langchain.text_splitter.SentenceTransformersTokenTextSplitter

class langchain.text_splitter.SentenceTransformersTokenTextSplitter(chunk_overlap: int = 50, model_name: str = 'sentence-transformers/all-mpnet-base-v2', tokens_per_chunk: Optional[int] = None, **kwargs: Any)

Splitting text to tokens using sentence model tokenizer.

Create a new TextSplitter.

Methods

__init__([chunk_overlap, model_name, ...])	Create a new TextSplitter.
atransform_documents(documents, **kwargs)	Asynchronously transform a list of documents.
count_tokens(*, text)
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.

__init__(chunk_overlap: int = 50, model_name: str = 'sentence-transformers/all-mpnet-base-v2', tokens_per_chunk: Optional[int] = None, **kwargs: Any) → None

Create a new TextSplitter.

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

Asynchronously transform a list of documents.

Parameters

documents – A sequence of Documents to be transformed.

Returns

A list of transformed Documents.

count_tokens(*, text: str) → int

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_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.

split_documents(documents: Iterable[Document]) → List[Document]

Split documents.

split_text(text: str) → List[str]

Split text into multiple components.

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

Transform sequence of documents by splitting them.

Examples using SentenceTransformersTokenTextSplitter

• Split by tokens