[Enterprise Search] Add Production Model Name to Allowed List for ELS…

…ER API Endpoints (#156020)

## Summary

Small PR to add the production model name to the `acceptableModelNames`
list that the Enteprise Search 1-Click deploy API endpoints use to check
to ensure we have a known, valid model name.

### Checklist
- [ ] [Unit or functional
tests](https://www.elastic.co/guide/en/kibana/master/development-tests.html)
were updated or added to match the most common scenarios

x-pack/plugins/enterprise_search/server/lib/ml/ml_model_deployment_common.ts

@@ -11,7 +11,7 @@ import {
   isResourceNotFoundException,
 } from '../../utils/identify_exceptions';
 
-export const acceptableModelNames = ['.elser_model_1_SNAPSHOT'];
+export const acceptableModelNames = ['.elser_model_1', '.elser_model_1_SNAPSHOT'];
 
 export function isNotFoundExceptionError(error: unknown): boolean {
   return (

x-pack/plugins/enterprise_search/server/lib/ml/start_ml_model_deployment.test.ts

@@ -15,7 +15,8 @@ import * as mockGetStatus from './get_ml_model_deployment_status';
 import { startMlModelDeployment } from './start_ml_model_deployment';
 
 describe('startMlModelDeployment', () => {
-  const knownModelName = '.elser_model_1_SNAPSHOT';
+  const productionModelName = '.elser_model_1';
+  const snapshotModelName = '.elser_model_1_SNAPSHOT';
   const mockTrainedModelsProvider = {
     getTrainedModels: jest.fn(),
     getTrainedModelsStats: jest.fn(),
@@ -27,7 +28,7 @@ describe('startMlModelDeployment', () => {
   });
 
   it('should error when there is no trained model provider', () => {
-    expect(() => startMlModelDeployment(knownModelName, undefined)).rejects.toThrowError(
+    expect(() => startMlModelDeployment(productionModelName, undefined)).rejects.toThrowError(
       'Machine Learning is not enabled'
     );
   });
@@ -49,15 +50,34 @@ describe('startMlModelDeployment', () => {
     jest.spyOn(mockGetStatus, 'getMlModelDeploymentStatus').mockReturnValueOnce(
       Promise.resolve({
         deploymentState: MlModelDeploymentState.Starting,
-        modelId: knownModelName,
+        modelId: productionModelName,
         nodeAllocationCount: 0,
         startTime: 123456,
         targetAllocationCount: 3,
       })
     );
 
     const response = await startMlModelDeployment(
-      knownModelName,
+      productionModelName,
+      mockTrainedModelsProvider as unknown as MlTrainedModels
+    );
+
+    expect(response.deploymentState).toEqual(MlModelDeploymentState.Starting);
+  });
+
+  it('should return the deployment state if not "downloaded" for snapshot model', async () => {
+    jest.spyOn(mockGetStatus, 'getMlModelDeploymentStatus').mockReturnValueOnce(
+      Promise.resolve({
+        deploymentState: MlModelDeploymentState.Starting,
+        modelId: snapshotModelName,
+        nodeAllocationCount: 0,
+        startTime: 123456,
+        targetAllocationCount: 3,
+      })
+    );
+
+    const response = await startMlModelDeployment(
+      snapshotModelName,
       mockTrainedModelsProvider as unknown as MlTrainedModels
     );
 
@@ -70,7 +90,7 @@ describe('startMlModelDeployment', () => {
       .mockReturnValueOnce(
         Promise.resolve({
           deploymentState: MlModelDeploymentState.Downloaded,
-          modelId: knownModelName,
+          modelId: productionModelName,
           nodeAllocationCount: 0,
           startTime: 123456,
           targetAllocationCount: 3,
@@ -79,7 +99,7 @@ describe('startMlModelDeployment', () => {
       .mockReturnValueOnce(
         Promise.resolve({
           deploymentState: MlModelDeploymentState.Starting,
-          modelId: knownModelName,
+          modelId: productionModelName,
           nodeAllocationCount: 0,
           startTime: 123456,
           targetAllocationCount: 3,
@@ -88,7 +108,7 @@ describe('startMlModelDeployment', () => {
     mockTrainedModelsProvider.startTrainedModelDeployment.mockImplementation(async () => {});
 
     const response = await startMlModelDeployment(
-      knownModelName,
+      productionModelName,
       mockTrainedModelsProvider as unknown as MlTrainedModels
     );
     expect(response.deploymentState).toEqual(MlModelDeploymentState.Starting);