MarkdownTextSplitter

class langchain_text_splitters.markdown.MarkdownTextSplitter(**kwargs: Any)

Attempts to split the text along Markdown-formatted headings.

Initialize a MarkdownTextSplitter.

Methods

__init__(**kwargs)	Initialize a MarkdownTextSplitter.
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_language(language, **kwargs)
from_tiktoken_encoder([encoding_name, ...])	Text splitter that uses tiktoken encoder to count length.
get_separators_for_language(language)
split_documents(documents)	Split documents.
split_text(text)	Split text into multiple components.
transform_documents(documents, **kwargs)	Transform sequence of documents by splitting them.

Parameters:

kwargs (Any) –

__init__(**kwargs: Any) → None

Initialize a MarkdownTextSplitter.

Parameters:

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 sequence of transformed Documents.

Return type:

Sequence[Document]

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

Create documents from a list of texts.

Parameters:

• texts (List[str]) –

• metadatas (List[dict] | None) –

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_language(language: Language, **kwargs: Any) → RecursiveCharacterTextSplitter

Parameters:

• language (Language) –

• kwargs (Any) –

Return type:

RecursiveCharacterTextSplitter

classmethod from_tiktoken_encoder(encoding_name: str = 'gpt2', model_name: str | None = None, allowed_special: Literal['all'] | AbstractSet[str] = {}, disallowed_special: Literal['all'] | Collection[str] = 'all', **kwargs: Any) → TS

Text splitter that uses tiktoken encoder to count length.

Parameters:

• encoding_name (str) –

• model_name (str | None) –

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

• disallowed_special (Literal['all'] | ~typing.Collection[str]) –

• kwargs (Any) –

Return type:

TS

static get_separators_for_language(language: Language) → List[str]

Parameters:

language (Language) –

Return type:

List[str]

split_documents(documents: Iterable[Document]) → List[Document]

Split documents.

Parameters:

documents (Iterable[Document]) –

Return type:

List[Document]

split_text(text: str) → List[str]

Split text into multiple components.

Parameters:

text (str) –

Return type:

List[str]

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]