refactor base scoring fn v.s. registerable scoring fn

llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_scoring_fn.py

@@ -9,14 +9,14 @@ from typing import Any, Dict, Optional
 
 from llama_stack.apis.scoring import ScoringResultRow
 from llama_stack.apis.scoring_functions import ScoringFnParams, ScoringFnParamsType
-from llama_stack.providers.utils.scoring.base_scoring_fn import BaseScoringFn
+from llama_stack.providers.utils.scoring.base_scoring_fn import RegisteredBaseScoringFn
 
 from .fn_defs.regex_parser_multiple_choice_answer import (
     regex_parser_multiple_choice_answer,
 )
 
 
-class RegexParserScoringFn(BaseScoringFn):
+class RegexParserScoringFn(RegisteredBaseScoringFn):
     """
     A scoring_fn that parses answer from generated response according to context and check match with expected_answer.
     """

llama_stack/providers/inline/scoring/basic/scoring_fn/subset_of_scoring_fn.py

@@ -8,12 +8,12 @@ from typing import Any, Dict, Optional
 
 from llama_stack.apis.scoring import ScoringResultRow
 from llama_stack.apis.scoring_functions import ScoringFnParams
-from llama_stack.providers.utils.scoring.base_scoring_fn import BaseScoringFn
+from llama_stack.providers.utils.scoring.base_scoring_fn import RegisteredBaseScoringFn
 
 from .fn_defs.subset_of import subset_of
 
 
-class SubsetOfScoringFn(BaseScoringFn):
+class SubsetOfScoringFn(RegisteredBaseScoringFn):
     """
     A scoring_fn that assigns a score of 1.0 if the expected string is included in the generated string, and 0.0 otherwise.
     """

llama_stack/providers/inline/scoring/braintrust/braintrust.py

@@ -20,7 +20,7 @@ from autoevals.ragas import AnswerCorrectness
 from llama_stack.distribution.request_headers import NeedsRequestProviderData
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
 
-from llama_stack.providers.utils.scoring.aggregation_utils import aggregate_average
+from llama_stack.providers.utils.scoring.aggregation_utils import aggregate_metrics
 
 from .config import BraintrustScoringConfig
 from .scoring_fn.fn_defs.answer_correctness import answer_correctness_fn_def
@@ -147,8 +147,11 @@ class BraintrustScoringImpl(
                 await self.score_row(input_row, scoring_fn_id)
                 for input_row in input_rows
             ]
-            aggregation_functions = [AggregationFunctionType.average]
+            aggregation_functions = self.supported_fn_defs_registry[
-            agg_results = aggregate_average(score_results)
+                scoring_fn_id
+            ].params.aggregation_functions
+
+            agg_results = aggregate_metrics(score_results, aggregation_functions)
             res[scoring_fn_id] = ScoringResult(
                 score_rows=score_results,
                 aggregated_results=agg_results,

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_correctness.py

@@ -5,14 +5,20 @@
 # the root directory of this source tree.
 
 from llama_stack.apis.common.type_system import NumberType
-from llama_stack.apis.scoring_functions import ScoringFn
+from llama_stack.apis.scoring_functions import (
+    AggregationFunctionType,
+    BasicScoringFnParams,
+    ScoringFn,
+)
 
 
 answer_correctness_fn_def = ScoringFn(
     identifier="braintrust::answer-correctness",
     description="Scores the correctness of the answer based on the ground truth.. One of Braintrust LLM basd scorer https://github.com/braintrustdata/autoevals/blob/main/py/autoevals/llm.py",
-    params=None,
     provider_id="braintrust",
     provider_resource_id="answer-correctness",
     return_type=NumberType(),
+    params=BasicScoringFnParams(
+        aggregation_functions=[AggregationFunctionType.average]
+    ),
 )

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/factuality.py

@@ -5,14 +5,20 @@
 # the root directory of this source tree.
 
 from llama_stack.apis.common.type_system import NumberType
-from llama_stack.apis.scoring_functions import ScoringFn
+from llama_stack.apis.scoring_functions import (
+    AggregationFunctionType,
+    BasicScoringFnParams,
+    ScoringFn,
+)
 
 
 factuality_fn_def = ScoringFn(
     identifier="braintrust::factuality",
     description="Test whether an output is factual, compared to an original (`expected`) value. One of Braintrust LLM basd scorer https://github.com/braintrustdata/autoevals/blob/main/py/autoevals/llm.py",
-    params=None,
     provider_id="braintrust",
     provider_resource_id="factuality",
     return_type=NumberType(),
+    params=BasicScoringFnParams(
+        aggregation_functions=[AggregationFunctionType.average]
+    ),
 )