如何拆分代码

RecursiveCharacterTextSplitter 包含了一些预先构建的分隔符列表，用于在特定的编程语言中拆分文本。

支持的语言存储在 langchain_text_splitters.Language 枚举中。它们包括：

"cpp",
"go",
"java",
"kotlin",
"js",
"ts",
"php",
"proto",
"python",
"rst",
"ruby",
"rust",
"scala",
"swift",
"markdown",
"latex",
"html",
"sol",
"csharp",
"cobol",
"c",
"lua",
"perl",
"haskell"

要查看给定语言的分隔符列表，请将该枚举的值传递给 RecursiveCharacterTextSplitter.get_separators_for_language。

要实例化一个针对特定语言定制的拆分器，请将枚举的值传递给 RecursiveCharacterTextSplitter.from_language。

下面我们演示了各种语言的示例。

%pip install -qU langchain-text-splitters

from langchain_text_splitters import (
    Language,
    RecursiveCharacterTextSplitter,
)

要查看支持的语言的完整列表：

[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']

您还可以查看给定语言使用的分隔符：

RecursiveCharacterTextSplitter.get_separators_for_language(Language.PYTHON)

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

Python

这是使用 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!")'),
 Document(page_content='# Call the function\nhello_world()')]

JS

这是使用 JS 文本拆分器的示例：

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}'),
 Document(page_content='// Call the function\nhelloWorld();')]

TS

这是使用 TS 文本拆分器的示例：

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

Markdown

这是使用 Markdown 文本拆分器的示例：

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

[Document(page_content='# 🦜️🔗 LangChain'),
 Document(page_content='⚡ Building applications with LLMs through composability ⚡'),
 Document(page_content='## Quick Install\n\n```bash'),

 Document(page_content="# Hopefully this code block isn't split"),

 Document(page_content='pip install langchain'),

 Document(page_content='```'),
 Document(page_content='As an open-source project in a rapidly developing field, we'),
 Document(page_content='are extremely open to contributions.')]

Latex

这是关于 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}

    language=Language.CSHARP, chunk_size=128, chunk_overlap=0
)
c_docs = c_splitter.create_documents([C_CODE])
c_docs

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

HTML

这是一个使用 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.HTML, chunk_size=60, chunk_overlap=0
)
html_docs = html_splitter.create_documents([html_text])
html_docs

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

Solidity

这是一个使用 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;'),
 Document(page_content='contract HelloWorld {\n   function add(uint a, uint b) pure public returns(uint) {\n       return a + b;\n   }\n}')]

C#

这是一个使用 C# 文本分割器的示例：

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

输出结果：
[Document(page_content='using System;'),
 Document(page_content='class Program\n{\n    static void Main()\n    {\n        int age = 30; // Change the age value as needed'),
 Document(page_content='// Categorize the age without any console output\n        if (age < 18)\n        {\n            // Age is under 18'),
 Document(page_content='}\n        else if (age >= 18 && age < 65)\n        {\n            // Age is an adult\n        }\n        else\n        {'),
 Document(page_content='// Age is a senior citizen\n        }\n    }\n}')]

Haskell

这是一个使用 Haskell 文本分割器的示例：

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

输出结果：
[Document(page_content='main :: IO ()'),
 Document(page_content='main = do\n    putStrLn "Hello, World!"\n-- Some'),
 Document(page_content='sample functions\nadd :: Int -> Int -> Int\nadd x y'),
 Document(page_content='= x + y')]

PHP

这是一个使用 PHP 文本分割器的示例：

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
)
haskell_docs = php_splitter.create_documents([PHP_CODE])
haskell_docs

输出结果：
[Document(page_content='<?php\nnamespace foo;'),
 Document(page_content='class Hello {'),
 Document(page_content='public function __construct() { }\n}'),
 Document(page_content='function hello() {\n    echo "Hello World!";\n}'),
 Document(page_content='interface Human {\n    public function breath();\n}'),
 Document(page_content='trait Foo { }\nenum Color\n{\n    case Red;'),
 Document(page_content='case Blue;\n}')]