refactor

llama_stack/providers/inline/eval/meta_reference/__init__.py

@@ -22,6 +22,7 @@ async def get_provider_impl(
         deps[Api.datasets],
         deps[Api.scoring],
         deps[Api.inference],
+        deps[Api.agents],
     )
     await impl.initialize()
     return impl

llama_stack/providers/inline/eval/meta_reference/eval.py

@@ -9,6 +9,7 @@ from llama_models.llama3.api.datatypes import *  # noqa: F403
 from .....apis.common.job_types import Job
 from .....apis.eval.eval import Eval, EvalTaskConfig, EvaluateResponse, JobStatus
 from llama_stack.apis.common.type_system import *  # noqa: F403
+from llama_stack.apis.agents import Agents
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
 from llama_stack.apis.eval_tasks import EvalTask
@@ -39,12 +40,14 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
         datasets_api: Datasets,
         scoring_api: Scoring,
         inference_api: Inference,
+        agent_api: Agents,
     ) -> None:
         self.config = config
         self.datasetio_api = datasetio_api
         self.datasets_api = datasets_api
         self.scoring_api = scoring_api
         self.inference_api = inference_api
+        self.agent_api = agent_api
 
         # TODO: assume sync job, will need jobs API for async scheduling
         self.jobs = {}
@@ -126,18 +129,15 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
         self.jobs[job_id] = res
         return Job(job_id=job_id)
 
-    async def evaluate_rows(
-        self,
-        task_id: str,
-        input_rows: List[Dict[str, Any]],
-        scoring_functions: List[str],
-        task_config: EvalTaskConfig,
-    ) -> EvaluateResponse:
+    async def _run_agent_generation(
+        self, task_config: EvalTaskConfig
+    ) -> List[Dict[str, Any]]:
+        pass
+
+    async def _run_model_generation(
+        self, input_rows: List[Dict[str, Any]], task_config: EvalTaskConfig
+    ) -> List[Dict[str, Any]]:
         candidate = task_config.eval_candidate
-        if candidate.type == "agent":
-            raise NotImplementedError(
-                "Evaluation with generation has not been implemented for agents"
-            )
         assert (
             candidate.sampling_params.max_tokens is not None
         ), "SamplingParams.max_tokens must be provided"
@@ -179,6 +179,24 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate):
             else:
                 raise ValueError("Invalid input row")
 
+        return generations
+
+    async def evaluate_rows(
+        self,
+        task_id: str,
+        input_rows: List[Dict[str, Any]],
+        scoring_functions: List[str],
+        task_config: EvalTaskConfig,
+    ) -> EvaluateResponse:
+        candidate = task_config.eval_candidate
+        if candidate.type == "agent":
+            raise NotImplementedError(
+                "Evaluation with generation has not been implemented for agents"
+            )
+
+        if candidate.type == "model":
+            generations = await self._run_model_generation(input_rows, task_config)
+
         # scoring with generated_answer
         score_input_rows = [
             input_r | generated_r

llama_stack/providers/registry/eval.py

@@ -22,6 +22,7 @@ def available_providers() -> List[ProviderSpec]:
                 Api.datasets,
                 Api.scoring,
                 Api.inference,
+                Api.agents,
             ],
         ),
     ]