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104 lines
3.6 KiB
Python
104 lines
3.6 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 Dict, Literal, Optional, Protocol, Union
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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 BaseJob
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from llama_stack.apis.inference import SamplingParams, SystemMessage
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from llama_stack.apis.scoring_functions import ScoringFnParams
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from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
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@json_schema_type
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class ModelCandidate(BaseModel):
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"""A model candidate for evaluation.
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:param model: The model ID to evaluate.
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:param sampling_params: The sampling parameters for the model.
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:param system_message: (Optional) The system message providing instructions or context to the model.
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"""
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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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"""An agent candidate for evaluation.
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:param config: The configuration for the agent candidate.
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"""
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type: Literal["agent"] = "agent"
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config: AgentConfig
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EvalCandidate = Annotated[Union[ModelCandidate, AgentCandidate], Field(discriminator="type")]
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register_schema(EvalCandidate, name="EvalCandidate")
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class EvaluateJob(BaseJob, BaseModel):
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type: Literal["eval"] = "eval"
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class ListEvaluateJobsResponse(BaseModel):
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data: list[EvaluateJob]
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@json_schema_type
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class BenchmarkConfig(BaseModel):
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"""A benchmark configuration for evaluation.
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:param eval_candidate: The candidate to evaluate.
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:param scoring_params: Map between scoring function id and parameters for each scoring function you want to run
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:param num_examples: (Optional) The number of examples to evaluate. If not provided, all examples in the dataset will be evaluated
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"""
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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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class Eval(Protocol):
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"""Llama Stack Evaluation API for running evaluations on model and agent candidates."""
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@webmethod(route="/eval/benchmarks/{benchmark_id}/evaluate", method="POST")
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async def evaluate(
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self,
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benchmark_id: str,
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benchmark_config: BenchmarkConfig,
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) -> EvaluateJob: ...
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# CRUD operations on running jobs
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@webmethod(route="/evaluate/jobs/{job_id:path}", method="GET")
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async def get_evaluate_job(self, job_id: str) -> EvaluateJob: ...
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@webmethod(route="/evaluate/jobs", method="GET")
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async def list_evaluate_jobs(self) -> ListEvaluateJobsResponse: ...
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@webmethod(route="/evaluate/jobs/{job_id:path}", method="POST")
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async def update_evaluate_job(self, job: EvaluateJob) -> EvaluateJob: ...
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@webmethod(route="/evaluate/job/{job_id:path}", method="DELETE")
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async def delete_evaluate_job(self, job_id: str) -> None: ...
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# Note: pause/resume/cancel are achieved as follows:
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# - POST with status=paused
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# - POST with status=resuming
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# - POST with status=cancelled
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