langchain/docs/modules/indexes/text_splitters/examples/code_splitter.ipynb

CodeTextSplitter

CodeTextSplitter allows you to split your code with multiple language support. Import enum Language and specify the language.

In [1]:

from langchain.text_splitter import (
    RecursiveCharacterTextSplitter,
    Language,
)

In [2]:

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

Out [2]:

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

In [3]:

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

Out [3]:

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

Python

Here's an example using the PythonTextSplitter

In [4]:

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

Out [4]:

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

JS

Here's an example using the JS text splitter

In [5]:

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

Out [5]:

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

Solidity

Here's an example using the Solidity text splitter

In [5]:

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

Out [5]:

[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={})]

Markdown

Here's an example using the Markdown text splitter.

In [7]:

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.
"""

In [8]:

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

Out [8]:

[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={})]

Latex

Here's an example on Latex text

In [9]:

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}
"""

In [10]:

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

Out [10]:

[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={})]

HTML

Here's an example using an HTML text splitter

In [11]:

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>
"""

In [12]:

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

Out [12]:

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

In [ ]: