feat: Extend Bigquery detect_anomalies tool to support future data anomaly detection

ARIMA supports both historical data and future data anomaly detection. This CL add how the tool support future table anomaly detection.

PiperOrigin-RevId: 827803748

tests/unittests/tools/bigquery/test_bigquery_query_tool.py

@@ -1509,6 +1509,62 @@ def test_detect_anomalies_with_custom_params(mock_uuid, mock_execute_sql):
   )
 
 
+# detect_anomalies calls execute_sql twice. We need to test that
+# the queries are properly constructed and call execute_sql with the correct
+# parameters exactly twice.
+@mock.patch("google.adk.tools.bigquery.query_tool.execute_sql", autospec=True)
+@mock.patch("uuid.uuid4", autospec=True)
+def test_detect_anomalies_on_target_table(mock_uuid, mock_execute_sql):
+  """Test time series anomaly detection tool with target data is provided."""
+  mock_credentials = mock.MagicMock(spec=Credentials)
+  mock_settings = BigQueryToolConfig(write_mode=WriteMode.PROTECTED)
+  mock_tool_context = mock.create_autospec(ToolContext, instance=True)
+  mock_uuid.return_value = "test_uuid"
+  mock_execute_sql.return_value = {"status": "SUCCESS"}
+
+  history_data_query = "SELECT * FROM `test-dataset.history-table`"
+  target_data_query = "SELECT * FROM `test-dataset.target-table`"
+  detect_anomalies(
+      project_id="test-project",
+      history_data=history_data_query,
+      times_series_timestamp_col="ts_timestamp",
+      times_series_data_col="ts_data",
+      times_series_id_cols=["dim1", "dim2"],
+      horizon=20,
+      target_data=target_data_query,
+      anomaly_prob_threshold=0.8,
+      credentials=mock_credentials,
+      settings=mock_settings,
+      tool_context=mock_tool_context,
+  )
+
+  expected_create_model_query = """
+  CREATE TEMP MODEL detect_anomalies_model_test_uuid
+    OPTIONS (MODEL_TYPE = 'ARIMA_PLUS', TIME_SERIES_TIMESTAMP_COL = 'ts_timestamp', TIME_SERIES_DATA_COL = 'ts_data', HORIZON = 20, TIME_SERIES_ID_COL = ['dim1', 'dim2'])
+  AS (SELECT * FROM `test-dataset.history-table`)
+  """
+
+  expected_anomaly_detection_query = """
+    SELECT * FROM ML.DETECT_ANOMALIES(MODEL detect_anomalies_model_test_uuid, STRUCT(0.8 AS anomaly_prob_threshold), (SELECT * FROM `test-dataset.target-table`))
+    """
+
+  assert mock_execute_sql.call_count == 2
+  mock_execute_sql.assert_any_call(
+      "test-project",
+      expected_create_model_query,
+      mock_credentials,
+      mock_settings,
+      mock_tool_context,
+  )
+  mock_execute_sql.assert_any_call(
+      "test-project",
+      expected_anomaly_detection_query,
+      mock_credentials,
+      mock_settings,
+      mock_tool_context,
+  )
+
+
 def test_detect_anomalies__with_invalid_id_cols():
   """Test time series anomaly detection tool invocation with invalid times_series_id_cols."""
   mock_credentials = mock.MagicMock(spec=Credentials)