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)[source]¶
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 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 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[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]¶
Asynchronously transform a list of documents.
- Parameters
documents – A sequence of Documents to be transformed.
- Returns
A list of transformed Documents.
- 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.