feat: GenAI Client(evals) - Support eval_cases with multi-turn agent_data in run_inference()

PiperOrigin-RevId: 908269463

Author: jsondai

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

@@ -0,0 +1,95 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# pylint: disable=protected-access,bad-continuation,missing-function-docstring
+
+from tests.unit.vertexai.genai.replays import pytest_helper
+from vertexai._genai import types
+from google.genai import types as genai_types
+
+
+def test_inference_with_eval_cases_multi_turn_agent_data(client):
+    """Tests run_inference with multi-turn agent_data in eval_cases.
+
+    Verifies that run_inference() accepts an EvaluationDataset with
+    eval_cases containing agent_data (no eval_dataset_df). The agent_data
+    has 2 turns: turn 0 is a completed user+agent exchange (history),
+    turn 1 is a new user query. The agent should see the history and
+    respond to the final query in context.
+    """
+    from google.adk.agents import LlmAgent
+
+    agent = LlmAgent(
+        name="test_agent",
+        model="gemini-2.5-flash",
+        instruction="You are a helpful assistant. Answer questions concisely.",
+    )
+
+    eval_case = types.EvalCase(
+        agent_data=types.evals.AgentData(
+            turns=[
+                types.evals.ConversationTurn(
+                    turn_index=0,
+                    events=[
+                        types.evals.AgentEvent(
+                            author="user",
+                            content=genai_types.Content(
+                                role="user",
+                                parts=[genai_types.Part(text="My name is Alice.")],
+                            ),
+                        ),
+                        types.evals.AgentEvent(
+                            author="test_agent",
+                            content=genai_types.Content(
+                                role="model",
+                                parts=[
+                                    genai_types.Part(
+                                        text="Hello Alice! How can I help you?"
+                                    )
+                                ],
+                            ),
+                        ),
+                    ],
+                ),
+                types.evals.ConversationTurn(
+                    turn_index=1,
+                    events=[
+                        types.evals.AgentEvent(
+                            author="user",
+                            content=genai_types.Content(
+                                role="user",
+                                parts=[genai_types.Part(text="What is my name?")],
+                            ),
+                        ),
+                    ],
+                ),
+            ],
+        ),
+    )
+    eval_dataset = types.EvaluationDataset(eval_cases=[eval_case])
+
+    inference_result = client.evals.run_inference(
+        agent=agent,
+        src=eval_dataset,
+    )
+    assert isinstance(inference_result, types.EvaluationDataset)
+    assert inference_result.eval_dataset_df is not None
+    assert "agent_data" in inference_result.eval_dataset_df.columns
+
+
+pytestmark = pytest_helper.setup(
+    file=__file__,
+    globals_for_file=globals(),
+    test_method="evals.run_inference",
+)