Source code for langchain_community.document_loaders.rtf

"""Loads rich text files."""
from pathlib import Path
from typing import Any, List, Union

from langchain_community.document_loaders.unstructured import (

[docs]class UnstructuredRTFLoader(UnstructuredFileLoader): """Load `RTF` files using `Unstructured`. You can run the loader in one of two modes: "single" and "elements". If you use "single" mode, the document will be returned as a single langchain Document object. If you use "elements" mode, the unstructured library will split the document into elements such as Title and NarrativeText. You can pass in additional unstructured kwargs after mode to apply different unstructured settings. Examples -------- from langchain_community.document_loaders import UnstructuredRTFLoader loader = UnstructuredRTFLoader( "example.rtf", mode="elements", strategy="fast", ) docs = loader.load() References ---------- """
[docs] def __init__( self, file_path: Union[str, Path], mode: str = "single", **unstructured_kwargs: Any, ): """ Initialize with a file path. Args: file_path: The path to the file to load. mode: The mode to use for partitioning. See unstructured for details. Defaults to "single". **unstructured_kwargs: Additional keyword arguments to pass to unstructured. """ min_unstructured_version = "0.5.12" if not satisfies_min_unstructured_version(min_unstructured_version): raise ValueError( "Partitioning rtf files is only supported in " f"unstructured>={min_unstructured_version}." ) super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List: from unstructured.partition.rtf import partition_rtf return partition_rtf(filename=self.file_path, **self.unstructured_kwargs)