RecursiveCharacterTextSplitter#

class langchain_text_splitters.character.RecursiveCharacterTextSplitter(
separators: List[str] | None = None,
keep_separator: bool | Literal['start', 'end'] = True,
is_separator_regex: bool = False,
**kwargs: Any,
)[source]#

Splitting text by recursively look at characters.

Recursively tries to split by different characters to find one that works.

Create a new TextSplitter.

Methods

__init__([separators, keep_separator, ...])

Create a new TextSplitter.

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 the input text into smaller chunks based on predefined separators.

transform_documents(documents, **kwargs)

Transform sequence of documents by splitting them.
Parameters:

  • separators (Optional[List[str]])

  • keep_separator (Union[bool, Literal['start', 'end']])

  • is_separator_regex (bool)

  • kwargs (Any)

__init__(
separators: List[str] | None = None,
keep_separator: bool | Literal['start', 'end'] = True,
is_separator_regex: bool = False,
**kwargs: Any,
) → None[source]#

Create a new TextSplitter.

Parameters:

  • separators (List[str] | None)

  • keep_separator (bool | Literal['start', 'end'])

  • is_separator_regex (bool)

  • kwargs (Any)

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] | None = None,
) → List[Document]#

Create documents from a list of texts.

Parameters:

  • texts (List[str])

  • metadatas (List[dict] | 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[source]#

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][source]#

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 the input text into smaller chunks based on predefined separators.

Parameters:

text (str) – The input text to be split.

Returns:

A list of text chunks obtained after splitting.

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]

Examples using RecursiveCharacterTextSplitter