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*   [Introduction](/v0.2/docs/introduction)
*   [Tutorials](/v0.2/docs/tutorials/)
    
    *   [Build a Question Answering application over a Graph Database](/v0.2/docs/tutorials/graph)
    *   [Tutorials](/v0.2/docs/tutorials/)
    *   [Build a Simple LLM Application](/v0.2/docs/tutorials/llm_chain)
    *   [Build a Query Analysis System](/v0.2/docs/tutorials/query_analysis)
    *   [Build a Chatbot](/v0.2/docs/tutorials/chatbot)
    *   [Build an Agent](/v0.2/docs/tutorials/agents)
    *   [Build an Extraction Chain](/v0.2/docs/tutorials/extraction)
    *   [Summarize Text](/v0.2/docs/tutorials/summarization)
    *   [Tagging](/v0.2/docs/tutorials/classification)
    *   [Build a Local RAG Application](/v0.2/docs/tutorials/local_rag)
    *   [Conversational RAG](/v0.2/docs/tutorials/qa_chat_history)
    *   [Build a Retrieval Augmented Generation (RAG) App](/v0.2/docs/tutorials/rag)
    *   [Build a Question/Answering system over SQL data](/v0.2/docs/tutorials/sql_qa)
*   [How-to guides](/v0.2/docs/how_to/)
    
    *   [How-to guides](/v0.2/docs/how_to/)
    *   [How to use example selectors](/v0.2/docs/how_to/example_selectors)
    *   [Installation](/v0.2/docs/how_to/installation)
    *   [How to stream responses from an LLM](/v0.2/docs/how_to/streaming_llm)
    *   [How to stream chat model responses](/v0.2/docs/how_to/chat_streaming)
    *   [How to embed text data](/v0.2/docs/how_to/embed_text)
    *   [How to use few shot examples in chat models](/v0.2/docs/how_to/few_shot_examples_chat)
    *   [How to cache model responses](/v0.2/docs/how_to/llm_caching)
    *   [How to cache chat model responses](/v0.2/docs/how_to/chat_model_caching)
    *   [How to create a custom LLM class](/v0.2/docs/how_to/custom_llm)
    *   [How to use few shot examples](/v0.2/docs/how_to/few_shot_examples)
    *   [How to use output parsers to parse an LLM response into structured format](/v0.2/docs/how_to/output_parser_structured)
    *   [How to return structured data from a model](/v0.2/docs/how_to/structured_output)
    *   [How to add ad-hoc tool calling capability to LLMs and Chat Models](/v0.2/docs/how_to/tools_prompting)
    *   [How to create a custom chat model class](/v0.2/docs/how_to/custom_chat)
    *   [How to do per-user retrieval](/v0.2/docs/how_to/qa_per_user)
    *   [How to track token usage](/v0.2/docs/how_to/chat_token_usage_tracking)
    *   [How to track token usage](/v0.2/docs/how_to/llm_token_usage_tracking)
    *   [How to pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
    *   [How to compose prompts together](/v0.2/docs/how_to/prompts_composition)
    *   [How to use legacy LangChain Agents (AgentExecutor)](/v0.2/docs/how_to/agent_executor)
    *   [How to add values to a chain's state](/v0.2/docs/how_to/assign)
    *   [How to attach runtime arguments to a Runnable](/v0.2/docs/how_to/binding)
    *   [How to cache embedding results](/v0.2/docs/how_to/caching_embeddings)
    *   [How to split by character](/v0.2/docs/how_to/character_text_splitter)
    *   [How to manage memory](/v0.2/docs/how_to/chatbots_memory)
    *   [How to do retrieval](/v0.2/docs/how_to/chatbots_retrieval)
    *   [How to use tools](/v0.2/docs/how_to/chatbots_tools)
    *   [How to split code](/v0.2/docs/how_to/code_splitter)
    *   [How to do retrieval with contextual compression](/v0.2/docs/how_to/contextual_compression)
    *   [How to write a custom retriever class](/v0.2/docs/how_to/custom_retriever)
    *   [How to create custom Tools](/v0.2/docs/how_to/custom_tools)
    *   [How to debug your LLM apps](/v0.2/docs/how_to/debugging)
    *   [How to load CSV data](/v0.2/docs/how_to/document_loader_csv)
    *   [How to write a custom document loader](/v0.2/docs/how_to/document_loader_custom)
    *   [How to load data from a directory](/v0.2/docs/how_to/document_loader_directory)
    *   [How to load PDF files](/v0.2/docs/how_to/document_loader_pdf)
    *   [How to load JSON data](/v0.2/docs/how_to/document_loaders_json)
    *   [How to select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
    *   [How to select examples by similarity](/v0.2/docs/how_to/example_selectors_similarity)
    *   [How to use reference examples](/v0.2/docs/how_to/extraction_examples)
    *   [How to handle long text](/v0.2/docs/how_to/extraction_long_text)
    *   [How to do extraction without using function calling](/v0.2/docs/how_to/extraction_parse)
    *   [Fallbacks](/v0.2/docs/how_to/fallbacks)
    *   [Few Shot Prompt Templates](/v0.2/docs/how_to/few_shot)
    *   [How to run custom functions](/v0.2/docs/how_to/functions)
    *   [How to construct knowledge graphs](/v0.2/docs/how_to/graph_constructing)
    *   [How to map values to a database](/v0.2/docs/how_to/graph_mapping)
    *   [How to improve results with prompting](/v0.2/docs/how_to/graph_prompting)
    *   [How to add a semantic layer over the database](/v0.2/docs/how_to/graph_semantic)
    *   [How to reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)
    *   [How to get log probabilities](/v0.2/docs/how_to/logprobs)
    *   [How to add message history](/v0.2/docs/how_to/message_history)
    *   [How to generate multiple embeddings per document](/v0.2/docs/how_to/multi_vector)
    *   [How to generate multiple queries to retrieve data for](/v0.2/docs/how_to/multiple_queries)
    *   [How to parse JSON output](/v0.2/docs/how_to/output_parser_json)
    *   [How to retry when output parsing errors occur](/v0.2/docs/how_to/output_parser_retry)
    *   [How to parse XML output](/v0.2/docs/how_to/output_parser_xml)
    *   [How to invoke runnables in parallel](/v0.2/docs/how_to/parallel)
    *   [How to retrieve the whole document for a chunk](/v0.2/docs/how_to/parent_document_retriever)
    *   [How to partially format prompt templates](/v0.2/docs/how_to/prompts_partial)
    *   [How to add chat history to a question-answering chain](/v0.2/docs/how_to/qa_chat_history_how_to)
    *   [How to return citations](/v0.2/docs/how_to/qa_citations)
    *   [How to return sources](/v0.2/docs/how_to/qa_sources)
    *   [How to stream from a question-answering chain](/v0.2/docs/how_to/qa_streaming)
    *   [How to construct filters](/v0.2/docs/how_to/query_constructing_filters)
    *   [How to add examples to the prompt](/v0.2/docs/how_to/query_few_shot)
    *   [How to deal with high cardinality categorical variables](/v0.2/docs/how_to/query_high_cardinality)
    *   [How to handle multiple queries](/v0.2/docs/how_to/query_multiple_queries)
    *   [How to handle multiple retrievers](/v0.2/docs/how_to/query_multiple_retrievers)
    *   [How to handle cases where no queries are generated](/v0.2/docs/how_to/query_no_queries)
    *   [How to recursively split text by characters](/v0.2/docs/how_to/recursive_text_splitter)
    *   [How to reduce retrieval latency](/v0.2/docs/how_to/reduce_retrieval_latency)
    *   [How to route execution within a chain](/v0.2/docs/how_to/routing)
    *   [How to chain runnables](/v0.2/docs/how_to/sequence)
    *   [How to split text by tokens](/v0.2/docs/how_to/split_by_token)
    *   [How to deal with large databases](/v0.2/docs/how_to/sql_large_db)
    *   [How to use prompting to improve results](/v0.2/docs/how_to/sql_prompting)
    *   [How to do query validation](/v0.2/docs/how_to/sql_query_checking)
    *   [How to stream](/v0.2/docs/how_to/streaming)
    *   [How to create a time-weighted retriever](/v0.2/docs/how_to/time_weighted_vectorstore)
    *   [How to use a chat model to call tools](/v0.2/docs/how_to/tool_calling)
    *   [How to call tools with multi-modal data](/v0.2/docs/how_to/tool_calls_multi_modal)
    *   [How to use LangChain tools](/v0.2/docs/how_to/tools_builtin)
    *   [How use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
    *   [How to create and query vector stores](/v0.2/docs/how_to/vectorstores)
*   [Conceptual guide](/v0.2/docs/concepts)
*   Ecosystem
    
    *   [🦜🛠️ LangSmith](/v0.2/docs/langsmith/)
        
    *   [🦜🕸️LangGraph.js](/v0.2/docs/langgraph)
*   Versions
    
    *   [Overview](/v0.2/docs/versions/overview)
    *   [v0.2](/v0.2/docs/versions/v0_2)
    *   [Release Policy](/v0.2/docs/versions/release_policy)
    *   [Packages](/v0.2/docs/versions/packages)
*   [Security](/v0.2/docs/security)

*   [](/v0.2/)
*   [How-to guides](/v0.2/docs/how_to/)
*   How to split code

On this page

How to split code
=================

Prerequisites

This guide assumes familiarity with the following concepts:

*   [Text splitters](/v0.2/docs/concepts#text-splitters)
*   [Recursively splitting text by character](/v0.2/docs/how_to/recursive_text_splitter)

[RecursiveCharacterTextSplitter](https://v02.api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) 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[​](#js "Direct link to JS")
------------------------------

Here’s an example using the JS text splitter:

    const JS_CODE = `function helloWorld() {  console.log("Hello, World!");}// Call the functionhelloWorld();`;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[​](#python "Direct link to Python")
------------------------------------------

Here’s an example for Python:

    const PYTHON_CODE = `def hello_world():    print("Hello, World!")# Call the functionhello_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[​](#markdown "Direct link to 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 splitpip 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[​](#latex "Direct link to 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[​](#html "Direct link to 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[​](#solidity "Direct link to Solidity")
------------------------------------------------

Here’s an example using of splitting on [Solidity](https://soliditylang.org/) 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[​](#php "Direct link to PHP")
---------------------------------

Here’s an example of splitting on PHP code:

    const PHP_CODE = `<?phpnamespace 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[​](#next-steps "Direct link to 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](/v0.2/docs/tutorials/rag).

* * *

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How to do retrieval with contextual compression

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*   [JS](#js)
*   [Python](#python)
*   [Markdown](#markdown)
*   [Latex](#latex)
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*   [Next steps](#next-steps)

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