fix: Strip None fields from agent_data in GenerateLossClusters to prevent INVALID_ARGUMENT errors

PiperOrigin-RevId: 901053406

Author: vertex-sdk-bot

tests/unit/vertexai/genai/replays/test_generate_loss_clusters.py

@@ -20,7 +20,7 @@
 
 
 def _make_eval_result():
-    """Creates an EvaluationResult with representative data for loss analysis."""
+    """Creates an EvaluationResult with rubric verdicts for loss analysis."""
     return types.EvaluationResult(
         eval_case_results=[
             types.EvalCaseResult(
@@ -32,6 +32,19 @@ def _make_eval_result():
                             "multi_turn_task_success_v1": types.EvalCaseMetricResult(
                                 score=0.0,
                                 explanation="Failed tool invocation",
+                                rubric_verdicts=[
+                                    types.evals.RubricVerdict(
+                                        evaluated_rubric=types.evals.Rubric(
+                                            rubric_id="tool_invocation",
+                                            content=types.evals.RubricContent(
+                                                property=types.evals.RubricContentProperty(
+                                                    description="The agent should invoke the find_flights tool with the correct parameters.",
+                                                )
+                                            ),
+                                        ),
+                                        verdict=False,
+                                    )
+                                ],
                             )
                         },
                     )

tests/unit/vertexai/genai/test_evals.py

@@ -441,6 +441,86 @@ def test_sanitize_agent_data_skips_error_payload(self):
         assert "error" not in sanitized
         assert sanitized == {}
 
+    def test_t_inline_results_strips_none_tool_fields(self):
+        """Tests that t_inline_results strips None tool fields like file_search."""
+        eval_result = common_types.EvaluationResult(
+            eval_case_results=[
+                common_types.EvalCaseResult(
+                    eval_case_index=0,
+                    response_candidate_results=[
+                        common_types.ResponseCandidateResult(
+                            response_index=0,
+                            metric_results={
+                                "multi_turn_task_success_v1": common_types.EvalCaseMetricResult(
+                                    score=0.0,
+                                    explanation="Failed",
+                                )
+                            },
+                        )
+                    ],
+                )
+            ],
+            evaluation_dataset=[
+                common_types.EvaluationDataset(
+                    eval_cases=[
+                        common_types.EvalCase(
+                            agent_data=vertexai_genai_types.evals.AgentData(
+                                agents={
+                                    "agent_0": vertexai_genai_types.evals.AgentConfig(
+                                        agent_id="agent_0",
+                                        agent_type="LlmAgent",
+                                        instruction="You are a helper.",
+                                        tools=[
+                                            genai_types.Tool(
+                                                function_declarations=[
+                                                    genai_types.FunctionDeclaration(
+                                                        name="search",
+                                                        description="Searches the web.",
+                                                    )
+                                                ]
+                                            )
+                                        ],
+                                    )
+                                },
+                                turns=[
+                                    vertexai_genai_types.evals.ConversationTurn(
+                                        turn_index=0,
+                                        events=[
+                                            vertexai_genai_types.evals.AgentEvent(
+                                                author="user",
+                                                content=genai_types.Content(
+                                                    parts=[genai_types.Part(text="Hi")],
+                                                ),
+                                            ),
+                                        ],
+                                    )
+                                ],
+                            )
+                        )
+                    ]
+                )
+            ],
+            metadata=common_types.EvaluationRunMetadata(
+                candidate_names=["candidate-1"]
+            ),
+        )
+
+        payload = _transformers.t_inline_results([eval_result])
+        assert len(payload) == 1
+
+        agent_data = payload[0]["request"]["prompt"]["agent_data"]
+        agent_config = agent_data["agents"]["agent_0"]
+        assert "tools" in agent_config
+        tool = agent_config["tools"][0]
+        # function_declarations should be preserved
+        assert "function_declarations" in tool
+        assert tool["function_declarations"][0]["name"] == "search"
+        # Gemini-API-only fields must NOT be present (they would be None)
+        assert "file_search" not in tool
+        assert "mcp_servers" not in tool
+        assert "google_search" not in tool
+        assert "code_execution" not in tool
+
     def test_t_inline_results_skips_error_agent_data_in_df(self):
         """Tests that t_inline_results skips error agent_data from DataFrame."""
         error_json = json.dumps({"error": "Agent run failed"})

vertexai/_genai/_transformers.py

@@ -422,7 +422,7 @@ def t_inline_results(
                 if agent_data:
                     if hasattr(agent_data, "model_dump"):
                         prompt_payload["agent_data"] = _sanitize_agent_data(
-                            agent_data.model_dump()
+                            agent_data.model_dump(exclude_none=True)
                         )
                     elif isinstance(agent_data, dict):
                         prompt_payload["agent_data"] = _sanitize_agent_data(agent_data)
@@ -442,7 +442,7 @@ def t_inline_results(
                 if df_agent_data is not None:
                     if hasattr(df_agent_data, "model_dump"):
                         prompt_payload["agent_data"] = _sanitize_agent_data(
-                            df_agent_data.model_dump()
+                            df_agent_data.model_dump(exclude_none=True)
                         )
                     elif isinstance(df_agent_data, str):
                         try: