feat: Renaming.

PiperOrigin-RevId: 945668576

Author: vertex-sdk-bot

agentplatform/_genai/_datasets_utils.py

@@ -280,63 +280,6 @@ async def save_dataframe_to_bigquery_async(
     await asyncio.to_thread(bq_client.delete_table, temp_table_id)
 
 
-def load_dataframe_from_bigquery(
-    *,
-    bigquery_uri: str,
-    project: str,
-    location: str,
-    credentials: google.auth.credentials.Credentials,
-) -> "bigframes.pandas.DataFrame":  # type: ignore # noqa: F821
-    """Loads a BigQuery table into a BigFrames DataFrame.
-
-    Args:
-      bigquery_uri: The URI of the BigQuery table, with or without the `bq://`
-        prefix.
-      project: The project to use for the BigFrames session.
-      location: The location to use for the BigFrames session.
-      credentials: The credentials to use for the BigFrames session.
-
-    Returns:
-      A BigFrames DataFrame backed by the BigQuery table.
-    """
-    bigframes = _try_import_bigframes()
-    session_options = bigframes.BigQueryOptions(
-        credentials=credentials,
-        project=project,
-        location=location,
-    )
-    with bigframes.connect(session_options) as session:
-        return session.read_gbq(bigquery_uri.removeprefix("bq://"))
-
-
-async def load_dataframe_from_bigquery_async(
-    *,
-    bigquery_uri: str,
-    project: str,
-    location: str,
-    credentials: google.auth.credentials.Credentials,
-) -> "bigframes.pandas.DataFrame":  # type: ignore # noqa: F821
-    """Loads a BigQuery table into a BigFrames DataFrame.
-
-    Args:
-      bigquery_uri: The URI of the BigQuery table, with or without the `bq://`
-        prefix.
-      project: The project to use for the BigFrames session.
-      location: The location to use for the BigFrames session.
-      credentials: The credentials to use for the BigFrames session.
-
-    Returns:
-      A BigFrames DataFrame backed by the BigQuery table.
-    """
-    return await asyncio.to_thread(
-        load_dataframe_from_bigquery,
-        bigquery_uri=bigquery_uri,
-        project=project,
-        location=location,
-        credentials=credentials,
-    )
-
-
 def resolve_dataset_name(resource_name_or_id: str, project: str, location: str) -> str:
     """Resolves a dataset name or ID to a full resource name."""
     if "/" not in resource_name_or_id:

agentplatform/_genai/_evals_common.py

@@ -100,7 +100,7 @@ def _get_api_client_with_location(
 
 def _get_agent_engine_instance(
     agent_name: str, api_client: BaseApiClient
-) -> Union[types.AgentEngine, Any]:
+) -> Union[types.Runtime, Any]:
     """Gets or creates an agent engine instance for the current thread."""
     if not hasattr(_thread_local_data, "agent_engine_instances"):
         _thread_local_data.agent_engine_instances = {}
@@ -556,31 +556,12 @@ def _resolve_inference_configs(
     return inference_configs
 
 
-def _is_gemini_agent_resource(agent: str) -> bool:
-    """Returns True if `agent` is a Gemini Agent resource name.
-
-    A Gemini Agent resource name has the format
-    `projects/{project}/locations/{location}/agents/{agent}`, as opposed to an
-    Agent Engine resource name which uses `.../reasoningEngines/{id}`.
-    """
-    parts = agent.split("/")
-    return (
-        len(parts) == 6
-        and parts[0] == "projects"
-        and parts[2] == "locations"
-        and parts[4] == "agents"
-        and bool(parts[1])
-        and bool(parts[3])
-        and bool(parts[5])
-    )
-
-
 def _add_evaluation_run_labels(
     labels: Optional[dict[str, str]] = None,
     agent: Optional[str] = None,
 ) -> Optional[dict[str, str]]:
     """Adds labels to the evaluation run."""
-    if agent and "reasoningEngines/" in agent and not _is_gemini_agent_resource(agent):
+    if agent:
         labels = labels or {}
         labels["vertex-ai-evaluation-agent-engine-id"] = agent.split(
             "reasoningEngines/"
@@ -614,7 +595,7 @@ def _execute_inference_concurrently(
     model_or_fn: Optional[Union[str, Callable[[Any], Any]]] = None,
     gemini_config: Optional[genai_types.GenerateContentConfig] = None,
     inference_fn: Optional[Callable[..., Any]] = None,
-    agent_engine: Optional[Union[str, types.AgentEngine]] = None,
+    agent_engine: Optional[Union[str, types.Runtime]] = None,
     agent: Optional["LlmAgent"] = None,  # type: ignore # noqa: F821
     user_simulator_config: Optional[types.evals.UserSimulatorConfig] = None,
 ) -> list[
@@ -1404,7 +1385,7 @@ def _execute_inference(
     api_client: BaseApiClient,
     src: Union[str, pd.DataFrame],
     model: Optional[Union[Callable[[Any], Any], str]] = None,
-    agent_engine: Optional[Union[str, types.AgentEngine]] = None,
+    agent_engine: Optional[Union[str, types.Runtime]] = None,
     agent: Optional["LlmAgent"] = None,  # type: ignore # noqa: F821
     dest: Optional[str] = None,
     config: Optional[genai_types.GenerateContentConfig] = None,
@@ -1499,7 +1480,7 @@ def _execute_inference(
                 f"Unsupported agent_engine type: {type(agent_engine)}. Expecting a"
                 " string (agent engine resource name in"
                 " 'projects/{project_id}/locations/{location_id}/reasoningEngines/{reasoning_engine_id}'"
-                " format) or a types.AgentEngine instance."
+                " format) or a types.Runtime instance."
             )
         if (
             _evals_constant.INTERMEDIATE_EVENTS in prompt_dataset.columns
@@ -2102,7 +2083,7 @@ def _create_agent_results_dataframe(
 
 def _run_agent_internal(
     api_client: BaseApiClient,
-    agent_engine: Optional[Union[str, types.AgentEngine]],
+    agent_engine: Optional[Union[str, types.Runtime]],
     agent: Optional["LlmAgent"],  # type: ignore # noqa: F821
     prompt_dataset: pd.DataFrame,
     user_simulator_config: Optional[types.evals.UserSimulatorConfig] = None,
@@ -2153,7 +2134,7 @@ def _run_agent_internal(
 
 def _run_agent(
     api_client: BaseApiClient,
-    agent_engine: Optional[Union[str, types.AgentEngine]],
+    agent_engine: Optional[Union[str, types.Runtime]],
     agent: Optional["LlmAgent"],  # type: ignore # noqa: F821
     prompt_dataset: pd.DataFrame,
     user_simulator_config: Optional[types.evals.UserSimulatorConfig] = None,
@@ -2224,7 +2205,7 @@ def _run_agent(
 
 def _create_agent_engine_session(
     *,
-    agent_engine: types.AgentEngine,
+    agent_engine: types.Runtime,
     user_id: str,
     session_state: Optional[dict[str, Any]] = None,
 ) -> Any:
@@ -2271,7 +2252,7 @@ def _create_agent_engine_session(
         operation = agent_engine.api_client.sessions.create(
             name=agent_engine.api_resource.name,
             user_id=user_id,
-            config=types.CreateAgentEngineSessionConfig(
+            config=types.CreateRuntimeSessionConfig(
                 session_state=session_state,
             ),
         )
@@ -2293,7 +2274,7 @@ def _create_agent_engine_session(
 def _execute_agent_run_with_retry(
     row: pd.Series,
     contents: Union[genai_types.ContentListUnion, genai_types.ContentListUnionDict],
-    agent_engine: types.AgentEngine,
+    agent_engine: types.Runtime,
     max_retries: int = 3,
 ) -> Union[list[dict[str, Any]], dict[str, Any]]:
     """Executes agent run over agent engine for a single prompt."""
@@ -2340,7 +2321,7 @@ def _execute_agent_run_with_retry(
                 author=ag_event.author or "user",
                 invocation_id="history",
                 timestamp=base_ts + datetime.timedelta(seconds=i),
-                config=types.AppendAgentEngineSessionEventConfig(
+                config=types.AppendRuntimeSessionEventConfig(
                     content=ag_event.content,
                 ),
             )

agentplatform/_genai/_evals_metric_handlers.py

@@ -703,16 +703,6 @@ def _build_evaluation_instance(
                     )
                 )
 
-    # An interactions data source is mutually exclusive with agent_data: when
-    # set, the backend fetches the interaction + Gemini Agent config and parses
-    # them into agent data server-side, so we must not also send agent_data.
-    interactions_data_source = getattr(eval_case, "interactions_data_source", None)
-    agent_data = (
-        None
-        if interactions_data_source is not None
-        else _eval_case_to_agent_data(eval_case, extracted_prompt, response_content)
-    )
-
     return types.EvaluationInstance(
         prompt=prompt_instance_data,
         response=_content_to_instance_data(response_content),
@@ -725,8 +715,9 @@ def _build_evaluation_instance(
         other_data=(
             types.MapInstance(map_instance=other_data_map) if other_data_map else None
         ),
-        agent_data=agent_data,
-        interactions_data_source=interactions_data_source,
+        agent_data=_eval_case_to_agent_data(
+            eval_case, extracted_prompt, response_content
+        ),
     )
 
 
@@ -1001,11 +992,7 @@ def _build_request_payload(
             eval_case, response_index, self.metric.name
         )
 
-        if (
-            not response_content
-            and not getattr(eval_case, "agent_data", None)
-            and not getattr(eval_case, "interactions_data_source", None)
-        ):
+        if not response_content and not getattr(eval_case, "agent_data", None):
             raise ValueError(
                 f"Response content missing for candidate {response_index}."
             )
@@ -1604,10 +1591,7 @@ def compute_metrics_and_aggregate(
                     num_responses = (
                         len(eval_case.responses) if eval_case.responses else 0
                     )
-                    if num_responses == 0 and (
-                        getattr(eval_case, "agent_data", None)
-                        or getattr(eval_case, "interactions_data_source", None)
-                    ):
+                    if num_responses == 0 and getattr(eval_case, "agent_data", None):
                         num_responses = 1
 
                     actual_num_candidates_for_case = min(