SemanticChunker#

class langchain_experimental.text_splitter.SemanticChunker(
embeddings: Embeddings,
buffer_size: int = 1,
add_start_index: bool = False,
breakpoint_threshold_type: Literal['percentile', 'standard_deviation', 'interquartile', 'gradient'] = 'percentile',
breakpoint_threshold_amount: float | None = None,
number_of_chunks: int | None = None,
sentence_split_regex: str = '(?<=[.?!])\\s+',
min_chunk_size: int | None = None,
)[source]#

Split the text based on semantic similarity.

Taken from Greg Kamradt’s wonderful notebook: FullStackRetrieval-com/RetrievalTutorials

All credits to him.

At a high level, this splits into sentences, then groups into groups of 3 sentences, and then merges one that are similar in the embedding space.

Methods

__init__(embeddings[, buffer_size, ...])

atransform_documents(documents, **kwargs)

Asynchronously transform a list of documents.

create_documents(texts[, metadatas])

Create documents from a list of texts.

split_documents(documents)

Split documents.

split_text(text)

transform_documents(documents, **kwargs)

Transform sequence of documents by splitting them.
Parameters:

  • embeddings (Embeddings)

  • buffer_size (int)

  • add_start_index (bool)

  • breakpoint_threshold_type (Literal['percentile', 'standard_deviation', 'interquartile', 'gradient'])

  • breakpoint_threshold_amount (float | None)

  • number_of_chunks (int | None)

  • sentence_split_regex (str)

  • min_chunk_size (int | None)

__init__(
embeddings: Embeddings,
buffer_size: int = 1,
add_start_index: bool = False,
breakpoint_threshold_type: Literal['percentile', 'standard_deviation', 'interquartile', 'gradient'] = 'percentile',
breakpoint_threshold_amount: float | None = None,
number_of_chunks: int | None = None,
sentence_split_regex: str = '(?<=[.?!])\\s+',
min_chunk_size: int | None = None,
)[source]#
Parameters:

  • embeddings (Embeddings)

  • buffer_size (int)

  • add_start_index (bool)

  • breakpoint_threshold_type (Literal['percentile', 'standard_deviation', 'interquartile', 'gradient'])

  • breakpoint_threshold_amount (float | None)

  • number_of_chunks (int | None)

  • sentence_split_regex (str)

  • min_chunk_size (int | 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][source]#

Create documents from a list of texts.

Parameters:

  • texts (List[str])

  • metadatas (List[dict] | None)

Return type:

List[Document]

split_documents(
documents: Iterable[Document],
) → List[Document][source]#

Split documents.

Parameters:

documents (Iterable[Document])

Return type:

List[Document]

split_text(text: str) → List[str][source]#
Parameters:

text (str)

Return type:

List[str]

transform_documents(
documents: Sequence[Document],
**kwargs: Any,
) → Sequence[Document][source]#

Transform sequence of documents by splitting them.

Parameters:

  • documents (Sequence[Document])

  • kwargs (Any)

Return type:

Sequence[Document]

Examples using SemanticChunker