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88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
"""Loader that loads HuggingFace datasets."""
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from typing import Iterator, List, Mapping, Optional, Sequence, Union
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from langchain.docstore.document import Document
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from langchain.document_loaders.base import BaseLoader
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class HuggingFaceDatasetLoader(BaseLoader):
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"""Loading logic for loading documents from the Hugging Face Hub."""
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def __init__(
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self,
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path: str,
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page_content_column: str = "text",
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name: Optional[str] = None,
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data_dir: Optional[str] = None,
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data_files: Optional[
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Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]
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] = None,
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cache_dir: Optional[str] = None,
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keep_in_memory: Optional[bool] = None,
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save_infos: bool = False,
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use_auth_token: Optional[Union[bool, str]] = None,
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num_proc: Optional[int] = None,
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):
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"""Initialize the HuggingFaceDatasetLoader.
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Args:
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path: Path or name of the dataset.
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page_content_column: Page content column name.
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name: Name of the dataset configuration.
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data_dir: Data directory of the dataset configuration.
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data_files: Path(s) to source data file(s).
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cache_dir: Directory to read/write data.
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keep_in_memory: Whether to copy the dataset in-memory.
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save_infos: Save the dataset information (checksums/size/splits/...).
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use_auth_token: Bearer token for remote files on the Datasets Hub.
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num_proc: Number of processes.
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"""
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self.path = path
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self.page_content_column = page_content_column
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self.name = name
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self.data_dir = data_dir
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self.data_files = data_files
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self.cache_dir = cache_dir
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self.keep_in_memory = keep_in_memory
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self.save_infos = save_infos
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self.use_auth_token = use_auth_token
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self.num_proc = num_proc
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def lazy_load(
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self,
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) -> Iterator[Document]:
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"""Load documents lazily."""
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try:
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from datasets import load_dataset
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except ImportError:
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raise ImportError(
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"Could not import datasets python package. "
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"Please install it with `pip install datasets`."
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)
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dataset = load_dataset(
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path=self.path,
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name=self.name,
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data_dir=self.data_dir,
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data_files=self.data_files,
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cache_dir=self.cache_dir,
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keep_in_memory=self.keep_in_memory,
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save_infos=self.save_infos,
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use_auth_token=self.use_auth_token,
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num_proc=self.num_proc,
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)
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yield from (
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Document(
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page_content=row.pop(self.page_content_column),
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metadata=row,
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)
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for key in dataset.keys()
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for row in dataset[key]
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)
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def load(self) -> List[Document]:
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"""Load documents."""
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return list(self.lazy_load())
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