JSFrameworkTextSplitter#

class langchain_text_splitters.jsx.JSFrameworkTextSplitter(
separators: List[str] | None = None,
chunk_size: int = 2000,
chunk_overlap: int = 0,
**kwargs: Any,
)[source]#

Text splitter that handles React (JSX), Vue, and Svelte code.

This splitter extends RecursiveCharacterTextSplitter to handle React (JSX), Vue, and Svelte code by: 1. Detecting and extracting custom component tags from the text 2. Using those tags as additional separators along with standard JS syntax

The splitter combines: - Custom component tags as separators (e.g. <Component, <div) - JavaScript syntax elements (function, const, if, etc) - Standard text splitting on newlines

This allows chunks to break at natural boundaries in React, Vue, and Svelte component code.

Initialize the JS Framework text splitter.

Parameters:

  • separators (List[str] | None) – Optional list of custom separator strings to use

  • chunk_size (int) – Maximum size of chunks to return

  • chunk_overlap (int) – Overlap in characters between chunks

  • **kwargs (Any) – Additional arguments to pass to parent class

Methods

__init__([separators, chunk_size, chunk_overlap])

Initialize the JS Framework text splitter.

atransform_documents(documents, **kwargs)

Asynchronously transform a list of documents.

create_documents(texts[, metadatas])

Create documents from a list of texts.

from_huggingface_tokenizer(tokenizer, **kwargs)

Text splitter that uses HuggingFace tokenizer to count length.

from_language(language, **kwargs)

Return an instance of this class based on a specific language.

from_tiktoken_encoder([encoding_name, ...])

Text splitter that uses tiktoken encoder to count length.

get_separators_for_language(language)

Retrieve a list of separators specific to the given language.

split_documents(documents)

Split documents.

split_text(text)

Split text into chunks.

transform_documents(documents, **kwargs)

Transform sequence of documents by splitting them.
__init__(
separators: List[str] | None = None,
chunk_size: int = 2000,
chunk_overlap: int = 0,
**kwargs: Any,
) → None[source]#

Initialize the JS Framework text splitter.

Parameters:

  • separators (List[str] | None) – Optional list of custom separator strings to use

  • chunk_size (int) – Maximum size of chunks to return

  • chunk_overlap (int) – Overlap in characters between chunks

  • **kwargs (Any) – Additional arguments to pass to parent class

Return type:

None

async atransform_documents(
documents: Sequence[Document],
**kwargs: Any,
) → Sequence[Document]#

Asynchronously transform a list of documents.

Parameters:

  • documents (Sequence[Document]) – A sequence of Documents to be transformed.

  • kwargs (Any)

Returns:

A sequence of transformed Documents.

Return type:

Sequence[Document]

create_documents(
texts: list[str],
metadatas: list[dict[Any, Any]] | None = None,
) → List[Document]#

Create documents from a list of texts.

Parameters:

  • texts (list[str])

  • metadatas (list[dict[Any, Any]] | None)

Return type:

List[Document]

classmethod from_huggingface_tokenizer(
tokenizer: Any,
**kwargs: Any,
) → TextSplitter#

Text splitter that uses HuggingFace tokenizer to count length.

Parameters:

  • tokenizer (Any)

  • kwargs (Any)

Return type:

TextSplitter

classmethod from_language(
language: Language,
**kwargs: Any,
) → RecursiveCharacterTextSplitter#

Return an instance of this class based on a specific language.

This method initializes the text splitter with language-specific separators.

Parameters:

  • language (Language) – The language to configure the text splitter for.

  • **kwargs (Any) – Additional keyword arguments to customize the splitter.

Returns:

An instance of the text splitter configured for the specified language.

Return type:

RecursiveCharacterTextSplitter

classmethod from_tiktoken_encoder(
encoding_name: str = 'gpt2',
model_name: str | None = None,
allowed_special: Literal['all'] | AbstractSet[str] = {},
disallowed_special: Literal['all'] | Collection[str] = 'all',
**kwargs: Any,
) → TS#

Text splitter that uses tiktoken encoder to count length.

Parameters:

  • encoding_name (str)

  • model_name (str | None)

  • allowed_special (Literal['all'] | ~typing.AbstractSet[str])

  • disallowed_special (Literal['all'] | ~typing.Collection[str])

  • kwargs (Any)

Return type:

TS

static get_separators_for_language(
language: Language,
) → List[str]#

Retrieve a list of separators specific to the given language.

Parameters:

language (Language) – The language for which to get the separators.

Returns:

A list of separators appropriate for the specified language.

Return type:

List[str]

split_documents(
documents: Iterable[Document],
) → List[Document]#

Split documents.

Parameters:

documents (Iterable[Document])

Return type:

List[Document]

split_text(
text: str,
) → List[str][source]#

Split text into chunks.

This method splits the text into chunks by: - Extracting unique opening component tags using regex - Creating separators list with extracted tags and JS separators - Splitting the text using the separators by calling the parent class method

Parameters:

text (str) – String containing code to split

Returns:

List of text chunks split on component and JS boundaries

Return type:

List[str]

transform_documents(
documents: Sequence[Document],
**kwargs: Any,
) → Sequence[Document]#

Transform sequence of documents by splitting them.

Parameters:

  • documents (Sequence[Document])

  • kwargs (Any)

Return type:

Sequence[Document]