langchain_text_splitters.base.TokenTextSplitter

class langchain_text_splitters.base.TokenTextSplitter(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)[source]

Splitting text to tokens using model tokenizer.

Create a new TextSplitter.

Methods

__init__([encoding_name, model_name, ...])

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_tiktoken_encoder([encoding_name, ...])

Text splitter that uses tiktoken encoder to count length.

split_documents(documents)

Split documents.

split_text(text)

Split text into multiple components.

transform_documents(documents, **kwargs)

Transform sequence of documents by splitting them.

Parameters
  • encoding_name (str) –

  • model_name (Optional[str]) –

  • allowed_special (Union[Literal['all'], AbstractSet[str]]) –

  • disallowed_special (Union[Literal['all'], Collection[str]]) –

  • kwargs (Any) –

__init__(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) None[source]

Create a new TextSplitter.

Parameters
  • encoding_name (str) –

  • model_name (Optional[str]) –

  • allowed_special (Union[Literal['all'], ~typing.AbstractSet[str]]) –

  • disallowed_special (Union[Literal['all'], ~typing.Collection[str]]) –

  • 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 list of transformed Documents.

Return type

Sequence[Document]

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

Create documents from a list of texts.

Parameters
  • texts (List[str]) –

  • metadatas (Optional[List[dict]]) –

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_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.

Parameters
  • encoding_name (str) –

  • model_name (Optional[str]) –

  • allowed_special (Union[Literal['all'], ~typing.AbstractSet[str]]) –

  • disallowed_special (Union[Literal['all'], ~typing.Collection[str]]) –

  • kwargs (Any) –

Return type

TS

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 multiple components.

Parameters

text (str) –

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 TokenTextSplitter