fix: GenAI Client(evals)-Propagate Vertex AI client config to LlmBackedUserSimulator

vertexai/_genai/_evals_common.py

@@ -559,6 +559,7 @@ def agent_run_wrapper(  # type: ignore[no-untyped-def]
                                 contents=contents_arg,
                                 user_simulator_config=user_simulator_config_arg,
                                 agent=agent_arg,
+                                api_client=api_client_arg,
                             )
 
                     future = executor.submit(
@@ -955,6 +956,7 @@ async def _run_adk_user_simulation(
     row: pd.Series,
     agent: "LlmAgent",  # type: ignore # noqa: F821
     config: Optional[types.evals.UserSimulatorConfig] = None,
+    api_client: Optional[BaseApiClient] = None,
 ) -> list[dict[str, Any]]:
     """Runs a multi-turn user simulation using ADK's EvaluationGenerator."""
     # Lazy-import ADK dependencies to avoid top-level import failures when
@@ -996,6 +998,23 @@ async def _run_adk_user_simulation(
         if config.max_turn is not None:
             user_simulator_kwargs["max_allowed_invocations"] = config.max_turn
 
+    # When using a Vertex AI client, convert the user simulator model name to
+    # a full resource path so that ADK's Gemini class automatically uses the
+    # Vertex AI backend.  This removes the need for users to manually set the
+    # GOOGLE_GENAI_USE_VERTEXAI environment variable.
+    if (
+        api_client
+        and getattr(api_client, "project", None)
+        and getattr(api_client, "location", None)
+    ):
+        model_name = user_simulator_kwargs.get("model", "gemini-2.5-flash")
+        if not model_name.startswith("projects/"):
+            user_simulator_kwargs["model"] = (
+                f"projects/{api_client.project}"
+                f"/locations/{api_client.location}"
+                f"/publishers/google/models/{model_name}"
+            )
+
     user_simulator_config = LlmBackedUserSimulatorConfig(**user_simulator_kwargs)
     user_simulator = LlmBackedUserSimulator(
         conversation_scenario=scenario, config=user_simulator_config
@@ -2095,11 +2114,12 @@ def _execute_local_agent_run_with_retry(
     agent: "LlmAgent",  # type: ignore # noqa: F821
     max_retries: int = 3,
     user_simulator_config: Optional[types.evals.UserSimulatorConfig] = None,
+    api_client: Optional[BaseApiClient] = None,
 ) -> Union[list[dict[str, Any]], dict[str, Any]]:
     """Executes agent run locally for a single prompt synchronously."""
     return asyncio.run(
         _execute_local_agent_run_with_retry_async(
-            row, contents, agent, max_retries, user_simulator_config
+            row, contents, agent, max_retries, user_simulator_config, api_client
         )
     )
 
@@ -2110,6 +2130,7 @@ async def _execute_local_agent_run_with_retry_async(
     agent: "LlmAgent",  # type: ignore # noqa: F821
     max_retries: int = 3,
     user_simulator_config: Optional[types.evals.UserSimulatorConfig] = None,
+    api_client: Optional[BaseApiClient] = None,
 ) -> Union[list[dict[str, Any]], dict[str, Any]]:
     """Executes agent run locally for a single prompt asynchronously."""
     # Lazy-import ADK dependencies to avoid top-level import failures when
@@ -2120,7 +2141,9 @@ async def _execute_local_agent_run_with_retry_async(
     # Multi-turn agent scraping with user simulation.
     if user_simulator_config or "conversation_plan" in row:
         try:
-            return await _run_adk_user_simulation(row, agent, user_simulator_config)
+            return await _run_adk_user_simulation(
+                row, agent, user_simulator_config, api_client
+            )
         except Exception as e:  # pylint: disable=broad-exception-caught
             logger.error("Multi-turn agent run with user simulation failed: %s", e)
             return {"error": f"Multi-turn agent run with user simulation failed: {e}"}