mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
c2a3021bb0
Signed-off-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Dan O'Donovan <dan.odonovan@gmail.com> Co-authored-by: Tom Daniel Grande <tomdgrande@gmail.com> Co-authored-by: Grande <Tom.Daniel.Grande@statsbygg.no> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: ccurme <chester.curme@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Tomaz Bratanic <bratanic.tomaz@gmail.com> Co-authored-by: ZhangShenao <15201440436@163.com> Co-authored-by: Friso H. Kingma <fhkingma@gmail.com> Co-authored-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Nuno Campos <nuno@langchain.dev> Co-authored-by: Morgante Pell <morgantep@google.com>
112 lines
3.9 KiB
Python
112 lines
3.9 KiB
Python
import logging
|
|
from typing import Any, Callable, Dict, Iterator, List, Optional
|
|
|
|
from langchain_core.documents import Document
|
|
from pydantic import BaseModel, model_validator
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class TensorflowDatasets(BaseModel):
|
|
"""Access to the TensorFlow Datasets.
|
|
|
|
The Current implementation can work only with datasets that fit in a memory.
|
|
|
|
`TensorFlow Datasets` is a collection of datasets ready to use, with TensorFlow
|
|
or other Python ML frameworks, such as Jax. All datasets are exposed
|
|
as `tf.data.Datasets`.
|
|
To get started see the Guide: https://www.tensorflow.org/datasets/overview and
|
|
the list of datasets: https://www.tensorflow.org/datasets/catalog/
|
|
overview#all_datasets
|
|
|
|
You have to provide the sample_to_document_function: a function that
|
|
a sample from the dataset-specific format to the Document.
|
|
|
|
Attributes:
|
|
dataset_name: the name of the dataset to load
|
|
split_name: the name of the split to load. Defaults to "train".
|
|
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
|
|
to a Document
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.utilities import TensorflowDatasets
|
|
|
|
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 = TensorflowDatasets(
|
|
dataset_name="mlqa/en",
|
|
split_name="train",
|
|
load_max_docs=MAX_DOCS,
|
|
sample_to_document_function=mlqaen_example_to_document,
|
|
)
|
|
|
|
"""
|
|
|
|
dataset_name: str = ""
|
|
split_name: str = "train"
|
|
load_max_docs: int = 100
|
|
sample_to_document_function: Optional[Callable[[Dict], Document]] = None
|
|
dataset: Any #: :meta private:
|
|
|
|
@model_validator(mode="before")
|
|
@classmethod
|
|
def validate_environment(cls, values: Dict) -> Any:
|
|
"""Validate that the python package exists in environment."""
|
|
try:
|
|
import tensorflow # noqa: F401
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Could not import tensorflow python package. "
|
|
"Please install it with `pip install tensorflow`."
|
|
)
|
|
try:
|
|
import tensorflow_datasets
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Could not import tensorflow_datasets python package. "
|
|
"Please install it with `pip install tensorflow-datasets`."
|
|
)
|
|
if values["sample_to_document_function"] is None:
|
|
raise ValueError(
|
|
"sample_to_document_function is None. "
|
|
"Please provide a function that converts a dataset sample to"
|
|
" a Document."
|
|
)
|
|
values["dataset"] = tensorflow_datasets.load(
|
|
values["dataset_name"], split=values["split_name"]
|
|
)
|
|
|
|
return values
|
|
|
|
def lazy_load(self) -> Iterator[Document]:
|
|
"""Download a selected dataset lazily.
|
|
|
|
Returns: an iterator of Documents.
|
|
|
|
"""
|
|
return (
|
|
self.sample_to_document_function(s)
|
|
for s in self.dataset.take(self.load_max_docs)
|
|
if self.sample_to_document_function is not None
|
|
)
|
|
|
|
def load(self) -> List[Document]:
|
|
"""Download a selected dataset.
|
|
|
|
Returns: a list of Documents.
|
|
|
|
"""
|
|
return list(self.lazy_load())
|