local persistent for hf dataset provider (#451)

# What does this PR do?

- local persistence for HF dataset provider
- follow https://github.com/meta-llama/llama-stack/pull/375

## Test Plan

**e2e**
1. fresh llama stack run w/ yaml
2. kill server
3. restart llama stack run w/ yaml

```yaml
datasets:
  - dataset_id: mmlu
    provider_id: huggingface-0
    url:
      uri: https://huggingface.co/datasets/llamastack/evals
    metadata:
      path: llamastack/evals
      name: evals__mmlu__details
      split: train
    dataset_schema:
      input_query:
        type: string
      expected_answer:
        type: string
```
<img width="686" alt="image"
src="https://github.com/user-attachments/assets/d7737931-6a7d-400a-a17d-fef6cbd97eea">


## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

llama_stack/providers/adapters/datasetio/huggingface/config.py

@@ -3,7 +3,15 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-from llama_stack.apis.datasetio import *  # noqa: F401, F403
+from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR
+from llama_stack.providers.utils.kvstore.config import (
+    KVStoreConfig,
+    SqliteKVStoreConfig,
+)
+from pydantic import BaseModel
 
 
-class HuggingfaceDatasetIOConfig(BaseModel): ...
+class HuggingfaceDatasetIOConfig(BaseModel):
+    kvstore: KVStoreConfig = SqliteKVStoreConfig(
+        db_path=(RUNTIME_BASE_DIR / "huggingface_datasetio.db").as_posix()
+    )  # Uses SQLite config specific to HF storage

llama_stack/providers/adapters/datasetio/huggingface/huggingface.py

@@ -11,9 +11,12 @@ from llama_stack.apis.datasetio import *  # noqa: F403
 import datasets as hf_datasets
 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 HuggingfaceDatasetIOConfig
 
+DATASETS_PREFIX = "datasets:"
+
 
 def load_hf_dataset(dataset_def: Dataset):
     if dataset_def.metadata.get("path", None):
@@ -33,9 +36,18 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
         self.config = config
         # local registry for keeping track of datasets within the provider
         self.dataset_infos = {}
+        self.kvstore = None
 
     async def initialize(self) -> None:
-        pass
+        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)
+            self.dataset_infos[dataset.identifier] = dataset
 
     async def shutdown(self) -> None: ...
 
@@ -43,6 +55,12 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
         self,
         dataset_def: Dataset,
     ) -> None:
+        # Store in kvstore
+        key = f"{DATASETS_PREFIX}{dataset_def.identifier}"
+        await self.kvstore.set(
+            key=key,
+            value=dataset_def.json(),
+        )
         self.dataset_infos[dataset_def.identifier] = dataset_def
 
     async def get_rows_paginated(