llama-stack-mirror/llama_stack/providers/inline/datasetio/localfs/datasetio.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import base64
import os
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
from urllib.parse import urlparse

import pandas

from llama_stack.apis.common.content_types import URL
from llama_stack.apis.datasetio import DatasetIO, PaginatedRowsResult
from llama_stack.apis.datasets import Dataset
from llama_stack.providers.datatypes import DatasetsProtocolPrivate
from llama_stack.providers.utils.datasetio.url_utils import get_dataframe_from_url
from llama_stack.providers.utils.kvstore import kvstore_impl

from .config import LocalFSDatasetIOConfig

DATASETS_PREFIX = "localfs_datasets:"


class BaseDataset(ABC):
    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)

    @abstractmethod
    def __len__(self) -> int:
        raise NotImplementedError()

    @abstractmethod
    def __getitem__(self, idx):
        raise NotImplementedError()

    @abstractmethod
    def load(self):
        raise NotImplementedError()


@dataclass
class DatasetInfo:
    dataset_def: Dataset
    dataset_impl: BaseDataset


class PandasDataframeDataset(BaseDataset):
    def __init__(self, dataset_def: Dataset, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)
        self.dataset_def = dataset_def
        self.df = None

    def __len__(self) -> int:
        assert self.df is not None, "Dataset not loaded. Please call .load() first"
        return len(self.df)

    def __getitem__(self, idx):
        assert self.df is not None, "Dataset not loaded. Please call .load() first"
        if isinstance(idx, slice):
            return self.df.iloc[idx].to_dict(orient="records")
        else:
            return self.df.iloc[idx].to_dict()

    def _validate_dataset_schema(self, df) -> pandas.DataFrame:
        # note that we will drop any columns in dataset that are not in the schema
        df = df[self.dataset_def.dataset_schema.keys()]
        # check all columns in dataset schema are present
        assert len(df.columns) == len(self.dataset_def.dataset_schema)
        # TODO: type checking against column types in dataset schema
        return df

    def load(self) -> None:
        if self.df is not None:
            return

        df = get_dataframe_from_url(self.dataset_def.url)
        if df is None:
            raise ValueError(f"Failed to load dataset from {self.dataset_def.url}")

        self.df = self._validate_dataset_schema(df)


class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
    def __init__(self, config: LocalFSDatasetIOConfig) -> None:
        self.config = config
        # local registry for keeping track of datasets within the provider
        self.dataset_infos = {}
        self.kvstore = None

    async def initialize(self) -> None:
        self.kvstore = await kvstore_impl(self.config.kvstore)
        # Load existing datasets from kvstore
        start_key = DATASETS_PREFIX
        end_key = f"{DATASETS_PREFIX}\xff"
        stored_datasets = await self.kvstore.range(start_key, end_key)

        for dataset in stored_datasets:
            dataset = Dataset.model_validate_json(dataset)
            dataset_impl = PandasDataframeDataset(dataset)
            self.dataset_infos[dataset.identifier] = DatasetInfo(
                dataset_def=dataset,
                dataset_impl=dataset_impl,
            )

    async def shutdown(self) -> None: ...

    async def register_dataset(
        self,
        dataset: Dataset,
    ) -> None:
        # Store in kvstore
        key = f"{DATASETS_PREFIX}{dataset.identifier}"
        await self.kvstore.set(
            key=key,
            value=dataset.json(),
        )
        dataset_impl = PandasDataframeDataset(dataset)
        self.dataset_infos[dataset.identifier] = DatasetInfo(
            dataset_def=dataset,
            dataset_impl=dataset_impl,
        )

    async def unregister_dataset(self, dataset_id: str) -> None:
        key = f"{DATASETS_PREFIX}{dataset_id}"
        await self.kvstore.delete(key=key)
        del self.dataset_infos[dataset_id]

    async def get_rows_paginated(
        self,
        dataset_id: str,
        rows_in_page: int,
        page_token: Optional[str] = None,
        filter_condition: Optional[str] = None,
    ) -> PaginatedRowsResult:
        dataset_info = self.dataset_infos.get(dataset_id)
        dataset_info.dataset_impl.load()

        if page_token and not page_token.isnumeric():
            raise ValueError("Invalid page_token")

        if page_token is None or len(page_token) == 0:
            next_page_token = 0
        else:
            next_page_token = int(page_token)

        start = next_page_token
        if rows_in_page == -1:
            end = len(dataset_info.dataset_impl)
        else:
            end = min(start + rows_in_page, len(dataset_info.dataset_impl))

        rows = dataset_info.dataset_impl[start:end]

        return PaginatedRowsResult(
            rows=rows,
            total_count=len(rows),
            next_page_token=str(end),
        )

    async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None:
        dataset_info = self.dataset_infos.get(dataset_id)
        if dataset_info is None:
            raise ValueError(f"Dataset with id {dataset_id} not found")

        dataset_impl = dataset_info.dataset_impl
        dataset_impl.load()

        new_rows_df = pandas.DataFrame(rows)
        new_rows_df = dataset_impl._validate_dataset_schema(new_rows_df)
        dataset_impl.df = pandas.concat([dataset_impl.df, new_rows_df], ignore_index=True)

        url = str(dataset_info.dataset_def.url)
        parsed_url = urlparse(url)

        if parsed_url.scheme == "file" or not parsed_url.scheme:
            file_path = parsed_url.path
            os.makedirs(os.path.dirname(file_path), exist_ok=True)
            dataset_impl.df.to_csv(file_path, index=False)
        elif parsed_url.scheme == "data":
            # For data URLs, we need to update the base64-encoded content
            if not parsed_url.path.startswith("text/csv;base64,"):
                raise ValueError("Data URL must be a base64-encoded CSV")

            csv_buffer = dataset_impl.df.to_csv(index=False)
            base64_content = base64.b64encode(csv_buffer.encode("utf-8")).decode("utf-8")
            dataset_info.dataset_def.url = URL(uri=f"data:text/csv;base64,{base64_content}")
        else:
            raise ValueError(
                f"Unsupported URL scheme: {parsed_url.scheme}. Only file:// and data: URLs are supported for writing."
            )