feat: Allow using registered metric resource names in evaluation

vertexai/_genai/_evals_common.py

@@ -1329,6 +1329,15 @@ def _resolve_evaluation_run_metrics(
     for metric_instance in metrics:
         if isinstance(metric_instance, types.EvaluationRunMetric):
             resolved_metrics_list.append(metric_instance)
+        elif isinstance(metric_instance, str) and metric_instance.startswith(
+            "projects/"
+        ):
+            resolved_metrics_list.append(
+                types.EvaluationRunMetric(
+                    metric=metric_instance.split("/")[-1],
+                    metric_resource_name=metric_instance,
+                )
+            )
         elif isinstance(
             metric_instance, _evals_metric_loaders.LazyLoadedPrebuiltMetric
         ):
@@ -1409,6 +1418,15 @@ def _resolve_metrics(
                 raise
         elif isinstance(metric_instance, types.Metric):
             resolved_metrics_list.append(metric_instance)
+        elif isinstance(metric_instance, str) and metric_instance.startswith(
+            "projects/"
+        ):
+            # Wrap the string in a Metric object to satisfy Pydantic validation
+            # and extract the ID for the 'name' property
+            metric_id = metric_instance.split("/")[-1]
+            resolved_metrics_list.append(
+                types.Metric(name=metric_id, metric_resource_name=metric_instance)
+            )
         else:
             try:
                 metric_name_str = str(metric_instance)

vertexai/_genai/_evals_metric_handlers.py

@@ -1027,7 +1027,7 @@ def get_metric_result(
             for attempt in range(_MAX_RETRIES):
                 try:
                     api_response = self.module._evaluate_instances(
-                        metrics=[self.metric],
+                        metrics_sources=[self.metric],
                         instance=payload.get("instance"),
                         autorater_config=payload.get("autorater_config"),
                     )
@@ -1164,7 +1164,7 @@ def get_metric_result(
             for attempt in range(_MAX_RETRIES):
                 try:
                     api_response = self.module._evaluate_instances(
-                        metrics=[self.metric],
+                        metrics_sources=[self.metric],
                         instance=payload.get("instance"),
                     )
                     break
@@ -1242,6 +1242,14 @@ def aggregate(
         )
 
 
+class RegisteredMetricHandler(PredefinedMetricHandler):
+    """Metric handler for registered metrics."""
+
+    def __init__(self, module: "evals.Evals", metric: types.Metric):
+        # Skip the parent check for SUPPORTED_PREDEFINED_METRICS
+        MetricHandler.__init__(self, module=module, metric=metric)
+
+
 _METRIC_HANDLER_MAPPING = [
     (
         lambda m: hasattr(m, "remote_custom_function") and m.remote_custom_function,
@@ -1251,6 +1259,10 @@ def aggregate(
         lambda m: m.custom_function and isinstance(m.custom_function, Callable),
         CustomMetricHandler,
     ),
+    (
+        lambda m: getattr(m, "metric_resource_name", None) is not None,
+        RegisteredMetricHandler,
+    ),
     (
         lambda m: m.name in ComputationMetricHandler.SUPPORTED_COMPUTATION_METRICS,
         ComputationMetricHandler,

vertexai/_genai/_transformers.py

@@ -31,12 +31,19 @@ def t_metrics(
     Args:
         metrics: A list of metrics used for evaluation.
         set_default_aggregation_metrics: Whether to set default aggregation metrics.
+
     Returns:
         A list of resolved metric payloads for the evaluation request.
     """
     metrics_payload = []
 
     for metric in metrics:
+        # Case 1: Registered Metric Resource Name
+        if isinstance(metric, str) and metric.startswith("projects/"):
+            metrics_payload.append({"metric_resource_name": metric})
+            continue
+
+        # Case 2: Inline Metric Configuration
         metric_payload_item: dict[str, Any] = {}
 
         metric_name = getv(metric, ["name"]).lower()
@@ -83,5 +90,23 @@ def t_metrics(
             raise ValueError(
                 f"Unsupported metric type or invalid metric name: {metric_name}"
             )
-        metrics_payload.append(metric_payload_item)
+        metrics_payload.append({"metric": metric_payload_item})
     return metrics_payload
+
+
+def t_metric_sources(metrics: list[Any]) -> list[dict[str, Any]]:
+    """Prepares the MetricSource payload for the evaluation request."""
+    sources_payload = []
+    for metric in metrics:
+        # Check if the 'metric' is a resource name string or contains one
+        resource_name = getattr(metric, "metric_resource_name", None)
+        if not resource_name and isinstance(metric, str) and metric.startswith("projects/"):
+            resource_name = metric
+
+        if resource_name:
+            sources_payload.append({"metric_resource_name": resource_name})
+        else:
+            # Fallback to existing Metric spec transformation
+            metric_payload = t_metrics([metric])[0]
+            sources_payload.append({"metric": metric_payload})
+    return sources_payload

vertexai/_genai/evals.py

@@ -238,11 +238,11 @@ def _EvaluateInstancesRequestParameters_to_vertex(
     if getv(from_object, ["autorater_config"]) is not None:
         setv(to_object, ["autoraterConfig"], getv(from_object, ["autorater_config"]))
 
-    if getv(from_object, ["metrics"]) is not None:
+    if getv(from_object, ["metrics_sources"]) is not None:
         setv(
             to_object,
-            ["metrics"],
-            [item for item in t.t_metrics(getv(from_object, ["metrics"]))],
+            ["metricSources"],
+            t.t_metric_sources(getv(from_object, ["metrics_sources"])),
         )
 
     if getv(from_object, ["instance"]) is not None:
@@ -399,6 +399,13 @@ def _EvaluationRunMetric_from_vertex(
             _UnifiedMetric_from_vertex(getv(from_object, ["metricConfig"]), to_object),
         )
 
+    if getv(from_object, ["metricResourceName"]) is not None:
+        setv(
+            to_object,
+            ["metric_resource_name"],
+            getv(from_object, ["metricResourceName"]),
+        )
+
     return to_object
 
 
@@ -417,6 +424,13 @@ def _EvaluationRunMetric_to_vertex(
             _UnifiedMetric_to_vertex(getv(from_object, ["metric_config"]), to_object),
         )
 
+    if getv(from_object, ["metric_resource_name"]) is not None:
+        setv(
+            to_object,
+            ["metricResourceName"],
+            getv(from_object, ["metric_resource_name"]),
+        )
+
     return to_object
 
 
@@ -512,6 +526,13 @@ def _GenerateInstanceRubricsRequest_to_vertex(
             ),
         )
 
+    if getv(from_object, ["metric_resource_name"]) is not None:
+        setv(
+            to_object,
+            ["metricResourceName"],
+            getv(from_object, ["metric_resource_name"]),
+        )
+
     if getv(from_object, ["config"]) is not None:
         setv(to_object, ["config"], getv(from_object, ["config"]))
 
@@ -976,7 +997,7 @@ def _evaluate_instances(
         ] = None,
         rubric_based_metric_input: Optional[types.RubricBasedMetricInputOrDict] = None,
         autorater_config: Optional[genai_types.AutoraterConfigOrDict] = None,
-        metrics: Optional[list[types.MetricOrDict]] = None,
+        metrics_sources: Optional[list[types.MetricOrDict]] = None,
         instance: Optional[types.EvaluationInstanceOrDict] = None,
         config: Optional[types.EvaluateInstancesConfigOrDict] = None,
     ) -> types.EvaluateInstancesResponse:
@@ -996,7 +1017,7 @@ def _evaluate_instances(
             tool_parameter_kv_match_input=tool_parameter_kv_match_input,
             rubric_based_metric_input=rubric_based_metric_input,
             autorater_config=autorater_config,
-            metrics=metrics,
+            metrics_sources=metrics_sources,
             instance=instance,
             config=config,
         )
@@ -1049,6 +1070,7 @@ def _generate_rubrics(
             types.PredefinedMetricSpecOrDict
         ] = None,
         rubric_generation_spec: Optional[types.RubricGenerationSpecOrDict] = None,
+        metric_resource_name: Optional[str] = None,
         config: Optional[types.RubricGenerationConfigOrDict] = None,
     ) -> types.GenerateInstanceRubricsResponse:
         """
@@ -1059,6 +1081,7 @@ def _generate_rubrics(
             contents=contents,
             predefined_rubric_generation_spec=predefined_rubric_generation_spec,
             rubric_generation_spec=rubric_generation_spec,
+            metric_resource_name=metric_resource_name,
             config=config,
         )
 
@@ -2230,7 +2253,7 @@ async def _evaluate_instances(
         ] = None,
         rubric_based_metric_input: Optional[types.RubricBasedMetricInputOrDict] = None,
         autorater_config: Optional[genai_types.AutoraterConfigOrDict] = None,
-        metrics: Optional[list[types.MetricOrDict]] = None,
+        metrics_sources: Optional[list[types.MetricOrDict]] = None,
         instance: Optional[types.EvaluationInstanceOrDict] = None,
         config: Optional[types.EvaluateInstancesConfigOrDict] = None,
     ) -> types.EvaluateInstancesResponse:
@@ -2250,7 +2273,7 @@ async def _evaluate_instances(
             tool_parameter_kv_match_input=tool_parameter_kv_match_input,
             rubric_based_metric_input=rubric_based_metric_input,
             autorater_config=autorater_config,
-            metrics=metrics,
+            metrics_sources=metrics_sources,
             instance=instance,
             config=config,
         )
@@ -2305,6 +2328,7 @@ async def _generate_rubrics(
             types.PredefinedMetricSpecOrDict
         ] = None,
         rubric_generation_spec: Optional[types.RubricGenerationSpecOrDict] = None,
+        metric_resource_name: Optional[str] = None,
         config: Optional[types.RubricGenerationConfigOrDict] = None,
     ) -> types.GenerateInstanceRubricsResponse:
         """
@@ -2315,6 +2339,7 @@ async def _generate_rubrics(
             contents=contents,
             predefined_rubric_generation_spec=predefined_rubric_generation_spec,
             rubric_generation_spec=rubric_generation_spec,
+            metric_resource_name=metric_resource_name,
             config=config,
         )
 

vertexai/_genai/types/common.py

@@ -2326,6 +2326,9 @@ class LLMBasedMetricSpec(_common.BaseModel):
         default=None,
         description="""Dynamically generate rubrics using this specification.""",
     )
+    metric_resource_name: Optional[str] = Field(
+        default=None, description="""The resource name of the metric definition."""
+    )
 
 
 class LLMBasedMetricSpecDict(TypedDict, total=False):
@@ -2350,6 +2353,9 @@ class LLMBasedMetricSpecDict(TypedDict, total=False):
     rubric_generation_spec: Optional[RubricGenerationSpecDict]
     """Dynamically generate rubrics using this specification."""
 
+    metric_resource_name: Optional[str]
+    """The resource name of the metric definition."""
+
 
 LLMBasedMetricSpecOrDict = Union[LLMBasedMetricSpec, LLMBasedMetricSpecDict]
 
@@ -2482,6 +2488,9 @@ class EvaluationRunMetric(_common.BaseModel):
     metric_config: Optional[UnifiedMetric] = Field(
         default=None, description="""The unified metric used for evaluation run."""
     )
+    metric_resource_name: Optional[str] = Field(
+        default=None, description="""The resource name of the metric definition."""
+    )
 
 
 class EvaluationRunMetricDict(TypedDict, total=False):
@@ -2493,6 +2502,9 @@ class EvaluationRunMetricDict(TypedDict, total=False):
     metric_config: Optional[UnifiedMetricDict]
     """The unified metric used for evaluation run."""
 
+    metric_resource_name: Optional[str]
+    """The resource name of the metric definition."""
+
 
 EvaluationRunMetricOrDict = Union[EvaluationRunMetric, EvaluationRunMetricDict]
 
@@ -4439,6 +4451,9 @@ class Metric(_common.BaseModel):
         default=None,
         description="""Optional steering instruction parameters for the automated predefined metric.""",
     )
+    metric_resource_name: Optional[str] = Field(
+        default=None, description="""The resource name of the metric definition."""
+    )
 
     # Allow extra fields to support metric-specific config fields.
     model_config = ConfigDict(extra="allow")
@@ -4643,6 +4658,9 @@ class MetricDict(TypedDict, total=False):
     metric_spec_parameters: Optional[dict[str, Any]]
     """Optional steering instruction parameters for the automated predefined metric."""
 
+    metric_resource_name: Optional[str]
+    """The resource name of the metric definition."""
+
 
 MetricOrDict = Union[Metric, MetricDict]
 
@@ -4679,11 +4697,9 @@ class _EvaluateInstancesRequestParameters(_common.BaseModel):
     autorater_config: Optional[genai_types.AutoraterConfig] = Field(
         default=None, description=""""""
     )
-    metrics: Optional[list[Metric]] = Field(
+    metrics_sources: Optional[list[Metric]] = Field(
         default=None,
-        description="""The metrics used for evaluation.
-  Currently, we only support evaluating a single metric. If multiple metrics
-  are provided, only the first one will be evaluated.""",
+        description="""The metrics used for evaluation. Each can be an inline configuration or a registered metric resource name.""",
     )
     instance: Optional[EvaluationInstance] = Field(
         default=None, description="""The instance to be evaluated."""
@@ -4727,10 +4743,8 @@ class _EvaluateInstancesRequestParametersDict(TypedDict, total=False):
     autorater_config: Optional[genai_types.AutoraterConfigDict]
     """"""
 
-    metrics: Optional[list[MetricDict]]
-    """The metrics used for evaluation.
-  Currently, we only support evaluating a single metric. If multiple metrics
-  are provided, only the first one will be evaluated."""
+    metrics_sources: Optional[list[MetricDict]]
+    """The metrics used for evaluation. Each can be an inline configuration or a registered metric resource name."""
 
     instance: Optional[EvaluationInstanceDict]
     """The instance to be evaluated."""
@@ -5354,6 +5368,10 @@ class _GenerateInstanceRubricsRequest(_common.BaseModel):
         default=None,
         description="""Specification for how the rubrics should be generated.""",
     )
+    metric_resource_name: Optional[str] = Field(
+        default=None,
+        description="""Registered metric resource name. If this field is set, the configuration provided in this field is used for rubric generation. The `predefined_rubric_generation_spec` and `rubric_generation_spec` fields will be ignored.""",
+    )
     config: Optional[RubricGenerationConfig] = Field(default=None, description="""""")
 
 
@@ -5374,6 +5392,9 @@ class _GenerateInstanceRubricsRequestDict(TypedDict, total=False):
     rubric_generation_spec: Optional[RubricGenerationSpecDict]
     """Specification for how the rubrics should be generated."""
 
+    metric_resource_name: Optional[str]
+    """Registered metric resource name. If this field is set, the configuration provided in this field is used for rubric generation. The `predefined_rubric_generation_spec` and `rubric_generation_spec` fields will be ignored."""
+
     config: Optional[RubricGenerationConfigDict]
     """"""