llama-stack-mirror/src/llama_stack/apis/models/models.py
Ashwin Bharambe fadf17daf3
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feat(api)!: deprecate register/unregister resource APIs (#4099)
Mark all register_* / unregister_* APIs as deprecated across models,
shields, tool groups, datasets, benchmarks, and scoring functions. This
is the first step toward moving resource mutations to an `/admin`
namespace as outlined in
https://github.com/llamastack/llama-stack/issues/3809#issuecomment-3492931585.

The deprecation flag will be reflected in the OpenAPI schema to warn API
users that these endpoints are being phased out. Next step will be
implementing the `/admin` route namespace for these resource management
operations.

- `register_model` / `unregister_model`
- `register_shield` / `unregister_shield`
- `register_tool_group` / `unregister_toolgroup`
- `register_dataset` / `unregister_dataset`
- `register_benchmark` / `unregister_benchmark`
- `register_scoring_function` / `unregister_scoring_function`
2025-11-10 10:36:33 -08:00

172 lines
5.1 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.
from enum import StrEnum
from typing import Any, Literal, Protocol, runtime_checkable
from pydantic import BaseModel, ConfigDict, Field, field_validator
from llama_stack.apis.common.tracing import telemetry_traceable
from llama_stack.apis.resource import Resource, ResourceType
from llama_stack.apis.version import LLAMA_STACK_API_V1
from llama_stack.schema_utils import json_schema_type, webmethod
class CommonModelFields(BaseModel):
metadata: dict[str, Any] = Field(
default_factory=dict,
description="Any additional metadata for this model",
)
@json_schema_type
class ModelType(StrEnum):
"""Enumeration of supported model types in Llama Stack.
:cvar llm: Large language model for text generation and completion
:cvar embedding: Embedding model for converting text to vector representations
:cvar rerank: Reranking model for reordering documents based on their relevance to a query
"""
llm = "llm"
embedding = "embedding"
rerank = "rerank"
@json_schema_type
class Model(CommonModelFields, Resource):
"""A model resource representing an AI model registered in Llama Stack.
:param type: The resource type, always 'model' for model resources
:param model_type: The type of model (LLM or embedding model)
:param metadata: Any additional metadata for this model
:param identifier: Unique identifier for this resource in llama stack
:param provider_resource_id: Unique identifier for this resource in the provider
:param provider_id: ID of the provider that owns this resource
"""
type: Literal[ResourceType.model] = ResourceType.model
@property
def model_id(self) -> str:
return self.identifier
@property
def provider_model_id(self) -> str:
assert self.provider_resource_id is not None, "Provider resource ID must be set"
return self.provider_resource_id
model_config = ConfigDict(protected_namespaces=())
model_type: ModelType = Field(default=ModelType.llm)
@field_validator("provider_resource_id")
@classmethod
def validate_provider_resource_id(cls, v):
if v is None:
raise ValueError("provider_resource_id cannot be None")
return v
class ModelInput(CommonModelFields):
model_id: str
provider_id: str | None = None
provider_model_id: str | None = None
model_type: ModelType | None = ModelType.llm
model_config = ConfigDict(protected_namespaces=())
class ListModelsResponse(BaseModel):
data: list[Model]
@json_schema_type
class OpenAIModel(BaseModel):
"""A model from OpenAI.
:id: The ID of the model
:object: The object type, which will be "model"
:created: The Unix timestamp in seconds when the model was created
:owned_by: The owner of the model
:custom_metadata: Llama Stack-specific metadata including model_type, provider info, and additional metadata
"""
id: str
object: Literal["model"] = "model"
created: int
owned_by: str
custom_metadata: dict[str, Any] | None = None
class OpenAIListModelsResponse(BaseModel):
data: list[OpenAIModel]
@runtime_checkable
@telemetry_traceable
class Models(Protocol):
async def list_models(self) -> ListModelsResponse:
"""List all models.
:returns: A ListModelsResponse.
"""
...
@webmethod(route="/models", method="GET", level=LLAMA_STACK_API_V1)
async def openai_list_models(self) -> OpenAIListModelsResponse:
"""List models using the OpenAI API.
:returns: A OpenAIListModelsResponse.
"""
...
@webmethod(route="/models/{model_id:path}", method="GET", level=LLAMA_STACK_API_V1)
async def get_model(
self,
model_id: str,
) -> Model:
"""Get model.
Get a model by its identifier.
:param model_id: The identifier of the model to get.
:returns: A Model.
"""
...
@webmethod(route="/models", method="POST", level=LLAMA_STACK_API_V1, deprecated=True)
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:
"""Register model.
Register a model.
:param model_id: The identifier of the model to register.
:param provider_model_id: The identifier of the model in the provider.
:param provider_id: The identifier of the provider.
:param metadata: Any additional metadata for this model.
:param model_type: The type of model to register.
:returns: A Model.
"""
...
@webmethod(route="/models/{model_id:path}", method="DELETE", level=LLAMA_STACK_API_V1, deprecated=True)
async def unregister_model(
self,
model_id: str,
) -> None:
"""Unregister model.
Unregister a model.
:param model_id: The identifier of the model to unregister.
"""
...