Source code for langchain_community.document_loaders.hugging_face_dataset

import json
from typing import Iterator, Mapping, Optional, Sequence, Union

from langchain_core.documents import Document

from langchain_community.document_loaders.base import BaseLoader

[docs]class HuggingFaceDatasetLoader(BaseLoader): """Load from `Hugging Face Hub` datasets."""
[docs] def __init__( self, path: str, page_content_column: str = "text", name: Optional[str] = None, data_dir: Optional[str] = None, data_files: Optional[ Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]] ] = None, cache_dir: Optional[str] = None, keep_in_memory: Optional[bool] = None, save_infos: bool = False, use_auth_token: Optional[Union[bool, str]] = None, num_proc: Optional[int] = None, ): """Initialize the HuggingFaceDatasetLoader. Args: path: Path or name of the dataset. page_content_column: Page content column name. Default is "text". name: Name of the dataset configuration. data_dir: Data directory of the dataset configuration. data_files: Path(s) to source data file(s). cache_dir: Directory to read/write data. keep_in_memory: Whether to copy the dataset in-memory. save_infos: Save the dataset information (checksums/size/splits/...). Default is False. use_auth_token: Bearer token for remote files on the Dataset Hub. num_proc: Number of processes. """ self.path = path self.page_content_column = page_content_column = name self.data_dir = data_dir self.data_files = data_files self.cache_dir = cache_dir self.keep_in_memory = keep_in_memory self.save_infos = save_infos self.use_auth_token = use_auth_token self.num_proc = num_proc
[docs] def lazy_load( self, ) -> Iterator[Document]: """Load documents lazily.""" try: from datasets import load_dataset except ImportError: raise ImportError( "Could not import datasets python package. " "Please install it with `pip install datasets`." ) dataset = load_dataset( path=self.path,, data_dir=self.data_dir, data_files=self.data_files, cache_dir=self.cache_dir, keep_in_memory=self.keep_in_memory, save_infos=self.save_infos, use_auth_token=self.use_auth_token, num_proc=self.num_proc, ) yield from ( Document( page_content=self.parse_obj(row.pop(self.page_content_column)), metadata=row, ) for key in dataset.keys() for row in dataset[key] )
[docs] def parse_obj(self, page_content: Union[str, object]) -> str: if isinstance(page_content, object): return json.dumps(page_content) return page_content