langchain.text_splitter.RecursiveCharacterTextSplitter

class langchain.text_splitter.RecursiveCharacterTextSplitter(separators: Optional[List[str]] = None, keep_separator: bool = 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 sequence of documents by splitting them.

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)

from_tiktoken_encoder([encoding_name, ...])

Text splitter that uses tiktoken encoder to count length.

get_separators_for_language(language)

split_documents(documents)

Split documents.

split_text(text)

Split text into multiple components.

transform_documents(documents, **kwargs)

Transform sequence of documents by splitting them.

__init__(separators: Optional[List[str]] = None, keep_separator: bool = True, is_separator_regex: bool = False, **kwargs: Any) None[source]

Create a new TextSplitter.

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

Asynchronously transform a sequence of documents by splitting them.

create_documents(texts: List[str], metadatas: Optional[List[dict]] = None) List[Document]

Create documents from a list of texts.

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

Text splitter that uses HuggingFace tokenizer to count length.

classmethod from_language(language: Language, **kwargs: Any) RecursiveCharacterTextSplitter[source]
classmethod from_tiktoken_encoder(encoding_name: str = 'gpt2', model_name: Optional[str] = None, allowed_special: Union[Literal['all'], AbstractSet[str]] = {}, disallowed_special: Union[Literal['all'], Collection[str]] = 'all', **kwargs: Any) TS

Text splitter that uses tiktoken encoder to count length.

static get_separators_for_language(language: Language) List[str][source]
split_documents(documents: Iterable[Document]) List[Document]

Split documents.

split_text(text: str) List[str][source]

Split text into multiple components.

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

Transform sequence of documents by splitting them.

Examples using RecursiveCharacterTextSplitter