dataset datasetio

llama_stack/apis/datasetio/datasetio.py

@@ -17,7 +17,7 @@ class PaginatedRowsResult(BaseModel):
     # the rows obey the DatasetSchema for the given dataset
     rows: List[Dict[str, Any]]
     total_count: int
-    next_page_token: Optional[str] = None
+    next_page_token: Optional[int] = None
 
 
 class DatasetStore(Protocol):
@@ -34,6 +34,6 @@ class DatasetIO(Protocol):
         self,
         dataset_id: str,
         rows_in_page: int,
-        page_token: Optional[str] = None,
+        page_token: Optional[int] = None,
         filter_condition: Optional[str] = None,
     ) -> PaginatedRowsResult: ...

llama_stack/distribution/routers/routing_tables.py

@@ -28,6 +28,10 @@ async def register_object_with_provider(obj: RoutableObject, p: Any) -> None:
         await p.register_shield(obj)
     elif api == Api.memory:
         await p.register_memory_bank(obj)
+    elif api == Api.datasetio:
+        await p.register_dataset(obj)
+    else:
+        raise ValueError(f"Unknown API {api} for registering object with provider")
 
 
 Registry = Dict[str, List[RoutableObjectWithProvider]]
@@ -81,6 +85,16 @@ class CommonRoutingTableImpl(RoutingTable):
 
                 add_objects(memory_banks)
 
+            elif api == Api.datasetio:
+                p.dataset_store = self
+                datasets = await p.list_datasets()
+
+                # do in-memory updates due to pesky Annotated unions
+                for d in datasets:
+                    d.provider_id = pid
+
+                add_objects(datasets)
+
     async def shutdown(self) -> None:
         for p in self.impls_by_provider_id.values():
             await p.shutdown()
@@ -138,6 +152,7 @@ class CommonRoutingTableImpl(RoutingTable):
             raise ValueError(f"Provider `{obj.provider_id}` not found")
 
         p = self.impls_by_provider_id[obj.provider_id]
+
         await register_object_with_provider(obj, p)
 
         if obj.identifier not in self.registry:

llama_stack/providers/impls/meta_reference/datasetio/datasetio.py

@@ -5,17 +5,83 @@
 # the root directory of this source tree.
 from typing import List, Optional
 
+import pandas
+
 from llama_models.llama3.api.datatypes import *  # noqa: F403
 
 from llama_stack.apis.datasetio import *  # noqa: F403
+from dataclasses import dataclass
+
 from llama_stack.providers.datatypes import DatasetsProtocolPrivate
+from llama_stack.providers.utils.datasetio.dataset_utils import BaseDataset
 
 from .config import MetaReferenceDatasetIOConfig
 
 
+@dataclass
+class DatasetInfo:
+    dataset_def: DatasetDef
+    dataset_impl: BaseDataset
+    next_page_token: Optional[int] = None
+
+
+class CustomDataset(BaseDataset):
+    def __init__(self, dataset_def: DatasetDef, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.dataset_def = dataset_def
+        # TODO: validate dataset_def against schema
+        self.df = None
+        self.load()
+
+    def __len__(self) -> int:
+        if self.df is None:
+            self.load()
+        return len(self.df)
+
+    def __getitem__(self, idx):
+        if isinstance(idx, slice):
+            return self.df.iloc[idx].to_dict(orient="records")
+        else:
+            return self.df.iloc[idx].to_dict()
+
+    def load(self) -> None:
+        if self.df:
+            return
+
+        # TODO: more robust support w/ data url
+        if self.dataset_def.url.uri.endswith(".csv"):
+            df = pandas.read_csv(self.dataset_def.url.uri)
+        elif self.dataset_def.url.uri.endswith(".xlsx"):
+            df = pandas.read_excel(self.dataset_def.url.uri)
+        elif self.dataset_def.url.uri.startswith("data:"):
+            parts = parse_data_url(self.dataset_def.url.uri)
+            data = parts["data"]
+            if parts["is_base64"]:
+                data = base64.b64decode(data)
+            else:
+                data = unquote(data)
+                encoding = parts["encoding"] or "utf-8"
+                data = data.encode(encoding)
+
+            mime_type = parts["mimetype"]
+            mime_category = mime_type.split("/")[0]
+            data_bytes = io.BytesIO(data)
+
+            if mime_category == "text":
+                df = pandas.read_csv(data_bytes)
+            else:
+                df = pandas.read_excel(data_bytes)
+        else:
+            raise ValueError(f"Unsupported file type: {self.dataset_def.url}")
+
+        self.df = df
+
+
 class MetaReferenceDatasetioImpl(DatasetIO, DatasetsProtocolPrivate):
     def __init__(self, config: MetaReferenceDatasetIOConfig) -> None:
         self.config = config
+        # local registry for keeping track of datasets within the provider
+        self.dataset_infos = {}
 
     async def initialize(self) -> None: ...
 
@@ -23,21 +89,38 @@ class MetaReferenceDatasetioImpl(DatasetIO, DatasetsProtocolPrivate):
 
     async def register_dataset(
         self,
-        memory_bank: DatasetDef,
+        dataset_def: DatasetDef,
     ) -> None:
-        print("register dataset")
+        self.dataset_infos[dataset_def.identifier] = DatasetInfo(
+            dataset_def=dataset_def,
+            dataset_impl=CustomDataset(dataset_def),
+            next_page_token=0,
+        )
 
     async def list_datasets(self) -> List[DatasetDef]:
-        print("list datasets")
-        return []
+        return [i.dataset_def for i in self.dataset_infos.values()]
 
     async def get_rows_paginated(
         self,
         dataset_id: str,
         rows_in_page: int,
-        page_token: Optional[str] = None,
+        page_token: Optional[int] = None,
         filter_condition: Optional[str] = None,
     ) -> PaginatedRowsResult:
-        print("get rows paginated")
+        dataset_info = self.dataset_infos.get(dataset_id)
+        if page_token is None:
+            dataset_info.next_page_token = 0
 
-        return PaginatedRowsResult(rows=[], total_count=1, next_page_token=None)
+        if rows_in_page == -1:
+            rows = dataset_info.dataset_impl[dataset_info.next_page_token :]
+
+        start = dataset_info.next_page_token
+        end = min(start + rows_in_page, len(dataset_info.dataset_impl))
+        rows = dataset_info.dataset_impl[start:end]
+        dataset_info.next_page_token = end
+
+        return PaginatedRowsResult(
+            rows=rows,
+            total_count=len(rows),
+            next_page_token=dataset_info.next_page_token,
+        )

llama_stack/providers/registry/datasetio.py

@@ -14,7 +14,7 @@ def available_providers() -> List[ProviderSpec]:
         InlineProviderSpec(
             api=Api.datasetio,
             provider_type="meta-reference",
-            pip_packages=[],
+            pip_packages=["pandas"],
             module="llama_stack.providers.impls.meta_reference.datasetio",
             config_class="llama_stack.providers.impls.meta_reference.datasetio.MetaReferenceDatasetIOConfig",
             api_dependencies=[],

llama_stack/providers/tests/datasetio/__init__.py

@@ -0,0 +1,5 @@
+# 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.

llama_stack/providers/tests/datasetio/provider_config_example.yaml

@@ -0,0 +1,4 @@
+providers:
+  - provider_id: test-meta
+    provider_type: meta-reference
+    config: {}

llama_stack/providers/tests/datasetio/test_datasetio.py

@@ -0,0 +1,109 @@
+# 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 os
+
+import pytest
+import pytest_asyncio
+
+from llama_stack.apis.datasetio import *  # noqa: F403
+from llama_stack.distribution.datatypes import *  # noqa: F403
+from llama_stack.providers.tests.resolver import resolve_impls_for_test
+
+# How to run this test:
+#
+# 1. Ensure you have a conda with the right dependencies installed. This is a bit tricky
+#    since it depends on the provider you are testing. On top of that you need
+#    `pytest` and `pytest-asyncio` installed.
+#
+# 2. Copy and modify the provider_config_example.yaml depending on the provider you are testing.
+#
+# 3. Run:
+#
+# ```bash
+# PROVIDER_ID=<your_provider> \
+#   PROVIDER_CONFIG=provider_config.yaml \
+#   pytest -s llama_stack/providers/tests/datasetio/test_datasetio.py \
+#   --tb=short --disable-warnings
+# ```
+
+
+@pytest_asyncio.fixture(scope="session")
+async def datasetio_settings():
+    impls = await resolve_impls_for_test(
+        Api.datasetio,
+    )
+    return {
+        "datasetio_impl": impls[Api.datasetio],
+        "datasets_impl": impls[Api.datasets],
+    }
+
+
+async def register_dataset(datasets_impl: Datasets):
+    dataset = DatasetDefWithProvider(
+        identifier="test_dataset",
+        provider_id=os.environ["PROVIDER_ID"],
+        url=URL(
+            uri="https://openaipublic.blob.core.windows.net/simple-evals/mmlu.csv",
+        ),
+        columns_schema={},
+    )
+    await datasets_impl.register_dataset(dataset)
+
+
+@pytest.mark.asyncio
+async def test_datasets_list(datasetio_settings):
+    # NOTE: this needs you to ensure that you are starting from a clean state
+    # but so far we don't have an unregister API unfortunately, so be careful
+    datasets_impl = datasetio_settings["datasets_impl"]
+    response = await datasets_impl.list_datasets()
+    assert isinstance(response, list)
+    assert len(response) == 0
+
+
+@pytest.mark.asyncio
+async def test_datasets_register(datasetio_settings):
+    # NOTE: this needs you to ensure that you are starting from a clean state
+    # but so far we don't have an unregister API unfortunately, so be careful
+    datasets_impl = datasetio_settings["datasets_impl"]
+    await register_dataset(datasets_impl)
+
+    response = await datasets_impl.list_datasets()
+    assert isinstance(response, list)
+    assert len(response) == 1
+
+    # register same dataset with same id again will fail
+    await register_dataset(datasets_impl)
+    response = await datasets_impl.list_datasets()
+    assert isinstance(response, list)
+    assert len(response) == 1
+    assert response[0].identifier == "test_dataset"
+
+
+@pytest.mark.asyncio
+async def test_get_rows_paginated(datasetio_settings):
+    datasetio_impl = datasetio_settings["datasetio_impl"]
+    datasets_impl = datasetio_settings["datasets_impl"]
+    await register_dataset(datasets_impl)
+
+    response = await datasetio_impl.get_rows_paginated(
+        dataset_id="test_dataset",
+        rows_in_page=3,
+    )
+
+    assert isinstance(response.rows, list)
+    assert len(response.rows) == 3
+    assert response.next_page_token == 3
+
+    # iterate over all rows
+    response = await datasetio_impl.get_rows_paginated(
+        dataset_id="test_dataset",
+        rows_in_page=10,
+        page_token=response.next_page_token,
+    )
+
+    assert isinstance(response.rows, list)
+    assert len(response.rows) == 10
+    assert response.next_page_token == 13

llama_stack/providers/utils/datasetio/dataset_utils.py

@@ -0,0 +1,23 @@
+# 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.
+from abc import ABC, abstractmethod
+
+
+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()