[Search] Consolidate ML model fetch calls (elastic#176257)

## Summary

With the introduction of
[fetch_ml_models.ts](https://github.com/elastic/kibana/blob/main/x-pack/plugins/enterprise_search/server/lib/ml/fetch_ml_models.ts),
the fetching and enriching of ML models for Search purposes has been
consolidated in that API. This allows us to remove the dependency on the
older method that works with ML plugin-specific `TrainedModel` entities.

This PR makes the following changes:
- Switch over code that depend on ML models to use the new function from
`fetch_ml_models.ts` (that already does sorting/filtering).
- Move the fetch process to `ml_inference_logic.ts`, and begin
periodically polling after mounting the logic. This enables passing down
values to lower components, e.g. `model_select_logic.ts`, instead of
repeating the fetch there.
- Use `MlModel` instead of `TrainedModel/MlTrainedModelConfig`. This
requires adding some missing properties to `MlModel`: `types`,
`inputFieldNames`, `version`.
- Remove the old fetch methods
(`x-pack/plugins/enterprise_search/server/lib/ml/ml_*_logic.ts`).
- Remove the "no models available" component and condition, since as of
8.12 at least the ELSER/E5 placeholders are always present.

### Checklist
- [x] [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

---------

Co-authored-by: Kibana Machine <42973632+kibanamachine@users.noreply.github.com>

x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.test.ts

@@ -8,6 +8,7 @@
 import { MlTrainedModelConfig, MlTrainedModelStats } from '@elastic/elasticsearch/lib/api/types';
 import { BUILT_IN_MODEL_TAG, TRAINED_MODEL_TYPE } from '@kbn/ml-trained-models-utils';
 
+import { MlModel, MlModelDeploymentState } from '../types/ml';
 import { MlInferencePipeline, TrainedModelState } from '../types/pipelines';
 
 import {
@@ -19,7 +20,7 @@ import {
   parseModelStateReasonFromStats,
 } from '.';
 
-const mockModel: MlTrainedModelConfig = {
+const mockTrainedModel: MlTrainedModelConfig = {
   inference_config: {
     ner: {},
   },
@@ -32,8 +33,27 @@ const mockModel: MlTrainedModelConfig = {
   version: '1',
 };
 
+const mockModel: MlModel = {
+  modelId: 'model_1',
+  type: 'ner',
+  title: 'Model 1',
+  description: 'Model 1 description',
+  licenseType: 'elastic',
+  modelDetailsPageUrl: 'https://my-model.ai',
+  deploymentState: MlModelDeploymentState.NotDeployed,
+  startTime: 0,
+  targetAllocationCount: 0,
+  nodeAllocationCount: 0,
+  threadsPerAllocation: 0,
+  isPlaceholder: false,
+  hasStats: false,
+  types: ['pytorch', 'ner'],
+  inputFieldNames: ['title'],
+  version: '1',
+};
+
 describe('getMlModelTypesForModelConfig lib function', () => {
-  const builtInMockModel: MlTrainedModelConfig = {
+  const builtInMockTrainedModel: MlTrainedModelConfig = {
     inference_config: {
       text_classification: {},
     },
@@ -47,13 +67,13 @@ describe('getMlModelTypesForModelConfig lib function', () => {
 
   it('should return the model type and inference config type', () => {
     const expected = ['pytorch', 'ner'];
-    const response = getMlModelTypesForModelConfig(mockModel);
+    const response = getMlModelTypesForModelConfig(mockTrainedModel);
     expect(response.sort()).toEqual(expected.sort());
   });
 
   it('should include the built in type', () => {
     const expected = ['lang_ident', 'text_classification', BUILT_IN_MODEL_TAG];
-    const response = getMlModelTypesForModelConfig(builtInMockModel);
+    const response = getMlModelTypesForModelConfig(builtInMockTrainedModel);
     expect(response.sort()).toEqual(expected.sort());
   });
 });
@@ -71,9 +91,9 @@ describe('generateMlInferencePipelineBody lib function', () => {
       {
         inference: {
           field_map: {
-            'my-source-field': 'MODEL_INPUT_FIELD',
+            'my-source-field': 'title',
           },
-          model_id: 'test_id',
+          model_id: 'model_1',
           on_failure: [
             {
               append: {
@@ -154,21 +174,21 @@ describe('generateMlInferencePipelineBody lib function', () => {
           {
             inference: expect.objectContaining({
               field_map: {
-                'my-source-field1': 'MODEL_INPUT_FIELD',
+                'my-source-field1': 'title',
               },
             }),
           },
           {
             inference: expect.objectContaining({
               field_map: {
-                'my-source-field2': 'MODEL_INPUT_FIELD',
+                'my-source-field2': 'title',
               },
             }),
           },
           {
             inference: expect.objectContaining({
               field_map: {
-                'my-source-field3': 'MODEL_INPUT_FIELD',
+                'my-source-field3': 'title',
               },
             }),
           },

x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.ts

@@ -18,6 +18,8 @@ import {
   BUILT_IN_MODEL_TAG,
 } from '@kbn/ml-trained-models-utils';
 
+import { MlModel } from '../types/ml';
+
 import {
   MlInferencePipeline,
   CreateMLInferencePipeline,
@@ -33,7 +35,7 @@ export interface MlInferencePipelineParams {
   description?: string;
   fieldMappings: FieldMapping[];
   inferenceConfig?: InferencePipelineInferenceConfig;
-  model: MlTrainedModelConfig;
+  model: MlModel;
   pipelineName: string;
 }
 
@@ -90,7 +92,7 @@ export const generateMlInferencePipelineBody = ({
             model_version: model.version,
             pipeline: pipelineName,
             processed_timestamp: '{{{ _ingest.timestamp }}}',
-            types: getMlModelTypesForModelConfig(model),
+            types: model.types,
           },
         ],
       },
@@ -104,19 +106,19 @@ export const getInferenceProcessor = (
   sourceField: string,
   targetField: string,
   inferenceConfig: InferencePipelineInferenceConfig | undefined,
-  model: MlTrainedModelConfig,
+  model: MlModel,
   pipelineName: string
 ): IngestInferenceProcessor => {
   // If model returned no input field, insert a placeholder
   const modelInputField =
-    model.input?.field_names?.length > 0 ? model.input.field_names[0] : 'MODEL_INPUT_FIELD';
+    model.inputFieldNames.length > 0 ? model.inputFieldNames[0] : 'MODEL_INPUT_FIELD';
 
   return {
     field_map: {
       [sourceField]: modelInputField,
     },
     inference_config: inferenceConfig,
-    model_id: model.model_id,
+    model_id: model.modelId,
     on_failure: [
       {
         append: {

x-pack/plugins/enterprise_search/common/types/ml.ts

@@ -27,6 +27,10 @@ export interface MlModel {
   modelId: string;
   /** Model inference type, e.g. ner, text_classification */
   type: string;
+  /** Type-related tags: model type (e.g. pytorch), inference type, built-in tag */
+  types: string[];
+  /** Field names in inference input configuration */
+  inputFieldNames: string[];
   title: string;
   description?: string;
   licenseType?: string;
@@ -44,4 +48,5 @@ export interface MlModel {
   isPlaceholder: boolean;
   /** Does this model have deployment stats? */
   hasStats: boolean;
+  version?: string;
 }

x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/ml_models/cached_fetch_models_api_logic.test.ts

@@ -30,15 +30,21 @@ const DEFAULT_VALUES: CachedFetchModelsApiLogicValues = {
 const FETCH_MODELS_API_DATA_RESPONSE: MlModel[] = [
   {
     modelId: 'model_1',
-    title: 'Model 1',
     type: 'ner',
+    title: 'Model 1',
+    description: 'Model 1 description',
+    licenseType: 'elastic',
+    modelDetailsPageUrl: 'https://my-model.ai',
     deploymentState: MlModelDeploymentState.NotDeployed,
     startTime: 0,
     targetAllocationCount: 0,
     nodeAllocationCount: 0,
     threadsPerAllocation: 0,
     isPlaceholder: false,
     hasStats: false,
+    types: ['pytorch', 'ner'],
+    inputFieldNames: ['title'],
+    version: '1',
   },
 ];
 const FETCH_MODELS_API_ERROR_RESPONSE = {

x-pack/plugins/enterprise_search/public/applications/enterprise_search_content/api/ml_models/cached_fetch_models_api_logic.ts

@@ -18,6 +18,8 @@ import { FetchModelsApiLogic, FetchModelsApiResponse } from './fetch_models_api_
 const FETCH_MODELS_POLLING_DURATION = 5000; // 5 seconds
 const FETCH_MODELS_POLLING_DURATION_ON_FAILURE = 30000; // 30 seconds
 
+export type { FetchModelsApiResponse } from './fetch_models_api_logic';
+
 export interface CachedFetchModlesApiLogicActions {
   apiError: Actions<{}, FetchModelsApiResponse>['apiError'];
   apiReset: Actions<{}, FetchModelsApiResponse>['apiReset'];