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代码分割 (Split code)

LangChain

CodeTextSplitter 允许您使用多种语言进行代码分割。导入枚举 Language 并指定语言。

from langchain.text_splitter import (
    RecursiveCharacterTextSplitter,
    Language,
)
Full list of support languages
[e.value for e in Language]
    ['cpp',
     'go',
     'java',
     'js',
     'php',
     'proto',
     'python',
     'rst',
     'ruby',
     'rust',
     'scala',
     'swift',
     'markdown',
     'latex',
     'html',
     'sol',]
You can also see the separators used for a given language
RecursiveCharacterTextSplitter.get_separators_for_language(Language.PYTHON)
    ['\nclass ', '\ndef ', '\n\tdef ', '\n\n', '\n', ' ', '']

Python​

这里是使用 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!")', metadata={}),
     Document(page_content='# Call the function\nhello_world()', metadata={})]

JS​

这里是使用 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}', metadata={}),
     Document(page_content='// Call the function\nhelloWorld();', metadata={})]

Markdown​

这里是使用 Markdown 文本分割器的示例

markdown_text = """
    # 🦜️🔗 LangChain

    ⚡ Building applications with LLMs through composability ⚡

    ## Quick Install

        ``` bash
            pip install langchain
        ```

    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(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​

这里是使用 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', 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​

这里是使用 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.MARKDOWN, chunk_size=60, chunk_overlap=0
)
html_docs = html_splitter.create_documents([html_text])
html_docs
    [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={})]

Solidity​

这里是使用 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;', metadata={}),
    Document(page_content='contract HelloWorld {\n   function add(uint a, uint b) pure public returns(uint) {\n       return a + b;\n   }\n}', metadata={})
]