SpacyTextSplitter#

class langchain_text_splitters.spacy.SpacyTextSplitter(separator: str = '\n\n', pipeline: str = 'en_core_web_sm', max_length: int = 1000000, *, strip_whitespace: bool = True, **kwargs: Any)[source]#

Splitting text using Spacy package.

Per default, Spacy’s en_core_web_sm model is used and its default max_length is 1000000 (it is the length of maximum character this model takes which can be increased for large files). For a faster, but potentially less accurate splitting, you can use pipeline=’sentencizer’.

Initialize the spacy text splitter.

Methods

__init__([separator, pipeline, max_length, ...])

Initialize the spacy text splitter.

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)

Split incoming text and return chunks.

transform_documents(documents, **kwargs)

Transform sequence of documents by splitting them.
Parameters:

  • separator (str) –

  • pipeline (str) –

  • max_length (int) –

  • strip_whitespace (bool) –

  • kwargs (Any) –

__init__(separator: str = '\n\n', pipeline: str = 'en_core_web_sm', max_length: int = 1000000, *, strip_whitespace: bool = True, **kwargs: Any) → None[source]#

Initialize the spacy text splitter.

Parameters:

  • separator (str) –

  • pipeline (str) –

  • max_length (int) –

  • strip_whitespace (bool) –

  • 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]#

Split incoming text and return chunks.

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 SpacyTextSplitter

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