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7c1998db25
This allows a set of rules to be defined for determining access to
resources. The rules are (loosely) based on the cedar policy format.
A rule defines a list of action either to permit or to forbid. It may
specify a principal or a resource that must match for the rule to take
effect. It may also specify a condition, either a 'when' or an 'unless',
with additional constraints as to where the rule applies.
A list of rules is held for each type to be protected and tried in order
to find a match. If a match is found, the request is permitted or
forbidden depening on the type of rule. If no match is found, the
request is denied. If no rules are specified for a given type, a rule
that allows any action as long as the resource attributes match the user
attributes is added (i.e. the previous behaviour is the default.
Some examples in yaml:
```
model:
- permit:
principal: user-1
actions: [create, read, delete]
comment: user-1 has full access to all models
- permit:
principal: user-2
actions: [read]
resource: model-1
comment: user-2 has read access to model-1 only
- permit:
actions: [read]
when:
user_in: resource.namespaces
comment: any user has read access to models with matching attributes
vector_db:
- forbid:
actions: [create, read, delete]
unless:
user_in: role::admin
comment: only user with admin role can use vector_db resources
```
---------
Signed-off-by: Gordon Sim <gsim@redhat.com>
93 lines
2.9 KiB
Python
93 lines
2.9 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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import uuid
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from typing import Any
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from llama_stack.apis.datasets import (
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Dataset,
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DatasetPurpose,
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Datasets,
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DatasetType,
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DataSource,
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ListDatasetsResponse,
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RowsDataSource,
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URIDataSource,
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)
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from llama_stack.apis.resource import ResourceType
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from llama_stack.distribution.datatypes import (
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DatasetWithOwner,
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)
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from llama_stack.log import get_logger
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from .common import CommonRoutingTableImpl
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logger = get_logger(name=__name__, category="core")
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class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
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async def list_datasets(self) -> ListDatasetsResponse:
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return ListDatasetsResponse(data=await self.get_all_with_type(ResourceType.dataset.value))
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async def get_dataset(self, dataset_id: str) -> Dataset:
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dataset = await self.get_object_by_identifier("dataset", dataset_id)
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if dataset is None:
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raise ValueError(f"Dataset '{dataset_id}' not found")
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return dataset
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async def register_dataset(
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self,
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purpose: DatasetPurpose,
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source: DataSource,
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metadata: dict[str, Any] | None = None,
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dataset_id: str | None = None,
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) -> Dataset:
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if isinstance(source, dict):
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if source["type"] == "uri":
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source = URIDataSource.parse_obj(source)
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elif source["type"] == "rows":
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source = RowsDataSource.parse_obj(source)
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if not dataset_id:
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dataset_id = f"dataset-{str(uuid.uuid4())}"
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provider_dataset_id = dataset_id
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# infer provider from source
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if metadata:
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if metadata.get("provider_id"):
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provider_id = metadata.get("provider_id") # pass through from nvidia datasetio
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elif source.type == DatasetType.rows.value:
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provider_id = "localfs"
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elif source.type == DatasetType.uri.value:
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# infer provider from uri
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if source.uri.startswith("huggingface"):
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provider_id = "huggingface"
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else:
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provider_id = "localfs"
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else:
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raise ValueError(f"Unknown data source type: {source.type}")
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if metadata is None:
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metadata = {}
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dataset = DatasetWithOwner(
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identifier=dataset_id,
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provider_resource_id=provider_dataset_id,
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provider_id=provider_id,
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purpose=purpose,
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source=source,
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metadata=metadata,
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)
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await self.register_object(dataset)
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return dataset
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async def unregister_dataset(self, dataset_id: str) -> None:
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dataset = await self.get_dataset(dataset_id)
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if dataset is None:
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raise ValueError(f"Dataset {dataset_id} not found")
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await self.unregister_object(dataset)
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