RecursiveCharacterTextSplitter includes prebuilt lists of separators that are useful for splitting text in a specific programming language. Supported languages are stored in theDocumentation Index
Fetch the complete documentation index at: https://nvd-54.mintlify.app/llms.txt
Use this file to discover all available pages before exploring further.
langchain_text_splitters.Language enum. They include:
"cpp",
"go",
"java",
"kotlin",
"js",
"ts",
"php",
"proto",
"python",
"rst",
"ruby",
"rust",
"scala",
"swift",
"markdown",
"latex",
"html",
"sol",
"csharp",
"cobol",
"c",
"lua",
"perl",
"haskell"
RecursiveCharacterTextSplitter.get_separators_for_language
RecursiveCharacterTextSplitter.from_language
pip install -qU langchain-text-splitters
from langchain_text_splitters import (
Language,
RecursiveCharacterTextSplitter,
)
[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']
RecursiveCharacterTextSplitter.get_separators_for_language(Language.PYTHON)
['\nclass ', '\ndef ', '\n\tdef ', '\n\n', '\n', ' ', '']
Python
以下是一个using the 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
以下是一个using the JS text splitter: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();')]
TS
以下是一个using the typescript text splitter: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();')]
Markdown
以下是一个using the Markdown text splitter: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
以下是一个on Latex text: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}')]
HTML
以下是一个using an HTML text splitter: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>')]
Solidity
以下是一个using the Solidity text splitter: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}')]
C#
以下是一个using the C# text splitter: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}')]
Haskell
以下是一个using the Haskell text splitter: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')]
PHP
以下是一个using the PHP text splitter: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}')]
PowerShell
以下是一个using the PowerShell text splitter: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}')]
Visual basic 6
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')]
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