如何拆分代码
RecursiveCharacterTextSplitter 包含预定义的分隔符列表，可用于按特定编程语言拆分文本。
支持的语言包括：
"html" | "cpp" | "go" | "java" | "js" | "php" | "proto" | "python" | "rst" | "ruby" | "rust" | "scala" | "swift" | "markdown" | "latex" | "sol"
要查看特定语言的分隔符列表，请将上面列表中的其中一个值传递给
getSeparatorsForLanguage() 静态方法
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
RecursiveCharacterTextSplitter.getSeparatorsForLanguage("js");
[
"\nfunction ", "\nconst ",
"\nlet ", "\nvar ",
"\nclass ", "\nif ",
"\nfor ", "\nwhile ",
"\nswitch ", "\ncase ",
"\ndefault ", "\n\n",
"\n", " ",
""
]
JS
以下是使用 JS 文本分割器的示例：
const JS_CODE = `
function helloWorld() {
console.log("Hello, World!");
}
// Call the function
helloWorld();
`;
const jsSplitter = RecursiveCharacterTextSplitter.fromLanguage("js", {
chunkSize: 60,
chunkOverlap: 0,
});
const jsDocs = await jsSplitter.createDocuments([JS_CODE]);
jsDocs;
[
Document {
pageContent: 'function helloWorld() {\n console.log("Hello, World!");\n}',
metadata: { loc: { lines: { from: 2, to: 4 } } }
},
Document {
pageContent: "// Call the function\nhelloWorld();",
metadata: { loc: { lines: { from: 6, to: 7 } } }
}
]
Python
这是一个 Python 示例：
const PYTHON_CODE = `
def hello_world():
print("Hello, World!")
# Call the function
hello_world()
`;
const pythonSplitter = RecursiveCharacterTextSplitter.fromLanguage("python", {
chunkSize: 50,
chunkOverlap: 0,
});
const pythonDocs = await pythonSplitter.createDocuments([PYTHON_CODE]);
pythonDocs;
[
Document {
pageContent: 'def hello_world():\n print("Hello, World!")',
metadata: { loc: { lines: { from: 2, to: 3 } } }
},
Document {
pageContent: "# Call the function\nhello_world()",
metadata: { loc: { lines: { from: 5, to: 6 } } }
}
]
Markdown
这是一个根据 Markdown 分隔符进行拆分的例子：
const markdownText = `
# 🦜️🔗 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.
`;
const mdSplitter = RecursiveCharacterTextSplitter.fromLanguage("markdown", {
chunkSize: 60,
chunkOverlap: 0,
});
const mdDocs = await mdSplitter.createDocuments([markdownText]);
mdDocs;
[
Document {
pageContent: "# 🦜️🔗 LangChain",
metadata: { loc: { lines: { from: 2, to: 2 } } }
},
Document {
pageContent: "⚡ Building applications with LLMs through composability ⚡",
metadata: { loc: { lines: { from: 4, to: 4 } } }
},
Document {
pageContent: "## Quick Install",
metadata: { loc: { lines: { from: 6, to: 6 } } }
},
Document {
pageContent: "```bash\n# Hopefully this code block isn't split",
metadata: { loc: { lines: { from: 8, to: 9 } } }
},
Document {
pageContent: "pip install langchain",
metadata: { loc: { lines: { from: 10, to: 10 } } }
},
Document {
pageContent: "```",
metadata: { loc: { lines: { from: 11, to: 11 } } }
},
Document {
pageContent: "As an open-source project in a rapidly developing field, we",
metadata: { loc: { lines: { from: 13, to: 13 } } }
},
Document {
pageContent: "are extremely open to contributions.",
metadata: { loc: { lines: { from: 13, to: 13 } } }
}
]
LaTeX
这是一个 LaTeX 文本的示例：
const latexText = `
\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}
`;
const latexSplitter = RecursiveCharacterTextSplitter.fromLanguage("latex", {
chunkSize: 60,
chunkOverlap: 0,
});
const latexDocs = await latexSplitter.createDocuments([latexText]);
latexDocs;
[
Document {
pageContent: "documentclass{article}\n\n\begin{document}\n\nmaketitle",
metadata: { loc: { lines: { from: 2, to: 6 } } }
},
Document {
pageContent: "section{Introduction}",
metadata: { loc: { lines: { from: 8, to: 8 } } }
},
Document {
pageContent: "Large language models (LLMs) are a type of machine learning",
metadata: { loc: { lines: { from: 9, to: 9 } } }
},
Document {
pageContent: "model that can be trained on vast amounts of text data to",
metadata: { loc: { lines: { from: 9, to: 9 } } }
},
Document {
pageContent: "generate human-like language. In recent years, LLMs have",
metadata: { loc: { lines: { from: 9, to: 9 } } }
},
Document {
pageContent: "made significant advances in a variety of natural language",
metadata: { loc: { lines: { from: 9, to: 9 } } }
},
Document {
pageContent: "processing tasks, including language translation, text",
metadata: { loc: { lines: { from: 9, to: 9 } } }
},
Document {
pageContent: "generation, and sentiment analysis.",
metadata: { loc: { lines: { from: 9, to: 9 } } }
},
Document {
pageContent: "subsection{History of LLMs}",
metadata: { loc: { lines: { from: 11, to: 11 } } }
},
Document {
pageContent: "The earliest LLMs were developed in the 1980s and 1990s,",
metadata: { loc: { lines: { from: 12, to: 12 } } }
},
Document {
pageContent: "but they were limited by the amount of data that could be",
metadata: { loc: { lines: { from: 12, to: 12 } } }
},
Document {
pageContent: "processed and the computational power available at the",
metadata: { loc: { lines: { from: 12, to: 12 } } }
},
Document {
pageContent: "time. In the past decade, however, advances in hardware and",
metadata: { loc: { lines: { from: 12, to: 12 } } }
},
Document {
pageContent: "software have made it possible to train LLMs on massive",
metadata: { loc: { lines: { from: 12, to: 12 } } }
},
Document {
pageContent: "datasets, leading to significant improvements in",
metadata: { loc: { lines: { from: 12, to: 12 } } }
},
Document {
pageContent: "performance.",
metadata: { loc: { lines: { from: 12, to: 12 } } }
},
Document {
pageContent: "subsection{Applications of LLMs}",
metadata: { loc: { lines: { from: 14, to: 14 } } }
},
Document {
pageContent: "LLMs have many applications in industry, including",
metadata: { loc: { lines: { from: 15, to: 15 } } }
},
Document {
pageContent: "chatbots, content creation, and virtual assistants. They",
metadata: { loc: { lines: { from: 15, to: 15 } } }
},
Document {
pageContent: "can also be used in academia for research in linguistics,",
metadata: { loc: { lines: { from: 15, to: 15 } } }
},
Document {
pageContent: "psychology, and computational linguistics.",
metadata: { loc: { lines: { from: 15, to: 15 } } }
},
Document {
pageContent: "end{document}",
metadata: { loc: { lines: { from: 17, to: 17 } } }
}
]
HTML
这是一个使用 HTML 文本分割器的例子：
const htmlText = `
<!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>
`;
const htmlSplitter = RecursiveCharacterTextSplitter.fromLanguage("html", {
chunkSize: 60,
chunkOverlap: 0,
});
const htmlDocs = await htmlSplitter.createDocuments([htmlText]);
htmlDocs;
[
Document {
pageContent: "<!DOCTYPE html>\n<html>",
metadata: { loc: { lines: { from: 2, to: 3 } } }
},
Document {
pageContent: "<head>\n <title>🦜️🔗 LangChain</title>",
metadata: { loc: { lines: { from: 4, to: 5 } } }
},
Document {
pageContent: "<style>\n body {\n font-family:",
metadata: { loc: { lines: { from: 6, to: 8 } } }
},
Document {
pageContent: "Arial, sans-serif;\n }\n h1 {",
metadata: { loc: { lines: { from: 8, to: 10 } } }
},
Document {
pageContent: "color: darkblue;\n }\n </style>",
metadata: { loc: { lines: { from: 11, to: 13 } } }
},
Document {
pageContent: "</head>",
metadata: { loc: { lines: { from: 14, to: 14 } } }
},
Document {
pageContent: "<body>",
metadata: { loc: { lines: { from: 15, to: 15 } } }
},
Document {
pageContent: "<div>\n <h1>🦜️🔗 LangChain</h1>",
metadata: { loc: { lines: { from: 16, to: 17 } } }
},
Document {
pageContent: "<p>⚡ Building applications with LLMs through composability",
metadata: { loc: { lines: { from: 18, to: 18 } } }
},
Document {
pageContent: "⚡</p>\n </div>",
metadata: { loc: { lines: { from: 18, to: 19 } } }
},
Document {
pageContent: "<div>\n As an open-source project in a rapidly",
metadata: { loc: { lines: { from: 20, to: 21 } } }
},
Document {
pageContent: "developing field, we are extremely open to contributions.",
metadata: { loc: { lines: { from: 21, to: 21 } } }
},
Document {
pageContent: "</div>\n </body>\n</html>",
metadata: { loc: { lines: { from: 22, to: 24 } } }
}
]
Solidity
以下是在 Solidity 代码中进行拆分的示例：
const SOL_CODE = `
pragma solidity ^0.8.20;
contract HelloWorld {
function add(uint a, uint b) pure public returns(uint) {
return a + b;
}
}
`;
const solSplitter = RecursiveCharacterTextSplitter.fromLanguage("sol", {
chunkSize: 128,
chunkOverlap: 0,
});
const solDocs = await solSplitter.createDocuments([SOL_CODE]);
solDocs;
[
Document {
pageContent: "pragma solidity ^0.8.20;",
metadata: { loc: { lines: { from: 2, to: 2 } } }
},
Document {
pageContent: "contract HelloWorld {\n" +
" function add(uint a, uint b) pure public returns(uint) {\n" +
" return a + "... 9 more characters,
metadata: { loc: { lines: { from: 3, to: 7 } } }
}
]
PHP
以下是在 PHP 代码中进行拆分的示例：
const 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;
}`;
const phpSplitter = RecursiveCharacterTextSplitter.fromLanguage("php", {
chunkSize: 50,
chunkOverlap: 0,
});
const phpDocs = await phpSplitter.createDocuments([PHP_CODE]);
phpDocs;
[
Document {
pageContent: "<?php\nnamespace foo;",
metadata: { loc: { lines: { from: 1, to: 2 } } }
},
Document {
pageContent: "class Hello {",
metadata: { loc: { lines: { from: 3, to: 3 } } }
},
Document {
pageContent: "public function __construct() { }\n}",
metadata: { loc: { lines: { from: 4, to: 5 } } }
},
Document {
pageContent: 'function hello() {\n echo "Hello World!";\n}',
metadata: { loc: { lines: { from: 6, to: 8 } } }
},
Document {
pageContent: "interface Human {\n public function breath();\n}",
metadata: { loc: { lines: { from: 9, to: 11 } } }
},
Document {
pageContent: "trait Foo { }\nenum Color\n{\n case Red;",
metadata: { loc: { lines: { from: 12, to: 15 } } }
},
Document {
pageContent: "case Blue;\n}",
metadata: { loc: { lines: { from: 16, to: 17 } } }
}
]
下一步
你现在已经了解了一种根据代码专用分隔符拆分文本的方法。 接下来，请查看完整的 检索增强生成教程。