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)	Splits the input text into smaller chunks based on tokenization.
`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]#

Splits the input text into smaller chunks based on tokenization.

This method uses a custom tokenizer configuration to encode the input text into tokens, processes the tokens in chunks of a specified size with overlap, and decodes them back into text chunks. The splitting is performed using the split_text_on_tokens function.

Parameters:: text (str) – The input text to be split into smaller chunks.
Returns:: A list of text chunks, where each chunk is derived from a portion of the input text based on the tokenization and chunking rules.
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