langchain_ai21.semantic_text_splitter.AI21SemanticTextSplitter

class langchain_ai21.semantic_text_splitter.AI21SemanticTextSplitter(chunk_size: int = 0, chunk_overlap: int = 0, client: Optional[Any] = None, api_key: Optional[SecretStr] = None, api_host: Optional[str] = None, timeout_sec: Optional[float] = None, num_retries: Optional[int] = None, **kwargs: Any)

Splitting text into coherent and readable units, based on distinct topics and lines

Create a new TextSplitter.

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(source)	Split text into multiple components.
split_text_to_documents(source)	Split text into multiple documents.
transform_documents(documents, **kwargs)	Transform sequence of documents by splitting them.

Parameters

• chunk_size (int) –

• chunk_overlap (int) –

• client (Optional[Any]) –

• api_key (Optional[SecretStr]) –

• api_host (Optional[str]) –

• timeout_sec (Optional[float]) –

• num_retries (Optional[int]) –

• kwargs (Any) –

__init__(chunk_size: int = 0, chunk_overlap: int = 0, client: Optional[Any] = None, api_key: Optional[SecretStr] = None, api_host: Optional[str] = None, timeout_sec: Optional[float] = None, num_retries: Optional[int] = None, **kwargs: Any) → None

Create a new TextSplitter.

Parameters

• chunk_size (int) –

• chunk_overlap (int) –

• client (Optional[Any]) –

• api_key (Optional[SecretStr]) –

• api_host (Optional[str]) –

• timeout_sec (Optional[float]) –

• num_retries (Optional[int]) –

• 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 list of transformed Documents.

Return type

Sequence[Document]

create_documents(texts: List[str], metadatas: Optional[List[dict]] = None) → List[Document]

Create documents from a list of texts.

Parameters

• texts (List[str]) –

• metadatas (Optional[List[dict]]) –

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: 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.

Parameters

• encoding_name (str) –

• model_name (Optional[str]) –

• allowed_special (Union[Literal['all'], ~typing.AbstractSet[str]]) –

• disallowed_special (Union[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(source: str) → List[str]

Split text into multiple components.

Parameters

source (str) – Specifies the text input for text segmentation

Return type

List[str]

split_text_to_documents(source: str) → List[Document]

Split text into multiple documents.

Parameters

source (str) – Specifies the text input for text segmentation

Return type

List[Document]

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]