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)	Return an instance of this class based on a specific language.
from_tiktoken_encoder([encoding_name, ...])	Text splitter that uses tiktoken encoder to count length.
get_separators_for_language(language)	Retrieve a list of separators specific to the given language.
split_documents(documents)	Split documents.
split_text(text)	Split the input text into smaller chunks based on predefined separators.
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[Any, Any]] | None = None,

) → List[Document]

Create documents from a list of texts.

Parameters:

• texts (list[str])

• metadatas (list[dict[Any, Any]] | 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

Return an instance of this class based on a specific language.

This method initializes the text splitter with language-specific separators.

Parameters:

• language (Language) – The language to configure the text splitter for.

• **kwargs (Any) – Additional keyword arguments to customize the splitter.

Returns:

An instance of the text splitter configured for the specified language.

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]

Retrieve a list of separators specific to the given language.

Parameters:

language (Language) – The language for which to get the separators.

Returns:

A list of separators appropriate for the specified 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 the input text into smaller chunks based on predefined separators.

Parameters:

text (str) – The input text to be split.

Returns:

A list of text chunks obtained after splitting.

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]