llama-stack-mirror/llama_stack/apis/models/models.py
Charlie Doern 03399cebf3 feat: introduce API leveling, post_training to v1alpha
Rather than have a single `LLAMA_STACK_VERSION`, we need to have a `_V1`, `_V1ALPHA`, and `_V1BETA` constant.

This also necessitated addition of `level` to the `WebMethod` so that routing can be handeled properly.

For backwards compat, the `v1` routes are being kept around and marked as `deprecated`. When used, the server will log a deprecation warning.

move:

post_training to v1alpha as it is under heavy development and not near its final state
eval: job scheduling is not implemented. Relies heavily on the datasetio API which is under development missing implementations of specific routes indicating the structure of those routes might change. Additionally eval depends on the inference API which is going to be deprecated, eval will likely need a major API surface change to conform to using completions properly

Signed-off-by: Charlie Doern <cdoern@redhat.com>
2025-09-26 09:42:57 -04:00

163 lines
4.9 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.resource import Resource, ResourceType
from llama_stack.apis.version import LLAMA_STACK_API_V1
from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
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
"""
llm = "llm"
embedding = "embedding"
@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
"""
id: str
object: Literal["model"] = "model"
created: int
owned_by: str
class OpenAIListModelsResponse(BaseModel):
data: list[OpenAIModel]
@runtime_checkable
@trace_protocol
class Models(Protocol):
@webmethod(route="/models", method="GET", level=LLAMA_STACK_API_V1)
async def list_models(self) -> ListModelsResponse:
"""List all models.
:returns: A ListModelsResponse.
"""
...
@webmethod(route="/openai/v1/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 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)
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 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)
async def unregister_model(
self,
model_id: str,
) -> None:
"""Unregister a model.
:param model_id: The identifier of the model to unregister.
"""
...