scoring updates

llama_stack/apis/benchmarks/benchmarks.py

@@ -8,7 +8,6 @@ from typing import Any, Dict, List, Literal, Optional, Protocol, runtime_checkab
 from pydantic import BaseModel, Field
 
 from llama_stack.apis.resource import Resource, ResourceType
-from llama_stack.apis.scoring_functions import ScoringFnParams
 from llama_stack.schema_utils import json_schema_type, webmethod
 
 

llama_stack/apis/eval/eval.py

@@ -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.
+        """Evaluate a list of rows on a candidate.
         
-        :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.
+        :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
         """
 

llama_stack/apis/scoring/scoring.py

@@ -8,7 +8,7 @@ from typing import Any, Dict, List, Optional, Protocol, runtime_checkable
 
 from pydantic import BaseModel
 
-from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams
+from llama_stack.apis.scoring_functions import ScoringFn
 from llama_stack.schema_utils import json_schema_type, webmethod
 
 # mapping of metric to value
@@ -56,23 +56,22 @@ class Scoring(Protocol):
     scoring_function_store: ScoringFunctionStore
 
     @webmethod(route="/scoring/score-batch", method="POST")
-    async def score_batch(
+    async def score_dataset(
         self,
         dataset_id: str,
-        scoring_functions: Dict[str, Optional[ScoringFnParams]],
-        save_results_dataset: bool = False,
+        scoring_fn_ids: List[str],
     ) -> ScoreBatchResponse: ...
 
     @webmethod(route="/scoring/score", method="POST")
     async def score(
         self,
-        input_rows: List[Dict[str, Any]],
-        scoring_functions: Dict[str, Optional[ScoringFnParams]],
+        dataset_rows: List[Dict[str, Any]],
+        scoring_fn_ids: List[str],
     ) -> ScoreResponse:
         """Score a list of rows.
 
-        :param input_rows: The rows to score.
-        :param scoring_functions: The scoring functions to use for the scoring.
+        :param dataset_rows: The rows to score.
+        :param scoring_fn_ids: The scoring function ids to use for the scoring.
         :return: ScoreResponse object containing rows and aggregated results
         """
         ...

llama_stack/apis/scoring_functions/scoring_functions.py

@@ -67,7 +67,7 @@ class AggregationFunctionType(Enum):
     accuracy = "accuracy"
 
 
-class BasicScoringFnParamsFields(BaseModel):
+class BasicScoringFnParams(BaseModel):
     """
     :param aggregation_functions: (Optional) Aggregation functions to apply to the scores of each row. If not provided, no aggregation will be performed.
     """
@@ -78,7 +78,7 @@ class BasicScoringFnParamsFields(BaseModel):
     )
 
 
-class RegexParserScoringFnParamsFields(BaseModel):
+class RegexParserScoringFnParams(BaseModel):
     """
     :param parsing_regexes: (Optional) Regexes to extract the answer from generated response.
     :param aggregation_functions: (Optional) Aggregation functions to apply to the scores of each row. If not provided, no aggregation will be performed.
@@ -93,7 +93,7 @@ class RegexParserScoringFnParamsFields(BaseModel):
         default_factory=list,
     )
 
-class CustomLLMAsJudgeScoringFnParamsFields(BaseModel):
+class CustomLLMAsJudgeScoringFnParams(BaseModel):
     type: Literal["custom_llm_as_judge"] = "custom_llm_as_judge"
     judge_model: str
     prompt_template: Optional[str] = None
@@ -103,103 +103,103 @@ class CustomLLMAsJudgeScoringFnParamsFields(BaseModel):
     )
 
 @json_schema_type
-class RegexParserScoringFnParams(BaseModel):
+class RegexParserScoringFn(BaseModel):
     type: Literal["regex_parser"] = "regex_parser"
-    regex_parser: RegexParserScoringFnParamsFields
+    regex_parser: RegexParserScoringFnParams
 
 
 @json_schema_type
-class RegexParserMathScoringFnParams(BaseModel):
+class RegexParserMathScoringFn(BaseModel):
     type: Literal["regex_parser_math_response"] = "regex_parser_math_response"
-    regex_parser_math_response: RegexParserScoringFnParamsFields
+    regex_parser_math_response: RegexParserScoringFnParams
 
 @json_schema_type
-class EqualityScoringFnParams(BaseModel):
+class EqualityScoringFn(BaseModel):
     type: Literal["equality"] = "equality"
-    equality: BasicScoringFnParamsFields
+    equality: BasicScoringFnParams
 
 @json_schema_type
-class SubsetOfcoringFnParams(BaseModel):
+class SubsetOfScoringFn(BaseModel):
     type: Literal["subset_of"] = "subset_of"
-    subset_of: BasicScoringFnParamsFields
+    subset_of: BasicScoringFnParams
 
 @json_schema_type
-class FactualityScoringFnParams(BaseModel):
+class FactualityScoringFn(BaseModel):
     type: Literal["factuality"] = "factuality"
-    factuality: BasicScoringFnParamsFields
+    factuality: BasicScoringFnParams
 
 @json_schema_type
-class FaithfulnessScoringFnParams(BaseModel):
+class FaithfulnessScoringFn(BaseModel):
     type: Literal["faithfulness"] = "faithfulness"
-    faithfulness: BasicScoringFnParamsFields
+    faithfulness: BasicScoringFnParams
 
 @json_schema_type
-class AnswerCorrectnessScoringFnParams(BaseModel):
+class AnswerCorrectnessScoringFn(BaseModel):
     type: Literal["answer_correctness"] = "answer_correctness"
-    answer_correctness: BasicScoringFnParamsFields
+    answer_correctness: BasicScoringFnParams
 
 @json_schema_type
-class AnswerRelevancyScoringFnParams(BaseModel):
+class AnswerRelevancyScoringFn(BaseModel):
     type: Literal["answer_relevancy"] = "answer_relevancy"
-    answer_relevancy: BasicScoringFnParamsFields
+    answer_relevancy: BasicScoringFnParams
 
 @json_schema_type
-class AnswerSimilarityScoringFnParams(BaseModel):
+class AnswerSimilarityScoringFn(BaseModel):
     type: Literal["answer_similarity"] = "answer_similarity"
-    answer_similarity: BasicScoringFnParamsFields
+    answer_similarity: BasicScoringFnParams
 
 
 @json_schema_type
-class ContextEntityRecallScoringFnParams(BaseModel):
+class ContextEntityRecallScoringFn(BaseModel):
     type: Literal["context_entity_recall"] = "context_entity_recall"
-    context_entity_recall: BasicScoringFnParamsFields
+    context_entity_recall: BasicScoringFnParams
 
 
 @json_schema_type
-class ContextPrecisionScoringFnParams(BaseModel):
+class ContextPrecisionScoringFn(BaseModel):
     type: Literal["context_precision"] = "context_precision"
-    context_precision: BasicScoringFnParamsFields
+    context_precision: BasicScoringFnParams
 
 
 @json_schema_type
-class ContextRecallScoringFnParams(BaseModel):
+class ContextRecallScoringFn(BaseModel):
     type: Literal["context_recall"] = "context_recall"
-    context_recall: BasicScoringFnParamsFields
+    context_recall: BasicScoringFnParams
 
 
 @json_schema_type
-class ContextRelevancyScoringFnParams(BaseModel):
+class ContextRelevancyScoringFn(BaseModel):
     type: Literal["context_relevancy"] = "context_relevancy"
-    context_relevancy: BasicScoringFnParamsFields
+    context_relevancy: BasicScoringFnParams
 
 
 @json_schema_type
-class CustomLLMAsJudgeScoringFnParams(BaseModel):
+class CustomLLMAsJudgeScoringFn(BaseModel):
     type: Literal["custom_llm_as_judge"] = "custom_llm_as_judge"
-    custom_llm_as_judge: CustomLLMAsJudgeScoringFnParamsFields
+    custom_llm_as_judge: CustomLLMAsJudgeScoringFnParams
 
 
-ScoringFnParams = register_schema(
+ScoringFnDefinition = register_schema(
     Annotated[
         Union[
-            CustomLLMAsJudgeScoringFnParams,
-            RegexParserScoringFnParams,
-            RegexParserMathScoringFnParams,
-            EqualityScoringFnParams,
-            SubsetOfcoringFnParams,
-            FactualityScoringFnParams,
-            FaithfulnessScoringFnParams,
-            AnswerCorrectnessScoringFnParams,
-            AnswerRelevancyScoringFnParams,
-            AnswerSimilarityScoringFnParams,
-            ContextEntityRecallScoringFnParams,
-            ContextPrecisionScoringFnParams,
-            ContextRecallScoringFnParams,
-            ContextRelevancyScoringFnParams,
+            CustomLLMAsJudgeScoringFn,
+            RegexParserScoringFn,
+            RegexParserMathScoringFn,
+            EqualityScoringFn,
+            SubsetOfScoringFn,
+            FactualityScoringFn,
+            FaithfulnessScoringFn,
+            AnswerCorrectnessScoringFn,
+            AnswerRelevancyScoringFn,
+            AnswerSimilarityScoringFn,
+            ContextEntityRecallScoringFn,
+            ContextPrecisionScoringFn,
+            ContextRecallScoringFn,
+            ContextRelevancyScoringFn,
         ],
         Field(discriminator="type"),
     ],
-    name="ScoringFnParams",
+    name="ScoringFnDefinition",
 )
 
 
@@ -208,7 +208,7 @@ class CommonScoringFnFields(BaseModel):
     :param fn: The scoring function type and parameters. 
     :param metadata: (Optional) Any additional metadata for this definition (e.g. description).
     """
-    fn: ScoringFnParams
+    fn: ScoringFnDefinition
     metadata: Dict[str, Any] = Field(
         default_factory=dict,
         description="Any additional metadata for this definition (e.g. description)",
@@ -288,7 +288,7 @@ class ScoringFunctions(Protocol):
     @webmethod(route="/scoring-functions", method="POST")
     async def register_scoring_function(
         self,
-        fn: ScoringFnParams,
+        fn: ScoringFnDefinition,
         scoring_fn_id: Optional[str] = None,
         metadata: Optional[Dict[str, Any]] = None,
     ) -> ScoringFn: