feat: GenAI Client(evals) - Add retry to predefine metric

tests/unit/vertexai/genai/test_evals.py

@@ -33,6 +33,7 @@
 from vertexai._genai import evals
 from vertexai._genai import types as vertexai_genai_types
 from google.genai import client
+from google.genai import errors as genai_errors
 from google.genai import types as genai_types
 import pandas as pd
 import pytest
@@ -4861,6 +4862,110 @@ def test_execute_evaluation_adds_creation_timestamp(
         assert result.metadata is not None
         assert result.metadata.creation_timestamp == mock_now
 
+    @mock.patch(
+        "vertexai._genai._evals_metric_handlers._evals_constant.SUPPORTED_PREDEFINED_METRICS",
+        frozenset(["summarization_quality"]),
+    )
+    @mock.patch("time.sleep", return_value=None)
+    @mock.patch("vertexai._genai.evals.Evals._evaluate_instances")
+    def test_predefined_metric_retry_on_resource_exhausted(
+        self,
+        mock_private_evaluate_instances,
+        mock_sleep,
+        mock_api_client_fixture,
+    ):
+        dataset_df = pd.DataFrame(
+            [{"prompt": "Test prompt", "response": "Test response"}]
+        )
+        input_dataset = vertexai_genai_types.EvaluationDataset(
+            eval_dataset_df=dataset_df
+        )
+        metric = vertexai_genai_types.Metric(name="summarization_quality")
+        metric_result = vertexai_genai_types.MetricResult(
+            score=0.9,
+            explanation="Mocked predefined explanation",
+            rubric_verdicts=[],
+            error=None,
+        )
+        error_response_json = {
+            "error": {
+                "code": 429,
+                "message": ("Judge model resource exhausted. Please try again later."),
+                "status": "RESOURCE_EXHAUSTED",
+            }
+        }
+        mock_private_evaluate_instances.side_effect = [
+            genai_errors.ClientError(code=429, response_json=error_response_json),
+            genai_errors.ClientError(code=429, response_json=error_response_json),
+            vertexai_genai_types.EvaluateInstancesResponse(
+                metric_results=[metric_result]
+            ),
+        ]
+
+        result = _evals_common._execute_evaluation(
+            api_client=mock_api_client_fixture,
+            dataset=input_dataset,
+            metrics=[metric],
+        )
+
+        assert mock_private_evaluate_instances.call_count == 3
+        assert mock_sleep.call_count == 2
+        assert len(result.summary_metrics) == 1
+        summary_metric = result.summary_metrics[0]
+        assert summary_metric.metric_name == "summarization_quality"
+        assert summary_metric.mean_score == 0.9
+
+    @mock.patch(
+        "vertexai._genai._evals_metric_handlers._evals_constant.SUPPORTED_PREDEFINED_METRICS",
+        frozenset(["summarization_quality"]),
+    )
+    @mock.patch("time.sleep", return_value=None)
+    @mock.patch("vertexai._genai.evals.Evals._evaluate_instances")
+    def test_predefined_metric_retry_fail_on_resource_exhausted(
+        self,
+        mock_private_evaluate_instances,
+        mock_sleep,
+        mock_api_client_fixture,
+    ):
+        dataset_df = pd.DataFrame(
+            [{"prompt": "Test prompt", "response": "Test response"}]
+        )
+        input_dataset = vertexai_genai_types.EvaluationDataset(
+            eval_dataset_df=dataset_df
+        )
+        error_response_json = {
+            "error": {
+                "code": 429,
+                "message": ("Judge model resource exhausted. Please try again later."),
+                "status": "RESOURCE_EXHAUSTED",
+            }
+        }
+        metric = vertexai_genai_types.Metric(name="summarization_quality")
+        mock_private_evaluate_instances.side_effect = [
+            genai_errors.ClientError(code=429, response_json=error_response_json),
+            genai_errors.ClientError(code=429, response_json=error_response_json),
+            genai_errors.ClientError(code=429, response_json=error_response_json),
+        ]
+
+        result = _evals_common._execute_evaluation(
+            api_client=mock_api_client_fixture,
+            dataset=input_dataset,
+            metrics=[metric],
+        )
+
+        assert mock_private_evaluate_instances.call_count == 3
+        assert mock_sleep.call_count == 2
+        assert len(result.summary_metrics) == 1
+        summary_metric = result.summary_metrics[0]
+        assert summary_metric.metric_name == "summarization_quality"
+        assert summary_metric.mean_score is None
+        assert summary_metric.num_cases_error == 1
+        assert (
+            "Judge model resource exhausted after 3 retries"
+        ) in result.eval_case_results[0].response_candidate_results[0].metric_results[
+            "summarization_quality"
+        ].error_message
+
 
 class TestEvaluationDataset:
     """Contains set of tests for the EvaluationDataset class methods."""

vertexai/_genai/_evals_metric_handlers.py

@@ -20,8 +20,10 @@
 import json
 import logging
 import statistics
+import time
 from typing import Any, Callable, Optional, TypeVar, Union
 
+from google.genai import errors as genai_errors
 from google.genai import _common
 from google.genai import types as genai_types
 from tqdm import tqdm
@@ -34,6 +36,7 @@
 
 
 logger = logging.getLogger(__name__)
+_MAX_RETRIES = 3
 
 
 def _extract_text_from_content(
@@ -964,9 +967,30 @@ def get_metric_result(
         metric_name = self.metric.name
         try:
             payload = self._build_request_payload(eval_case, response_index)
-            api_response = self.module._evaluate_instances(
-                metrics=[self.metric], instance=payload.get("instance")
-            )
+            for attempt in range(_MAX_RETRIES):
+                try:
+                    api_response = self.module._evaluate_instances(
+                        metrics=[self.metric], instance=payload.get("instance")
+                    )
+                    break
+                except genai_errors.ClientError as e:
+                    if e.code == 429:
+                        logger.warning(
+                            "Resource Exhausted error on attempt %d/%d: %s. Retrying in %s"
+                            " seconds...",
+                            attempt + 1,
+                            _MAX_RETRIES,
+                            e,
+                            2**attempt,
+                        )
+                        if attempt == _MAX_RETRIES - 1:
+                            return types.EvalCaseMetricResult(
+                                metric_name=metric_name,
+                                error_message=f"Judge model resource exhausted after {_MAX_RETRIES} retries: {e}",
+                            )
+                        time.sleep(2**attempt)
+                    else:
+                        raise e
 
             if (
                 api_response