SentenceTransformersTokenTextSplitter#

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

Create a new TextSplitter.

Parameters:

  • chunk_overlap (int)

  • model_name (str)

  • tokens_per_chunk (int | None)

  • 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]

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

text (str)

Return type:

int

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 SentenceTransformersTokenTextSplitter#