mirror of
https://github.com/hwchase17/langchain
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78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
from typing import Callable, Dict, Iterator, Optional
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from langchain_core.documents import Document
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from langchain_community.document_loaders.base import BaseLoader
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from langchain_community.utilities.tensorflow_datasets import TensorflowDatasets
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class TensorflowDatasetLoader(BaseLoader):
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"""Load from `TensorFlow Dataset`.
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Attributes:
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dataset_name: the name of the dataset to load
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split_name: the name of the split to load.
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load_max_docs: a limit to the number of loaded documents. Defaults to 100.
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sample_to_document_function: a function that converts a dataset sample
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into a Document
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Example:
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.. code-block:: python
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from langchain_community.document_loaders import TensorflowDatasetLoader
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def mlqaen_example_to_document(example: dict) -> Document:
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return Document(
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page_content=decode_to_str(example["context"]),
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metadata={
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"id": decode_to_str(example["id"]),
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"title": decode_to_str(example["title"]),
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"question": decode_to_str(example["question"]),
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"answer": decode_to_str(example["answers"]["text"][0]),
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},
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)
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tsds_client = TensorflowDatasetLoader(
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dataset_name="mlqa/en",
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split_name="test",
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load_max_docs=100,
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sample_to_document_function=mlqaen_example_to_document,
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)
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"""
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def __init__(
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self,
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dataset_name: str,
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split_name: str,
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load_max_docs: Optional[int] = 100,
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sample_to_document_function: Optional[Callable[[Dict], Document]] = None,
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):
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"""Initialize the TensorflowDatasetLoader.
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Args:
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dataset_name: the name of the dataset to load
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split_name: the name of the split to load.
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load_max_docs: a limit to the number of loaded documents. Defaults to 100.
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sample_to_document_function: a function that converts a dataset sample
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into a Document.
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"""
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self.dataset_name: str = dataset_name
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self.split_name: str = split_name
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self.load_max_docs = load_max_docs
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"""The maximum number of documents to load."""
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self.sample_to_document_function: Optional[
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Callable[[Dict], Document]
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] = sample_to_document_function
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"""Custom function that transform a dataset sample into a Document."""
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self._tfds_client = TensorflowDatasets(
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dataset_name=self.dataset_name,
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split_name=self.split_name,
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load_max_docs=self.load_max_docs,
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sample_to_document_function=self.sample_to_document_function,
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)
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def lazy_load(self) -> Iterator[Document]:
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yield from self._tfds_client.lazy_load()
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