> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-docsdy-1782337909-2539eb6.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Splitting code text splitter integration guide

[RecursiveCharacterTextSplitter](https://reference.langchain.com/python/langchain-text-splitters/character/RecursiveCharacterTextSplitter) includes prebuilt lists of separators that are useful for [splitting text](/oss/python/integrations/splitters/) in a specific programming language.

Supported languages are stored in the `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"
```

To view the list of separators for a given language, pass a value from this enum into

```python theme={null}
RecursiveCharacterTextSplitter.get_separators_for_language
```

To instantiate a splitter that is tailored for a specific language, pass a value from the enum into

```python theme={null}
RecursiveCharacterTextSplitter.from_language
```

Below we demonstrate examples for the various languages.

```python theme={null}
pip install -qU langchain-text-splitters
```

```python theme={null}
from langchain_text_splitters import (
    Language,
    RecursiveCharacterTextSplitter,
)
```

To view the full list of supported languages:

```python theme={null}
[e.value for e in Language]
```

```python theme={null}
['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']
```

You can also see the separators used for a given language:

```python theme={null}
RecursiveCharacterTextSplitter.get_separators_for_language(Language.PYTHON)
```

```python theme={null}
['\nclass ', '\ndef ', '\n\tdef ', '\n\n', '\n', ' ', '']
```

## Python

Here's an example using the PythonTextSplitter:

```python theme={null}
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
```

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

## JS

Here's an example using the JS text splitter:

```python theme={null}
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
```

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

## TS

Here's an example using the typescript text splitter:

```python theme={null}
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
```

```javascript theme={null}
[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

Here's an example using the Markdown text splitter:

```python theme={null}
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.
"""
```

```python theme={null}
md_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.MARKDOWN, chunk_size=60, chunk_overlap=0
)
md_docs = md_splitter.create_documents([markdown_text])
md_docs
```

```text theme={null}
[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

Here's an example on Latex text:

```python theme={null}
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}
"""
```

```python theme={null}
latex_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.MARKDOWN, chunk_size=60, chunk_overlap=0
)
latex_docs = latex_splitter.create_documents([latex_text])
latex_docs
```

```text theme={null}
[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

Here's an example using an HTML text splitter:

```python theme={null}
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>
"""
```

```python theme={null}
html_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.HTML, chunk_size=60, chunk_overlap=0
)
html_docs = html_splitter.create_documents([html_text])
html_docs
```

```text theme={null}
[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

Here's an example using the Solidity text splitter:

```python theme={null}
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
```

```text theme={null}
[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\#

Here's an example using the C# text splitter:

```python theme={null}
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
```

```text theme={null}
[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

Here's an example using the Haskell text splitter:

```python theme={null}
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
```

```text theme={null}
[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

Here's an example using the PHP text splitter:

```python theme={null}
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
```

```text theme={null}
[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

Here's an example using the PowerShell text splitter:

```python theme={null}
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
```

```text theme={null}
[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

```python theme={null}
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
```

```text theme={null}
[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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