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102 lines
3.2 KiB
Python
102 lines
3.2 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, Union
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from llama_models.schema_utils import json_schema_type, webmethod
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from pydantic import BaseModel, Field
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from typing_extensions import Annotated
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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.inference import SamplingParams, SystemMessage
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from llama_stack.apis.scoring import ScoringResult
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from llama_stack.apis.scoring_functions import ScoringFnParams
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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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num_examples: Optional[int] = Field(
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description="Number of examples to evaluate (useful for testing), if not provided, all examples in the dataset will be evaluated",
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default=None,
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)
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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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num_examples: Optional[int] = Field(
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description="Number of examples to evaluate (useful for testing), if not provided, all examples in the dataset will be evaluated",
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default=None,
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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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