mirror of
https://github.com/meta-llama/llama-stack.git
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6192bf43a4
* wip * scoring fn api * eval api * eval task * evaluate api update * pre commit * unwrap context -> config * config field doc * typo * naming fix * separate benchmark / app eval * api name * rename * wip tests * wip * datasetio test * delete unused * fixture * scoring resolve * fix scoring register * scoring test pass * score batch * scoring fix * fix eval * test eval works * remove type ignore * api refactor * add default task_eval_id for routing * add eval_id for jobs * remove type ignore * only keep 1 run_eval * fix optional * register task required * register task required * delete old tests * delete old tests * fixture return impl
92 lines
2.8 KiB
Python
92 lines
2.8 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 Literal, Optional, Protocol, Union
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from typing_extensions import Annotated
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_models.schema_utils import json_schema_type, webmethod
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from llama_stack.apis.scoring_functions import * # noqa: F403
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from llama_stack.apis.agents import AgentConfig
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from llama_stack.apis.common.job_types import Job, JobStatus
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from llama_stack.apis.scoring import * # noqa: F403
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from llama_stack.apis.eval_tasks import * # noqa: F403
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@json_schema_type
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class ModelCandidate(BaseModel):
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type: Literal["model"] = "model"
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model: str
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sampling_params: SamplingParams
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system_message: Optional[SystemMessage] = None
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@json_schema_type
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class AgentCandidate(BaseModel):
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type: Literal["agent"] = "agent"
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config: AgentConfig
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EvalCandidate = Annotated[
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Union[ModelCandidate, AgentCandidate], Field(discriminator="type")
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]
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@json_schema_type
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class BenchmarkEvalTaskConfig(BaseModel):
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type: Literal["benchmark"] = "benchmark"
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eval_candidate: EvalCandidate
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@json_schema_type
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class AppEvalTaskConfig(BaseModel):
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type: Literal["app"] = "app"
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eval_candidate: EvalCandidate
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scoring_params: Dict[str, ScoringFnParams] = Field(
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description="Map between scoring function id and parameters for each scoring function you want to run",
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default_factory=dict,
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)
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# we could optinally add any specific dataset config here
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EvalTaskConfig = Annotated[
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Union[BenchmarkEvalTaskConfig, AppEvalTaskConfig], Field(discriminator="type")
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]
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@json_schema_type
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class EvaluateResponse(BaseModel):
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generations: List[Dict[str, Any]]
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# each key in the dict is a scoring function name
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scores: Dict[str, ScoringResult]
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class Eval(Protocol):
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@webmethod(route="/eval/run_eval", method="POST")
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async def run_eval(
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self,
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task_id: str,
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task_config: EvalTaskConfig,
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) -> Job: ...
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@webmethod(route="/eval/evaluate_rows", method="POST")
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async def evaluate_rows(
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self,
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task_id: str,
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input_rows: List[Dict[str, Any]],
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scoring_functions: List[str],
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task_config: EvalTaskConfig,
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) -> EvaluateResponse: ...
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@webmethod(route="/eval/job/status", method="GET")
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async def job_status(self, task_id: str, job_id: str) -> Optional[JobStatus]: ...
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@webmethod(route="/eval/job/cancel", method="POST")
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async def job_cancel(self, task_id: str, job_id: str) -> None: ...
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@webmethod(route="/eval/job/result", method="GET")
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async def job_result(self, task_id: str, job_id: str) -> EvaluateResponse: ...
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