forked from phoenix-oss/llama-stack-mirror
111 lines
4 KiB
Python
111 lines
4 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 Any
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import pandas
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from llama_stack.apis.common.responses import PaginatedResponse
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from llama_stack.apis.datasetio import DatasetIO
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from llama_stack.apis.datasets import Dataset
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from llama_stack.providers.datatypes import DatasetsProtocolPrivate
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from llama_stack.providers.utils.datasetio.url_utils import get_dataframe_from_uri
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from llama_stack.providers.utils.kvstore import kvstore_impl
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from llama_stack.providers.utils.pagination import paginate_records
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from .config import LocalFSDatasetIOConfig
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DATASETS_PREFIX = "localfs_datasets:"
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class PandasDataframeDataset:
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def __init__(self, dataset_def: Dataset, *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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assert self.df is not None, "Dataset not loaded. Please call .load() first"
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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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async def load(self) -> None:
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if self.df is not None:
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return
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if self.dataset_def.source.type == "uri":
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self.df = await get_dataframe_from_uri(self.dataset_def.source.uri)
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elif self.dataset_def.source.type == "rows":
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self.df = pandas.DataFrame(self.dataset_def.source.rows)
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else:
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raise ValueError(f"Unsupported dataset source type: {self.dataset_def.source.type}")
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if self.df is None:
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raise ValueError(f"Failed to load dataset from {self.dataset_def.url}")
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class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
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def __init__(self, config: LocalFSDatasetIOConfig) -> 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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self.kvstore = None
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async def initialize(self) -> None:
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self.kvstore = await kvstore_impl(self.config.kvstore)
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# Load existing datasets from kvstore
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start_key = DATASETS_PREFIX
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end_key = f"{DATASETS_PREFIX}\xff"
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stored_datasets = await self.kvstore.values_in_range(start_key, end_key)
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for dataset in stored_datasets:
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dataset = Dataset.model_validate_json(dataset)
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self.dataset_infos[dataset.identifier] = dataset
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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: Dataset,
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) -> None:
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# Store in kvstore
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key = f"{DATASETS_PREFIX}{dataset_def.identifier}"
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await self.kvstore.set(
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key=key,
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value=dataset_def.model_dump_json(),
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)
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self.dataset_infos[dataset_def.identifier] = dataset_def
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async def unregister_dataset(self, dataset_id: str) -> None:
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key = f"{DATASETS_PREFIX}{dataset_id}"
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await self.kvstore.delete(key=key)
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del self.dataset_infos[dataset_id]
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async def iterrows(
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self,
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dataset_id: str,
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start_index: int | None = None,
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limit: int | None = None,
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) -> PaginatedResponse:
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dataset_def = self.dataset_infos[dataset_id]
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dataset_impl = PandasDataframeDataset(dataset_def)
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await dataset_impl.load()
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records = dataset_impl.df.to_dict("records")
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return paginate_records(records, start_index, limit)
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async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None:
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dataset_def = self.dataset_infos[dataset_id]
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dataset_impl = PandasDataframeDataset(dataset_def)
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await dataset_impl.load()
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new_rows_df = pandas.DataFrame(rows)
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dataset_impl.df = pandas.concat([dataset_impl.df, new_rows_df], ignore_index=True)
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