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[Any, Any]] | None = None,
) → List[Document]#

Create documents from a list of texts.

Parameters:

  • texts (list[str])

  • metadatas (list[dict[Any, Any]] | 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