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https://github.com/meta-llama/llama-stack.git
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143 lines
4.4 KiB
Python
143 lines
4.4 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 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 CommonJobFields, 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.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 = register_schema(
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Annotated[Union[ModelCandidate, AgentCandidate], Field(discriminator="type")],
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name="EvalCandidate",
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)
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@json_schema_type
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class EvaluateResponse(BaseModel):
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"""The response from an evaluation.
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:param generations: The generations from the evaluation.
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:param scores: The scores from the evaluation.
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"""
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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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@json_schema_type
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class EvalJob(CommonJobFields):
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type: Literal["eval"] = "eval"
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result_files: List[str] = Field(
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description="The file ids of the eval results.",
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default_factory=list,
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)
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result_datasets: List[str] = Field(
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description="The ids of the datasets containing the eval results.",
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default_factory=list,
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)
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# how the job is created
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benchmark_id: str = Field(description="The id of the benchmark to evaluate on.")
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candidate: EvalCandidate = Field(description="The candidate to evaluate on.")
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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/jobs", method="POST")
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async def evaluate_benchmark(
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self,
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benchmark_id: str,
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candidate: EvalCandidate,
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) -> EvalJob:
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"""Run an evaluation on a benchmark.
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:param benchmark_id: The ID of the benchmark to run the evaluation on.
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:param candidate: The candidate to evaluate on.
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:return: The job that was created to run the evaluation.
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"""
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@webmethod(route="/eval/rows", method="POST")
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async def evaluate_rows(
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self,
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dataset_rows: List[Dict[str, Any]],
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scoring_fn_ids: List[str],
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candidate: EvalCandidate,
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) -> EvaluateResponse:
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"""Evaluate a list of rows on a candidate.
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:param dataset_rows: The rows to evaluate.
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:param scoring_fn_ids: The scoring function ids to use for the evaluation.
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:param candidate: The candidate to evaluate on.
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:return: EvaluateResponse object containing generations and scores
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"""
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@webmethod(route="/eval/jobs", method="GET")
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async def list_eval_jobs(self) -> List[EvalJob]:
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"""List all evaluation jobs.
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:return: A list of evaluation jobs.
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"""
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...
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@webmethod(route="/eval/job/{job_id}", method="GET")
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async def get_eval_job(self, job_id: str) -> Optional[EvalJob]:
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"""Get a job by id.
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:param job_id: The id of the job to get.
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:return: The job.
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"""
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...
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@webmethod(route="/eval/job/{job_id}", method="DELETE")
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async def delete_eval_job(self, job_id: str) -> Optional[EvalJob]:
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"""Delete a job.
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:param job_id: The id of the job to delete.
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"""
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...
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@webmethod(route="/eval/job/{job_id}/cancel", method="POST")
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async def cancel_eval_job(self, job_id: str) -> Optional[EvalJob]:
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"""Cancel a job.
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:param job_id: The id of the job to cancel.
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"""
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...
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