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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>
82 lines
3.1 KiB
Python
82 lines
3.1 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 time
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from typing import Any
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from llama_stack.apis.models import ListModelsResponse, Model, Models, ModelType, OpenAIListModelsResponse, OpenAIModel
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from llama_stack.distribution.datatypes import (
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ModelWithOwner,
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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 ModelsRoutingTable(CommonRoutingTableImpl, Models):
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async def list_models(self) -> ListModelsResponse:
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return ListModelsResponse(data=await self.get_all_with_type("model"))
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async def openai_list_models(self) -> OpenAIListModelsResponse:
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models = await self.get_all_with_type("model")
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openai_models = [
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OpenAIModel(
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id=model.identifier,
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object="model",
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created=int(time.time()),
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owned_by="llama_stack",
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)
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for model in models
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]
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return OpenAIListModelsResponse(data=openai_models)
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async def get_model(self, model_id: str) -> Model:
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model = await self.get_object_by_identifier("model", model_id)
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if model is None:
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raise ValueError(f"Model '{model_id}' not found")
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return model
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async def register_model(
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self,
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model_id: str,
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provider_model_id: str | None = None,
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provider_id: str | None = None,
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metadata: dict[str, Any] | None = None,
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model_type: ModelType | None = None,
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) -> Model:
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if provider_model_id is None:
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provider_model_id = model_id
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if provider_id is None:
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# If provider_id not specified, use the only provider if it supports this model
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if len(self.impls_by_provider_id) == 1:
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provider_id = list(self.impls_by_provider_id.keys())[0]
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else:
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raise ValueError(
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f"No provider specified and multiple providers available. Please specify a provider_id. Available providers: {self.impls_by_provider_id.keys()}"
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)
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if metadata is None:
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metadata = {}
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if model_type is None:
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model_type = ModelType.llm
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if "embedding_dimension" not in metadata and model_type == ModelType.embedding:
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raise ValueError("Embedding model must have an embedding dimension in its metadata")
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model = ModelWithOwner(
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identifier=model_id,
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provider_resource_id=provider_model_id,
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provider_id=provider_id,
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metadata=metadata,
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model_type=model_type,
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)
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registered_model = await self.register_object(model)
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return registered_model
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async def unregister_model(self, model_id: str) -> None:
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existing_model = await self.get_model(model_id)
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if existing_model is None:
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raise ValueError(f"Model {model_id} not found")
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await self.unregister_object(existing_model)
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