[AINode] Fix call inference bug

integration-test/src/test/java/org/apache/iotdb/ainode/it/AINodeCallInferenceIT.java

@@ -48,6 +48,8 @@ public class AINodeCallInferenceIT {
 
   private static final String CALL_INFERENCE_SQL_TEMPLATE =
       "CALL INFERENCE(%s, \"SELECT s%d FROM root.AI LIMIT %d\", generateTime=true, outputLength=%d)";
+  private static final String CALL_INFERENCE_BY_DEFAULT_SQL_TEMPLATE =
+      "CALL INFERENCE(%s, \"SELECT s%d FROM root.AI LIMIT 256\")";
   private static final int DEFAULT_INPUT_LENGTH = 256;
   private static final int DEFAULT_OUTPUT_LENGTH = 48;
 
@@ -69,6 +71,7 @@ public void callInferenceTest() throws SQLException {
       try (Connection connection = EnvFactory.getEnv().getConnection(BaseEnv.TREE_SQL_DIALECT);
           Statement statement = connection.createStatement()) {
         callInferenceTest(statement, modelInfo);
+        callInferenceByDefaultTest(statement, modelInfo);
       }
     }
   }
@@ -96,4 +99,23 @@ public static void callInferenceTest(Statement statement, AINodeTestUtils.FakeMo
       }
     }
   }
+
+  public static void callInferenceByDefaultTest(
+      Statement statement, AINodeTestUtils.FakeModelInfo modelInfo) throws SQLException {
+    // Invoke call inference for specified models, there should exist result.
+    for (int i = 0; i < 4; i++) {
+      String callInferenceSQL =
+          String.format(CALL_INFERENCE_BY_DEFAULT_SQL_TEMPLATE, modelInfo.getModelId(), i);
+      try (ResultSet resultSet = statement.executeQuery(callInferenceSQL)) {
+        ResultSetMetaData resultSetMetaData = resultSet.getMetaData();
+        checkHeader(resultSetMetaData, "output");
+        int count = 0;
+        while (resultSet.next()) {
+          count++;
+        }
+        // Ensure the call inference return results
+        Assert.assertTrue(count > 0);
+      }
+    }
+  }
 }

iotdb-core/ainode/iotdb/ainode/core/manager/inference_manager.py

@@ -265,7 +265,11 @@ def inference(self, req: TInferenceReq):
             req,
             data_getter=lambda r: r.dataset,
             extract_attrs=lambda r: {
-                "output_length": int(r.inferenceAttributes.pop("outputLength", 96)),
+                "output_length": (
+                    96
+                    if r.inferenceAttributes is None
+                    else int(r.inferenceAttributes.pop("outputLength", 96))
+                ),
                 **(r.inferenceAttributes or {}),
             },
             resp_cls=TInferenceResp,