Source code for langchain.document_loaders.tensorflow_datasets
from typing import Callable, Dict, Iterator, List, Optional
from langchain.document_loaders.base import BaseLoader
from langchain.schema import Document
from langchain.utilities.tensorflow_datasets import TensorflowDatasets
[docs]class TensorflowDatasetLoader(BaseLoader):
    """Load from `TensorFlow Dataset`.
    Attributes:
        dataset_name: the name of the dataset to load
        split_name: the name of the split to load.
        load_max_docs: a limit to the number of loaded documents. Defaults to 100.
        sample_to_document_function: a function that converts a dataset sample
          into a Document
    Example:
        .. code-block:: python
            from langchain.document_loaders import TensorflowDatasetLoader
            def mlqaen_example_to_document(example: dict) -> Document:
                return Document(
                    page_content=decode_to_str(example["context"]),
                    metadata={
                        "id": decode_to_str(example["id"]),
                        "title": decode_to_str(example["title"]),
                        "question": decode_to_str(example["question"]),
                        "answer": decode_to_str(example["answers"]["text"][0]),
                    },
                )
            tsds_client = TensorflowDatasetLoader(
                    dataset_name="mlqa/en",
                    split_name="test",
                    load_max_docs=100,
                    sample_to_document_function=mlqaen_example_to_document,
                )
    """
[docs]    def __init__(
        self,
        dataset_name: str,
        split_name: str,
        load_max_docs: Optional[int] = 100,
        sample_to_document_function: Optional[Callable[[Dict], Document]] = None,
    ):
        """Initialize the TensorflowDatasetLoader.
        Args: