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*   [Introduction](/v0.2/docs/introduction)
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    *   [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)
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*   [How-to guides](/v0.2/docs/how_to/)
    
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    *   [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)
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    *   [How to select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
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    *   [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)
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    *   [Fallbacks](/v0.2/docs/how_to/fallbacks)
    *   [Few Shot Prompt Templates](/v0.2/docs/how_to/few_shot)
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    *   [How to reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)
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    *   [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)
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    *   [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)
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    *   [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)
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*   How to recursively split text by characters

On this page

How to recursively split text by characters
===========================================

Prerequisites

This guide assumes familiarity with the following concepts:

*   [Text splitters](/v0.2/docs/concepts#text-splitters)

This text splitter is the recommended one for generic text. It is parameterized by a list of characters. It tries to split on them in order until the chunks are small enough. The default list is `["\n\n", "\n", " ", ""]`. This has the effect of trying to keep all paragraphs (and then sentences, and then words) together as long as possible, as those would generically seem to be the strongest semantically related pieces of text.

1.  How the text is split: by list of characters.
2.  How the chunk size is measured: by number of characters.

Below we show example usage.

To obtain the string content directly, use `.splitText`.

To create LangChain [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) objects (e.g., for use in downstream tasks), use `.createDocuments`.

    import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";const text = `Hi.\n\nI'm Harrison.\n\nHow? Are? You?\nOkay then f f f f.This is a weird text to write, but gotta test the splittingggg some how.\n\nBye!\n\n-H.`;const splitter = new RecursiveCharacterTextSplitter({  chunkSize: 10,  chunkOverlap: 1,});const output = await splitter.createDocuments([text]);console.log(output.slice(0, 3));

    [  Document {    pageContent: "Hi.",    metadata: { loc: { lines: { from: 1, to: 1 } } }  },  Document {    pageContent: "I'm",    metadata: { loc: { lines: { from: 3, to: 3 } } }  },  Document {    pageContent: "Harrison.",    metadata: { loc: { lines: { from: 3, to: 3 } } }  }]

You’ll note that in the above example we are splitting a raw text string and getting back a list of documents. We can also split documents directly.

    import { Document } from "@langchain/core/documents";import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";const text = `Hi.\n\nI'm Harrison.\n\nHow? Are? You?\nOkay then f f f f.This is a weird text to write, but gotta test the splittingggg some how.\n\nBye!\n\n-H.`;const splitter = new RecursiveCharacterTextSplitter({  chunkSize: 10,  chunkOverlap: 1,});const docOutput = await splitter.splitDocuments([  new Document({ pageContent: text }),]);console.log(docOutput.slice(0, 3));

    [  Document {    pageContent: "Hi.",    metadata: { loc: { lines: { from: 1, to: 1 } } }  },  Document {    pageContent: "I'm",    metadata: { loc: { lines: { from: 3, to: 3 } } }  },  Document {    pageContent: "Harrison.",    metadata: { loc: { lines: { from: 3, to: 3 } } }  }]

You can customize the `RecursiveCharacterTextSplitter` with arbitrary separators by passing a `separators` parameter like this:

    import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";import { Document } from "@langchain/core/documents";const text = `Some other considerations include:- Do you deploy your backend and frontend together, or separately?- Do you deploy your backend co-located with your database, or separately?**Production Support:** As you move your LangChains into production, we'd love to offer more hands-on support.Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to share more about what you're building, and our team will get in touch.## Deployment OptionsSee below for a list of deployment options for your LangChain app. If you don't see your preferred option, please get in touch and we can add it to this list.`;const splitter = new RecursiveCharacterTextSplitter({  chunkSize: 50,  chunkOverlap: 1,  separators: ["|", "##", ">", "-"],});const docOutput = await splitter.splitDocuments([  new Document({ pageContent: text }),]);console.log(docOutput.slice(0, 3));

    [  Document {    pageContent: "Some other considerations include:",    metadata: { loc: { lines: { from: 1, to: 1 } } }  },  Document {    pageContent: "- Do you deploy your backend and frontend together",    metadata: { loc: { lines: { from: 3, to: 3 } } }  },  Document {    pageContent: "r, or separately?",    metadata: { loc: { lines: { from: 3, to: 3 } } }  }]

Next steps[​](#next-steps "Direct link to Next steps")
------------------------------------------------------

You’ve now learned a method for splitting text by character.

Next, check out [specific techinques for splitting on code](/v0.2/docs/how_to/code_splitter) or the [full tutorial on retrieval-augmented generation](/v0.2/docs/tutorials/rag).

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