Fix PR #2976 bug due to missing adding function_name and algorithm in querying models

plugin/src/main/java/org/opensearch/ml/autoredeploy/MLModelAutoReDeployer.java

@@ -241,7 +241,9 @@ private void queryRunningModels(ActionListener<SearchResponse> listener) {
         String[] includes = new String[] {
             MLModel.AUTO_REDEPLOY_RETRY_TIMES_FIELD,
             MLModel.PLANNING_WORKER_NODES_FIELD,
-            MLModel.DEPLOY_TO_ALL_NODES_FIELD };
+            MLModel.DEPLOY_TO_ALL_NODES_FIELD,
+            MLModel.FUNCTION_NAME_FIELD,
+            MLModel.ALGORITHM_FIELD };
 
         String[] excludes = new String[] { MLModel.MODEL_CONTENT_FIELD, MLModel.OLD_MODEL_CONTENT_FIELD };
         FetchSourceContext fetchContext = new FetchSourceContext(true, includes, excludes);
@@ -263,6 +265,14 @@ private void triggerModelRedeploy(ModelAutoRedeployArrangement modelAutoRedeploy
         String functionName = (String) Optional
             .ofNullable(sourceAsMap.get(MLModel.FUNCTION_NAME_FIELD))
             .orElse(sourceAsMap.get(MLModel.ALGORITHM_FIELD));
+        if (functionName == null) {
+            log
+                .error(
+                    "Model function_name or algorithm is null, model is not in correct status, please check the model, model id is: {}",
+                    modelId
+                );
+            return;
+        }
         if (FunctionName.REMOTE == FunctionName.from(functionName)) {
             log.info("Skipping redeploying remote model {} as remote model deployment can be done at prediction time.", modelId);
             return;

plugin/src/test/java/org/opensearch/ml/autoredeploy/MLModelAutoReDeployerIT.java

@@ -0,0 +1,73 @@
+/*
+ *
+ *  * Copyright OpenSearch Contributors
+ *  * SPDX-License-Identifier: Apache-2.0
+ *
+ */
+
+package org.opensearch.ml.autoredeploy;
+
+import static org.opensearch.ml.common.MLTask.MODEL_ID_FIELD;
+
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Path;
+
+import org.junit.Before;
+import org.opensearch.ml.common.MLTaskState;
+import org.opensearch.ml.rest.MLCommonsRestTestCase;
+
+import lombok.SneakyThrows;
+
+public class MLModelAutoReDeployerIT extends MLCommonsRestTestCase {
+
+    @Before
+    public void setup() throws IOException {
+        prepareModel();
+    }
+
+    @SneakyThrows
+    private void prepareModel() {
+        String requestBody = Files
+            .readString(
+                Path.of(this.getClass().getClassLoader().getResource("org/opensearch/ml/autoredeploy/TracedSmallModelRequest.json").toURI())
+            );
+        String registerFirstModelTaskId = registerModel(requestBody);
+        String registerSecondModelTaskId = registerModel(requestBody);
+        waitForTask(registerFirstModelTaskId, MLTaskState.COMPLETED);
+        getTask(client(), registerFirstModelTaskId, response -> {
+            String firstModelId = (String) response.get(MODEL_ID_FIELD);
+            try {
+                String deployFirstModelTaskId = deployModel(firstModelId);
+                getTask(client(), registerSecondModelTaskId, response1 -> {
+                    String secondModelId = (String) response1.get(MODEL_ID_FIELD);
+                    try {
+                        /**
+                         * At this time point, the model auto redeployer should be querying the deploying/deploy failed/partially deployed models.
+                         * The original deploy model task should be able to complete successfully, if not it means the
+                         * org.opensearch.ml.action.forward.TransportForwardAction.triggerNextModelDeployAndCheckIfRestRetryTimes might throw exception
+                         * which cause by org.opensearch.ml.autoredeploy.MLModelAutoReDeployer#redeployAModel. The auto redeploy constructs an arrangement
+                         * with two models, the first model deploy done event will trigger the auto redeploy's next model deploy, and if during this
+                         * any error occurs, the first model deploy task status won't be updated to complete. So if this IT can pass, then it means the
+                         * next model auto redeploy trigger is correct.
+                         */
+                        String deploySecondModelTaskId = deployModel(secondModelId);
+                        waitForTask(deploySecondModelTaskId, MLTaskState.COMPLETED);
+                    } catch (Exception e) {
+                        fail(e.getMessage());
+                    }
+                });
+                waitForTask(deployFirstModelTaskId, MLTaskState.COMPLETED);
+            } catch (Exception e) {
+                logger.error(e.getMessage(), e);
+                fail(e.getMessage());
+            }
+        });
+    }
+
+    public void testModelAutoRedeploy() {
+        // This is a tricky IT to test model auto redeploy, since model auto redeploy is not easily to test with IT, so adding this test
+        // to mimic a case when a cluster spin up and immediately deploy a model, the model auto redeployer also selected this deploying
+        // model to deploy, and the original deploy task should complete and the auto redeploy task should fail in background.
+    }
+}

plugin/src/test/resources/org/opensearch/ml/autoredeploy/TracedSmallModelRequest.json

@@ -0,0 +1,14 @@
+{
+  "name": "traced_small_model",
+  "version": "1.0.0",
+  "model_format": "TORCH_SCRIPT",
+  "model_task_type": "text_embedding",
+  "model_content_hash_value": "e13b74006290a9d0f58c1376f9629d4ebc05a0f9385f40db837452b167ae9021",
+  "model_config": {
+    "model_type": "bert",
+    "embedding_dimension": 768,
+    "framework_type": "sentence_transformers",
+    "all_config": "{\"architectures\":[\"BertModel\"],\"max_position_embeddings\":512,\"model_type\":\"bert\",\"num_attention_heads\":12,\"num_hidden_layers\":6}"
+  },
+  "url": "https://github.com/opensearch-project/ml-commons/blob/2.x/ml-algorithms/src/test/resources/org/opensearch/ml/engine/algorithms/text_embedding/traced_small_model.zip?raw=true"
+}