如何分割代码
RecursiveCharacterTextSplitter 包含预构建的分隔符列表,这些列表对于在特定编程语言中分割文本很有用。
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,请将此枚举中的值传递给
RecursiveCharacterTextSplitter.get_separators_for_language`
要实例化为特定语言量身定制的分割器,请将枚举中的值传递给
RecursiveCharacterTextSplitter.from_language
下面我们演示各种语言的示例。
%pip install -qU langchain-text-splitters
from langchain_text_splitters import (
Language,
RecursiveCharacterTextSplitter,
)
API 参考: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
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(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