mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-10-25 09:05:37 +00:00
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.
|
|
# 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 time
|
|
from typing import Any
|
|
|
|
from llama_stack.apis.models import ListModelsResponse, Model, Models, ModelType, OpenAIListModelsResponse, OpenAIModel
|
|
from llama_stack.distribution.datatypes import (
|
|
ModelWithOwner,
|
|
)
|
|
from llama_stack.log import get_logger
|
|
|
|
from .common import CommonRoutingTableImpl
|
|
|
|
logger = get_logger(name=__name__, category="core")
|
|
|
|
|
|
class ModelsRoutingTable(CommonRoutingTableImpl, Models):
|
|
async def list_models(self) -> ListModelsResponse:
|
|
return ListModelsResponse(data=await self.get_all_with_type("model"))
|
|
|
|
async def openai_list_models(self) -> OpenAIListModelsResponse:
|
|
models = await self.get_all_with_type("model")
|
|
openai_models = [
|
|
OpenAIModel(
|
|
id=model.identifier,
|
|
object="model",
|
|
created=int(time.time()),
|
|
owned_by="llama_stack",
|
|
)
|
|
for model in models
|
|
]
|
|
return OpenAIListModelsResponse(data=openai_models)
|
|
|
|
async def get_model(self, model_id: str) -> Model:
|
|
model = await self.get_object_by_identifier("model", model_id)
|
|
if model is None:
|
|
raise ValueError(f"Model '{model_id}' not found")
|
|
return model
|
|
|
|
async def register_model(
|
|
self,
|
|
model_id: str,
|
|
provider_model_id: str | None = None,
|
|
provider_id: str | None = None,
|
|
metadata: dict[str, Any] | None = None,
|
|
model_type: ModelType | None = None,
|
|
) -> Model:
|
|
if provider_model_id is None:
|
|
provider_model_id = model_id
|
|
if provider_id is None:
|
|
# If provider_id not specified, use the only provider if it supports this model
|
|
if len(self.impls_by_provider_id) == 1:
|
|
provider_id = list(self.impls_by_provider_id.keys())[0]
|
|
else:
|
|
raise ValueError(
|
|
f"No provider specified and multiple providers available. Please specify a provider_id. Available providers: {self.impls_by_provider_id.keys()}"
|
|
)
|
|
if metadata is None:
|
|
metadata = {}
|
|
if model_type is None:
|
|
model_type = ModelType.llm
|
|
if "embedding_dimension" not in metadata and model_type == ModelType.embedding:
|
|
raise ValueError("Embedding model must have an embedding dimension in its metadata")
|
|
model = ModelWithOwner(
|
|
identifier=model_id,
|
|
provider_resource_id=provider_model_id,
|
|
provider_id=provider_id,
|
|
metadata=metadata,
|
|
model_type=model_type,
|
|
)
|
|
registered_model = await self.register_object(model)
|
|
return registered_model
|
|
|
|
async def unregister_model(self, model_id: str) -> None:
|
|
existing_model = await self.get_model(model_id)
|
|
if existing_model is None:
|
|
raise ValueError(f"Model {model_id} not found")
|
|
await self.unregister_object(existing_model)
|