'),
 Document(page_content='<style>\n            body {\n                font-family: Aria'),
 Document(page_content='l, sans-serif;\n            }\n            h1 {'),
 Document(page_content='color: darkblue;\n            }\n        </style>\n    </head'),
 Document(page_content='>'),
 Document(page_content='<body>'),
 Document(page_content='<div>\n            <h1>🦜️🔗 LangChain</h1>'),
 Document(page_content='<p>⚡ Building applications with LLMs through composability ⚡'),
 Document(page_content='</p>\n        </div>'),
 Document(page_content='<div>\n            As an open-source project in a rapidly dev'),
 Document(page_content='eloping field, we are extremely open to contributions.'),
 Document(page_content='</div>\n    </body>\n</html>')]

Solidity​

这里有一个使用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(page_content='pragma solidity ^0.8.20;'),
 Document(page_content='contract HelloWorld {\n   function add(uint a, uint b) pure public returns(uint) {\n       return a + b;\n   }\n}')]

C#​

这是一个使用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(page_content='using System;'),
 Document(page_content='class Program\n{\n    static void Main()\n    {\n        int age = 30; // Change the age value as needed'),
 Document(page_content='// Categorize the age without any console output\n        if (age < 18)\n        {\n            // Age is under 18'),
 Document(page_content='}\n        else if (age >= 18 && age < 65)\n        {\n            // Age is an adult\n        }\n        else\n        {'),
 Document(page_content='// Age is a senior citizen\n        }\n    }\n}')]

Haskell​

这是一个使用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(page_content='main :: IO ()'),
 Document(page_content='main = do\n    putStrLn "Hello, World!"\n-- Some'),
 Document(page_content='sample functions\nadd :: Int -> Int -> Int\nadd x y'),
 Document(page_content='= x + y')]

PHP​

这是一个使用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(page_content='<?php\nnamespace foo;'),
 Document(page_content='class Hello {'),
 Document(page_content='public function __construct() { }\n}'),
 Document(page_content='function hello() {\n    echo "Hello World!";\n}'),
 Document(page_content='interface Human {\n    public function breath();\n}'),
 Document(page_content='trait Foo { }\nenum Color\n{\n    case Red;'),
 Document(page_content='case Blue;\n}')]

PowerShell​

这是一个使用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

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