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scoring updates

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Xi Yan 2025-03-12 21:54:12 -07:00
parent 7b50fdb2b1
commit 3a87562e8d
6 changed files with 1346 additions and 1466 deletions
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48
llama_stack/apis/eval/eval.py
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@ -13,7 +13,6 @@ from llama_stack.apis.agents import AgentConfig
from llama_stack.apis.common.job_types import Job, JobStatus
from llama_stack.apis.inference import SamplingParams, SystemMessage
from llama_stack.apis.scoring import ScoringResult
from llama_stack.apis.scoring_functions import ScoringFnParams
from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
@ -49,27 +48,6 @@ EvalCandidate = register_schema(
)
@json_schema_type
class BenchmarkConfig(BaseModel):
"""A benchmark configuration for evaluation.
:param eval_candidate: The candidate to evaluate.
:param scoring_params: Map between scoring function id and parameters for each scoring function you want to run
:param num_examples: (Optional) The number of examples to evaluate. If not provided, all examples in the dataset will be evaluated
"""
eval_candidate: EvalCandidate
scoring_params: Dict[str, ScoringFnParams] = Field(
description="Map between scoring function id and parameters for each scoring function you want to run",
default_factory=dict,
)
num_examples: Optional[int] = Field(
description="Number of examples to evaluate (useful for testing), if not provided, all examples in the dataset will be evaluated",
default=None,
)
# we could optinally add any specific dataset config here
@json_schema_type
class EvaluateResponse(BaseModel):
"""The response from an evaluation.
@ -87,32 +65,30 @@ class Eval(Protocol):
"""Llama Stack Evaluation API for running evaluations on model and agent candidates."""
@webmethod(route="/eval/benchmarks/{benchmark_id}/jobs", method="POST")
async def run_eval(
async def evaluate_benchmark(
self,
benchmark_id: str,
benchmark_config: BenchmarkConfig,
candidate: EvalCandidate,
) -> Job:
"""Run an evaluation on a benchmark.
:param benchmark_id: The ID of the benchmark to run the evaluation on.
:param benchmark_config: The configuration for the benchmark.
:param candidate: The candidate to evaluate on.
:return: The job that was created to run the evaluation.
"""
@webmethod(route="/eval/benchmarks/{benchmark_id}/evaluations", method="POST")
@webmethod(route="/eval/rows", method="POST")
async def evaluate_rows(
self,
benchmark_id: str,
input_rows: List[Dict[str, Any]],
scoring_functions: List[str],
benchmark_config: BenchmarkConfig,
dataset_rows: List[Dict[str, Any]],
scoring_fn_ids: List[str],
candidate: EvalCandidate,
) -> EvaluateResponse:
"""Evaluate a list of rows on a benchmark.
:param benchmark_id: The ID of the benchmark to run the evaluation on.
:param input_rows: The rows to evaluate.
:param scoring_functions: The scoring functions to use for the evaluation.
:param benchmark_config: The configuration for the benchmark.
"""Evaluate a list of rows on a candidate.
:param dataset_rows: The rows to evaluate.
:param scoring_fn_ids: The scoring function ids to use for the evaluation.
:param candidate: The candidate to evaluate on.
:return: EvaluateResponse object containing generations and scores
"""