How to split code
Prerequisites
This guide assumes familiarity with the following concepts:
RecursiveCharacterTextSplitter includes pre-built lists of separators that are useful for splitting text in a specific programming language.
Supported languages include:
"html" | "cpp" | "go" | "java" | "js" | "php" | "proto" | "python" | "rst" | "ruby" | "rust" | "scala" | "swift" | "markdown" | "latex" | "sol"
To view the list of separators for a given language, pass one of the
values from the list above into the getSeparatorsForLanguage() static
method
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
RecursiveCharacterTextSplitter.getSeparatorsForLanguage("js");
[
"\nfunction ", "\nconst ",
"\nlet ", "\nvar ",
"\nclass ", "\nif ",
"\nfor ", "\nwhile ",
"\nswitch ", "\ncase ",
"\ndefault ", "\n\n",
"\n", " ",
""
]
JS
Here’s an example using the JS text splitter:
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
Here’s an example for Python:
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
Here’s an example of splitting on markdown separators:
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
Here’s an example on Latex text:
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
Here’s an example using an HTML text splitter:
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
Here’s an example using of splitting on Solidity code:
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
Here’s an example of splitting on PHP code:
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 } } }
}
]
Next steps
You’ve now learned a method for splitting text on code-specific separators.
Next, check out the full tutorial on retrieval-augmented generation.