langchain.text_splitter.CharacterTextSplitter¶
- class langchain.text_splitter.CharacterTextSplitter(separator: str = '\n\n', is_separator_regex: bool = False, **kwargs: Any)[source]¶
Splitting text that looks at characters.
Create a new TextSplitter.
Methods
__init__([separator, is_separator_regex])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 incoming text and return chunks.
transform_documents(documents, **kwargs)Transform sequence of documents by splitting them.
- __init__(separator: str = '\n\n', is_separator_regex: bool = False, **kwargs: Any) None[source]¶
Create a new TextSplitter.
- 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]¶
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.