```python
from langchain.text_splitter import (
    RecursiveCharacterTextSplitter,
    Language,
)
```


```python
# Full list of support languages
[e.value for e in Language]
```

<CodeOutputBlock lang="python">

```
    ['cpp',
     'go',
     'java',
     'js',
     'php',
     'proto',
     'python',
     'rst',
     'ruby',
     'rust',
     'scala',
     'swift',
     'markdown',
     'latex',
     'html',
     'sol',]
```

</CodeOutputBlock>


```python
# You can also see the separators used for a given language
RecursiveCharacterTextSplitter.get_separators_for_language(Language.PYTHON)
```

<CodeOutputBlock lang="python">

```
    ['\nclass ', '\ndef ', '\n\tdef ', '\n\n', '\n', ' ', '']
```

</CodeOutputBlock>

## Python

Here's an example using the PythonTextSplitter


```python
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
```

<CodeOutputBlock lang="python">

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

</CodeOutputBlock>

## JS
Here's an example using the JS text splitter


```python
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
```

<CodeOutputBlock lang="python">

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

</CodeOutputBlock>

## Markdown

Here's an example using the Markdown text splitter.


````python
markdown_text = """
# 🦜️🔗 LangChain

⚡ Building applications with LLMs through composability ⚡

## Quick Install

```bash
# Hopefully this code block isn't split
pip install langchain
```

As an open source project in a rapidly developing field, we are extremely open to contributions.
"""
````


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

<CodeOutputBlock lang="python">

```
    [Document(page_content='# 🦜️🔗 LangChain', metadata={}),
     Document(page_content='⚡ Building applications with LLMs through composability ⚡', metadata={}),
     Document(page_content='## Quick Install', metadata={}),
     Document(page_content="```bash\n# Hopefully this code block isn't split", metadata={}),
     Document(page_content='pip install langchain', metadata={}),
     Document(page_content='```', metadata={}),
     Document(page_content='As an open source project in a rapidly developing field, we', metadata={}),
     Document(page_content='are extremely open to contributions.', metadata={})]
```

</CodeOutputBlock>

## Latex

Here's an example on Latex text


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

<CodeOutputBlock lang="python">

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

</CodeOutputBlock>

## HTML

Here's an example using an HTML text splitter


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

<CodeOutputBlock lang="python">

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

</CodeOutputBlock>


## Solidity
Here's an example using the Solidity text splitter

```python
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
```

<CodeOutputBlock>

```
[
    Document(page_content='pragma solidity ^0.8.20;', metadata={}),
    Document(page_content='contract HelloWorld {\n   function add(uint a, uint b) pure public returns(uint) {\n       return a + b;\n   }\n}', metadata={})
]
 ```

 </CodeOutputBlock>
