langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter¶

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

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.
Parameters

  • chunk_overlap (int) –

  • model_name (str) –

  • tokens_per_chunk (Optional[int]) –

  • kwargs (Any) –

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

Create a new TextSplitter.

Parameters

  • chunk_overlap (int) –

  • model_name (str) –

  • tokens_per_chunk (Optional[int]) –

  • 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]

count_tokens(*, text: str) → int[source]¶
Parameters

text (str) –

Return type

int

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 SentenceTransformersTokenTextSplitter¶