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


class HuggingFaceDatasetLoader(BaseLoader):
    """Load from `Hugging Face Hub` datasets."""

    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
        self.name = 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

    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,
            name=self.name,
            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]
        )

    def parse_obj(self, page_content: Union[str, object]) -> str:
        if isinstance(page_content, object):
            return json.dumps(page_content)
        return page_content