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https://github.com/meta-llama/llama-stack.git
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c029fbcd13
# What does this PR do? - Removed Optional return types for GET methods - Raised ValueError when requested resource is not found - Ensures proper 4xx response for missing resources - Updated the API generator to check for wrong signatures ``` $ uv run --with ".[dev]" ./docs/openapi_generator/run_openapi_generator.sh Validating API method return types... API Method Return Type Validation Errors: Method ScoringFunctions.get_scoring_function returns Optional type ``` Closes: https://github.com/meta-llama/llama-stack/issues/1630 ## Test Plan Run the server then: ``` curl http://127.0.0.1:8321/v1/models/foo {"detail":"Invalid value: Model 'foo' not found"}% ``` Server log: ``` INFO: 127.0.0.1:52307 - "GET /v1/models/foo HTTP/1.1" 400 Bad Request 09:51:42.654 [END] /v1/models/foo [StatusCode.OK] (134.65ms) 09:51:42.651 [ERROR] Error executing endpoint route='/v1/models/{model_id:path}' method='get' Traceback (most recent call last): File "/Users/leseb/Documents/AI/llama-stack/llama_stack/distribution/server/server.py", line 193, in endpoint return await maybe_await(value) File "/Users/leseb/Documents/AI/llama-stack/llama_stack/distribution/server/server.py", line 156, in maybe_await return await value File "/Users/leseb/Documents/AI/llama-stack/llama_stack/providers/utils/telemetry/trace_protocol.py", line 102, in async_wrapper result = await method(self, *args, **kwargs) File "/Users/leseb/Documents/AI/llama-stack/llama_stack/distribution/routers/routing_tables.py", line 217, in get_model raise ValueError(f"Model '{model_id}' not found") ValueError: Model 'foo' not found ``` Signed-off-by: Sébastien Han <seb@redhat.com>
66 lines
1.9 KiB
Python
66 lines
1.9 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 typing import Any, Dict, List, Literal, Optional, Protocol, runtime_checkable
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from pydantic import BaseModel, Field
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from llama_stack.apis.resource import Resource, ResourceType
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from llama_stack.schema_utils import json_schema_type, webmethod
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class CommonBenchmarkFields(BaseModel):
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dataset_id: str
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scoring_functions: List[str]
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metadata: Dict[str, Any] = Field(
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default_factory=dict,
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description="Metadata for this evaluation task",
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)
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@json_schema_type
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class Benchmark(CommonBenchmarkFields, Resource):
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type: Literal[ResourceType.benchmark.value] = ResourceType.benchmark.value
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@property
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def benchmark_id(self) -> str:
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return self.identifier
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@property
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def provider_benchmark_id(self) -> str:
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return self.provider_resource_id
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class BenchmarkInput(CommonBenchmarkFields, BaseModel):
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benchmark_id: str
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provider_id: Optional[str] = None
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provider_benchmark_id: Optional[str] = None
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class ListBenchmarksResponse(BaseModel):
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data: List[Benchmark]
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@runtime_checkable
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class Benchmarks(Protocol):
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@webmethod(route="/eval/benchmarks", method="GET")
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async def list_benchmarks(self) -> ListBenchmarksResponse: ...
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@webmethod(route="/eval/benchmarks/{benchmark_id}", method="GET")
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async def get_benchmark(
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self,
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benchmark_id: str,
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) -> Benchmark: ...
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@webmethod(route="/eval/benchmarks", method="POST")
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async def register_benchmark(
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self,
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benchmark_id: str,
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dataset_id: str,
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scoring_functions: List[str],
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provider_benchmark_id: Optional[str] = None,
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provider_id: Optional[str] = None,
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metadata: Optional[Dict[str, Any]] = None,
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) -> None: ...
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