langchain.text_splitter.TextSplitter

class langchain.text_splitter.TextSplitter(chunk_size: int = 4000, chunk_overlap: int = 200, length_function: ~typing.Callable[[str], int] = <built-in function len>, keep_separator: bool = False, add_start_index: bool = False, strip_whitespace: bool = True)

Interface for splitting text into chunks.

Create a new TextSplitter.

Parameters

• chunk_size – Maximum size of chunks to return

• chunk_overlap – Overlap in characters between chunks

• length_function – Function that measures the length of given chunks

• keep_separator – Whether to keep the separator in the chunks

• add_start_index – If True, includes chunk’s start index in metadata

• strip_whitespace – If True, strips whitespace from the start and end of every document

Methods

__init__([chunk_size, chunk_overlap, ...])	Create a new TextSplitter.
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 text into multiple components.
transform_documents(documents, **kwargs)	Transform sequence of documents by splitting them.

__init__(chunk_size: int = 4000, chunk_overlap: int = 200, length_function: ~typing.Callable[[str], int] = <built-in function len>, keep_separator: bool = False, add_start_index: bool = False, strip_whitespace: bool = True) → None

Create a new TextSplitter.

Parameters

• chunk_size – Maximum size of chunks to return

• chunk_overlap – Overlap in characters between chunks

• length_function – Function that measures the length of given chunks

• keep_separator – Whether to keep the separator in the chunks

• add_start_index – If True, includes chunk’s start index in metadata

• strip_whitespace – If True, strips whitespace from the start and end of every document

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.

abstract split_text(text: str) → List[str]

Split text into multiple components.

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

Transform sequence of documents by splitting them.