如何拆分代码
RecursiveCharacterTextSplitter 包含预构建的分隔符列表，这些分隔符对于在特定编程语言中拆分文本非常有用。
支持的语言存储在langchain_text_splitters.Languageenum 中。他们包括：
"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，请将此枚举中的值传递到
RecursiveCharacterTextSplitter.get_separators_for_language
要实例化为特定语言定制的拆分器，请将枚举中的值传递给
RecursiveCharacterTextSplitter.from_language
下面我们演示了各种语言的示例。
%pip install -qU langchain-text-splitters
from langchain_text_splitters import (
Language,
RecursiveCharacterTextSplitter,
)
要查看支持的语言的完整列表：
[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', ' ', '']
蟒
下面是一个使用 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
下面是一个使用 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 系列
下面是使用 TS 文本拆分器的示例：
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
下面是一个使用 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.')]
乳胶
以下是 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全文]
下面是一个使用 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>')]
坚固
以下是使用 Solidity 文本拆分器的示例：
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#
下面是使用 C# 文本拆分器的示例：
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}')]
哈斯克尔
下面是一个使用 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')]
菲律宾
下面是一个使用 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
下面是使用 PowerShell 文本拆分器的示例：
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