forked from phoenix-oss/llama-stack-mirror
821810657f
* skeleton dataset / datasetio * dataset datasetio * config * address comments * delete dataset_utils * address comments * naming fix
142 lines
4.3 KiB
Python
142 lines
4.3 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import List, Optional
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import pandas
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_stack.apis.datasetio import * # noqa: F403
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from llama_stack.providers.datatypes import DatasetsProtocolPrivate
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from .config import MetaReferenceDatasetIOConfig
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class BaseDataset(ABC):
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def __init__(self, *args, **kwargs) -> None:
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super().__init__(*args, **kwargs)
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@abstractmethod
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def __len__(self) -> int:
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raise NotImplementedError()
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@abstractmethod
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def __getitem__(self, idx):
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raise NotImplementedError()
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@abstractmethod
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def load(self):
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raise NotImplementedError()
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@dataclass
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class DatasetInfo:
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dataset_def: DatasetDef
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dataset_impl: BaseDataset
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class PandasDataframeDataset(BaseDataset):
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def __init__(self, dataset_def: DatasetDef, *args, **kwargs) -> None:
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super().__init__(*args, **kwargs)
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self.dataset_def = dataset_def
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self.df = None
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def __len__(self) -> int:
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assert self.df is not None, "Dataset not loaded. Please call .load() first"
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return len(self.df)
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def __getitem__(self, idx):
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if isinstance(idx, slice):
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return self.df.iloc[idx].to_dict(orient="records")
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else:
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return self.df.iloc[idx].to_dict()
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def load(self) -> None:
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if self.df is not None:
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return
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# TODO: more robust support w/ data url
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if self.dataset_def.url.uri.endswith(".csv"):
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df = pandas.read_csv(self.dataset_def.url.uri)
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elif self.dataset_def.url.uri.endswith(".xlsx"):
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df = pandas.read_excel(self.dataset_def.url.uri)
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elif self.dataset_def.url.uri.startswith("data:"):
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parts = parse_data_url(self.dataset_def.url.uri)
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data = parts["data"]
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if parts["is_base64"]:
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data = base64.b64decode(data)
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else:
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data = unquote(data)
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encoding = parts["encoding"] or "utf-8"
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data = data.encode(encoding)
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mime_type = parts["mimetype"]
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mime_category = mime_type.split("/")[0]
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data_bytes = io.BytesIO(data)
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if mime_category == "text":
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df = pandas.read_csv(data_bytes)
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else:
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df = pandas.read_excel(data_bytes)
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else:
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raise ValueError(f"Unsupported file type: {self.dataset_def.url}")
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self.df = df
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class MetaReferenceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
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def __init__(self, config: MetaReferenceDatasetIOConfig) -> None:
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self.config = config
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# local registry for keeping track of datasets within the provider
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self.dataset_infos = {}
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async def initialize(self) -> None: ...
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async def shutdown(self) -> None: ...
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async def register_dataset(
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self,
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dataset_def: DatasetDef,
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) -> None:
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dataset_impl = PandasDataframeDataset(dataset_def)
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self.dataset_infos[dataset_def.identifier] = DatasetInfo(
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dataset_def=dataset_def,
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dataset_impl=dataset_impl,
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)
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async def list_datasets(self) -> List[DatasetDef]:
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return [i.dataset_def for i in self.dataset_infos.values()]
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async def get_rows_paginated(
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self,
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dataset_id: str,
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rows_in_page: int,
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page_token: Optional[str] = None,
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filter_condition: Optional[str] = None,
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) -> PaginatedRowsResult:
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dataset_info = self.dataset_infos.get(dataset_id)
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dataset_info.dataset_impl.load()
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if page_token is None:
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next_page_token = 0
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else:
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next_page_token = int(page_token)
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if rows_in_page == -1:
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rows = dataset_info.dataset_impl[next_page_token:]
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start = next_page_token
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end = min(start + rows_in_page, len(dataset_info.dataset_impl))
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rows = dataset_info.dataset_impl[start:end]
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return PaginatedRowsResult(
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rows=rows,
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total_count=len(rows),
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next_page_token=str(end),
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)
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