langchain.text_splitter.SpacyTextSplitter¶

class langchain.text_splitter.SpacyTextSplitter(separator: str = '\n\n', pipeline: str = 'en_core_web_sm', **kwargs: Any)[source]¶

Splitting text using Spacy package.

Per default, Spacy’s en_core_web_sm model is used. For a faster, but potentially less accurate splitting, you can use pipeline=’sentencizer’.

Initialize the spacy text splitter.

Methods

__init__([separator, pipeline])

Initialize the spacy text splitter.

atransform_documents(documents, **kwargs)

Asynchronously transform a sequence of documents by splitting them.

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 incoming text and return chunks.

transform_documents(documents, **kwargs)

Transform sequence of documents by splitting them.

__init__(separator: str = '\n\n', pipeline: str = 'en_core_web_sm', **kwargs: Any) None[source]¶

Initialize the spacy text splitter.

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

Asynchronously transform a sequence of documents by splitting them.

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][source]¶

Split incoming text and return chunks.

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

Transform sequence of documents by splitting them.

Examples using SpacyTextSplitter¶