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This PR focuses on improving the developer experience by adding comprehensive docstrings to the API data models across the Llama Stack. These docstrings provide detailed explanations for each model and its fields, making the API easier to understand and use. **Key changes:** - **Added Docstrings:** Added reST formatted docstrings to Pydantic models in the `llama_stack/apis/` directory. This includes models for: - Agents (`agents.py`) - Benchmarks (`benchmarks.py`) - Datasets (`datasets.py`) - Inference (`inference.py`) - And many other API modules. - **OpenAPI Spec Update:** Regenerated the OpenAPI specification (`docs/_static/llama-stack-spec.yaml` and `docs/_static/llama-stack-spec.html`) to include the new docstrings. This will be reflected in the API documentation, providing richer information to users. **Impact:** - Developers using the Llama Stack API will have a better understanding of the data structures. - The auto-generated API documentation is now more informative. --------- Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
162 lines
4.7 KiB
Python
162 lines
4.7 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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from enum import StrEnum
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from typing import Any, Literal, Protocol, runtime_checkable
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from pydantic import BaseModel, ConfigDict, Field, field_validator
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from llama_stack.apis.resource import Resource, ResourceType
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from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
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from llama_stack.schema_utils import json_schema_type, webmethod
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class CommonModelFields(BaseModel):
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metadata: dict[str, Any] = Field(
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default_factory=dict,
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description="Any additional metadata for this model",
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)
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@json_schema_type
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class ModelType(StrEnum):
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"""Enumeration of supported model types in Llama Stack.
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:cvar llm: Large language model for text generation and completion
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:cvar embedding: Embedding model for converting text to vector representations
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"""
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llm = "llm"
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embedding = "embedding"
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@json_schema_type
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class Model(CommonModelFields, Resource):
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"""A model resource representing an AI model registered in Llama Stack.
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:param type: The resource type, always 'model' for model resources
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:param model_type: The type of model (LLM or embedding model)
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:param metadata: Any additional metadata for this model
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:param identifier: Unique identifier for this resource in llama stack
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:param provider_resource_id: Unique identifier for this resource in the provider
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:param provider_id: ID of the provider that owns this resource
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"""
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type: Literal[ResourceType.model] = ResourceType.model
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@property
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def model_id(self) -> str:
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return self.identifier
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@property
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def provider_model_id(self) -> str:
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assert self.provider_resource_id is not None, "Provider resource ID must be set"
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return self.provider_resource_id
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model_config = ConfigDict(protected_namespaces=())
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model_type: ModelType = Field(default=ModelType.llm)
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@field_validator("provider_resource_id")
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@classmethod
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def validate_provider_resource_id(cls, v):
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if v is None:
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raise ValueError("provider_resource_id cannot be None")
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return v
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class ModelInput(CommonModelFields):
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model_id: str
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provider_id: str | None = None
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provider_model_id: str | None = None
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model_type: ModelType | None = ModelType.llm
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model_config = ConfigDict(protected_namespaces=())
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class ListModelsResponse(BaseModel):
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data: list[Model]
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@json_schema_type
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class OpenAIModel(BaseModel):
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"""A model from OpenAI.
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:id: The ID of the model
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:object: The object type, which will be "model"
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:created: The Unix timestamp in seconds when the model was created
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:owned_by: The owner of the model
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"""
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id: str
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object: Literal["model"] = "model"
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created: int
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owned_by: str
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class OpenAIListModelsResponse(BaseModel):
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data: list[OpenAIModel]
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@runtime_checkable
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@trace_protocol
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class Models(Protocol):
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@webmethod(route="/models", method="GET")
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async def list_models(self) -> ListModelsResponse:
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"""List all models.
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:returns: A ListModelsResponse.
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"""
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...
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@webmethod(route="/openai/v1/models", method="GET")
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async def openai_list_models(self) -> OpenAIListModelsResponse:
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"""List models using the OpenAI API.
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:returns: A OpenAIListModelsResponse.
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"""
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...
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@webmethod(route="/models/{model_id:path}", method="GET")
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async def get_model(
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self,
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model_id: str,
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) -> Model:
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"""Get a model by its identifier.
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:param model_id: The identifier of the model to get.
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:returns: A Model.
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"""
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...
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@webmethod(route="/models", method="POST")
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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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"""Register a model.
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:param model_id: The identifier of the model to register.
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:param provider_model_id: The identifier of the model in the provider.
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:param provider_id: The identifier of the provider.
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:param metadata: Any additional metadata for this model.
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:param model_type: The type of model to register.
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:returns: A Model.
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"""
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...
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@webmethod(route="/models/{model_id:path}", method="DELETE")
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async def unregister_model(
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self,
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model_id: str,
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) -> None:
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"""Unregister a model.
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:param model_id: The identifier of the model to unregister.
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"""
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...
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