TokenTextSplitter#

class langchain_text_splitters.base.TokenTextSplitter(encoding_name: str = 'gpt2', model_name: str | None = None, allowed_special: Literal['all'] | AbstractSet[str] = {}, disallowed_special: 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: str | None = None, allowed_special: Literal['all'] | AbstractSet[str] = {}, disallowed_special: Literal['all'] | Collection[str] = 'all', **kwargs: Any) → None[source]#

Create a new TextSplitter.

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:

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_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

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