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feat: Implement FastAPI router system
This commit introduces a new FastAPI router-based system for defining API endpoints, enabling a migration path away from the legacy @webmethod decorator system. The implementation includes router infrastructure, migration of the Batches API as the first example, and updates to server, OpenAPI generation, and inspection systems to support both routing approaches. The router infrastructure consists of a router registry system that allows APIs to register FastAPI router factories, which are then automatically discovered and included in the server application. Standard error responses are centralized in router_utils to ensure consistent OpenAPI specification generation with proper $ref references to component responses. The Batches API has been migrated to demonstrate the new pattern. The protocol definition and models remain in llama_stack_api/batches, maintaining clear separation between API contracts and server implementation. The FastAPI router implementation lives in llama_stack/core/server/routers/batches, following the established pattern where API contracts are defined in llama_stack_api and server routing logic lives in llama_stack/core/server. The server now checks for registered routers before falling back to the legacy webmethod-based route discovery, ensuring backward compatibility during the migration period. The OpenAPI generator has been updated to handle both router-based and webmethod-based routes, correctly extracting metadata from FastAPI route decorators and Pydantic Field descriptions. The inspect endpoint now includes routes from both systems, with proper filtering for deprecated routes and API levels. Response descriptions are now explicitly defined in router decorators, ensuring the generated OpenAPI specification matches the previous format. Error responses use $ref references to component responses (BadRequest400, TooManyRequests429, etc.) as required by the specification. This is neat and will allow us to remove a lot of boiler plate code from our generator once the migration is done. This implementation provides a foundation for incrementally migrating other APIs to the router system while maintaining full backward compatibility with existing webmethod-based APIs. Closes: https://github.com/llamastack/llama-stack/issues/4188 Signed-off-by: Sébastien Han <seb@redhat.com>
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16 changed files with 604 additions and 123 deletions
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# 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 typing import Literal, Protocol, runtime_checkable
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from pydantic import BaseModel, Field
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from llama_stack_api.schema_utils import json_schema_type, webmethod
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from llama_stack_api.version import LLAMA_STACK_API_V1
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try:
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from openai.types import Batch as BatchObject
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except ImportError as e:
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raise ImportError("OpenAI package is required for batches API. Please install it with: pip install openai") from e
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@json_schema_type
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class ListBatchesResponse(BaseModel):
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"""Response containing a list of batch objects."""
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object: Literal["list"] = "list"
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data: list[BatchObject] = Field(..., description="List of batch objects")
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first_id: str | None = Field(default=None, description="ID of the first batch in the list")
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last_id: str | None = Field(default=None, description="ID of the last batch in the list")
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has_more: bool = Field(default=False, description="Whether there are more batches available")
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@runtime_checkable
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class Batches(Protocol):
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"""
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The Batches API enables efficient processing of multiple requests in a single operation,
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particularly useful for processing large datasets, batch evaluation workflows, and
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cost-effective inference at scale.
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The API is designed to allow use of openai client libraries for seamless integration.
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This API provides the following extensions:
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- idempotent batch creation
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Note: This API is currently under active development and may undergo changes.
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"""
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@webmethod(route="/batches", method="POST", level=LLAMA_STACK_API_V1)
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async def create_batch(
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self,
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input_file_id: str,
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endpoint: str,
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completion_window: Literal["24h"],
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metadata: dict[str, str] | None = None,
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idempotency_key: str | None = None,
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) -> BatchObject:
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"""Create a new batch for processing multiple API requests.
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:param input_file_id: The ID of an uploaded file containing requests for the batch.
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:param endpoint: The endpoint to be used for all requests in the batch.
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:param completion_window: The time window within which the batch should be processed.
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:param metadata: Optional metadata for the batch.
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:param idempotency_key: Optional idempotency key. When provided, enables idempotent behavior.
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:returns: The created batch object.
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"""
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...
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@webmethod(route="/batches/{batch_id}", method="GET", level=LLAMA_STACK_API_V1)
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async def retrieve_batch(self, batch_id: str) -> BatchObject:
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"""Retrieve information about a specific batch.
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:param batch_id: The ID of the batch to retrieve.
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:returns: The batch object.
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"""
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...
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@webmethod(route="/batches/{batch_id}/cancel", method="POST", level=LLAMA_STACK_API_V1)
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async def cancel_batch(self, batch_id: str) -> BatchObject:
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"""Cancel a batch that is in progress.
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:param batch_id: The ID of the batch to cancel.
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:returns: The updated batch object.
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"""
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...
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@webmethod(route="/batches", method="GET", level=LLAMA_STACK_API_V1)
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async def list_batches(
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self,
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after: str | None = None,
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limit: int = 20,
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) -> ListBatchesResponse:
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"""List all batches for the current user.
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:param after: A cursor for pagination; returns batches after this batch ID.
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:param limit: Number of batches to return (default 20, max 100).
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:returns: A list of batch objects.
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"""
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...
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