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[source]¶
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][source]¶
Asynchronously transform a sequence of documents by splitting them.
create_documents(texts: List[str], metadatas: Optional[List[dict]] = None) → List[Document][source]¶
Create documents from a list of texts.
classmethod from_huggingface_tokenizer(tokenizer: Any, **kwargs: Any) → TextSplitter[source]¶
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[source]¶
Text splitter that uses tiktoken encoder to count length.
split_documents(documents: Iterable[Document]) → List[Document][source]¶
Split documents.