Text Splitter#
Functionality for splitting text.
- class langchain.text_splitter.CharacterTextSplitter(separator: str = '\n\n', **kwargs: Any)[source]#
Implementation of splitting text that looks at characters.
- class langchain.text_splitter.Language(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
- CPP = 'cpp'#
- GO = 'go'#
- HTML = 'html'#
- JAVA = 'java'#
- JS = 'js'#
- LATEX = 'latex'#
- MARKDOWN = 'markdown'#
- PHP = 'php'#
- PROTO = 'proto'#
- PYTHON = 'python'#
- RST = 'rst'#
- RUBY = 'ruby'#
- RUST = 'rust'#
- SCALA = 'scala'#
- SWIFT = 'swift'#
- class langchain.text_splitter.LatexTextSplitter(**kwargs: Any)[source]#
Attempts to split the text along Latex-formatted layout elements.
- class langchain.text_splitter.MarkdownTextSplitter(**kwargs: Any)[source]#
Attempts to split the text along Markdown-formatted headings.
- class langchain.text_splitter.NLTKTextSplitter(separator: str = '\n\n', **kwargs: Any)[source]#
Implementation of splitting text that looks at sentences using NLTK.
- class langchain.text_splitter.PythonCodeTextSplitter(**kwargs: Any)[source]#
Attempts to split the text along Python syntax.
- class langchain.text_splitter.RecursiveCharacterTextSplitter(separators: Optional[List[str]] = None, keep_separator: bool = True, **kwargs: Any)[source]#
Implementation of splitting text that looks at characters.
Recursively tries to split by different characters to find one that works.
- classmethod from_language(language: langchain.text_splitter.Language, **kwargs: Any) langchain.text_splitter.RecursiveCharacterTextSplitter [source]#
- static get_separators_for_language(language: langchain.text_splitter.Language) List[str] [source]#
- class langchain.text_splitter.SentenceTransformersTokenTextSplitter(chunk_overlap: int = 50, model_name: str = 'sentence-transformers/all-mpnet-base-v2', tokens_per_chunk: Optional[int] = None, **kwargs: Any)[source]#
Implementation of splitting text that looks at tokens.
- class langchain.text_splitter.SpacyTextSplitter(separator: str = '\n\n', pipeline: str = 'en_core_web_sm', **kwargs: Any)[source]#
Implementation of splitting text that looks at sentences using Spacy.
- class langchain.text_splitter.TextSplitter(chunk_size: int = 4000, chunk_overlap: int = 200, length_function: typing.Callable[[str], int] = <built-in function len>, keep_separator: bool = False, add_start_index: bool = False)[source]#
Interface for splitting text into chunks.
- async atransform_documents(documents: Sequence[langchain.schema.Document], **kwargs: Any) Sequence[langchain.schema.Document] [source]#
Asynchronously transform a sequence of documents by splitting them.
- create_documents(texts: List[str], metadatas: Optional[List[dict]] = None) List[langchain.schema.Document] [source]#
Create documents from a list of texts.
- classmethod from_huggingface_tokenizer(tokenizer: Any, **kwargs: Any) langchain.text_splitter.TextSplitter [source]#
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) langchain.text_splitter.TS [source]#
Text splitter that uses tiktoken encoder to count length.
- class langchain.text_splitter.TokenTextSplitter(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)[source]#
Implementation of splitting text that looks at tokens.
- class langchain.text_splitter.Tokenizer(chunk_overlap: 'int', tokens_per_chunk: 'int', decode: 'Callable[[list[int]], str]', encode: 'Callable[[str], List[int]]')[source]#
- chunk_overlap: int#
- decode: Callable[[list[int]], str]#
- encode: Callable[[str], List[int]]#
- tokens_per_chunk: int#
- langchain.text_splitter.split_text_on_tokens(*, text: str, tokenizer: langchain.text_splitter.Tokenizer) List[str] [source]#
Split incoming text and return chunks.