[ML] Versioning all ML APIs

x-pack/packages/ml/date_picker/src/services/full_time_range_selector_service.ts

@@ -22,7 +22,7 @@ import type { GetTimeFieldRangeResponse } from './types';
  */
 export type SetFullTimeRangeApiPath =
   | '/internal/file_upload/time_field_range'
-  | '/api/ml/fields_service/time_field_range';
+  | '/internal/ml/fields_service/time_field_range';
 
 /**
  * Determines the full available time range of the given Data View and updates

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

@@ -18,7 +18,7 @@ describe('MLModelsApiLogic', () => {
     it('calls the ml api', async () => {
       http.get.mockResolvedValue(mlModelStats);
       const result = await getMLModelsStats();
-      expect(http.get).toHaveBeenCalledWith('/api/ml/trained_models/_stats');
+      expect(http.get).toHaveBeenCalledWith('/internal/ml/trained_models/_stats', { version: '1' });
       expect(result).toEqual(mlModelStats);
     });
   });

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

@@ -17,7 +17,10 @@ export interface GetMlModelsStatsResponse {
 }
 
 export const getMLModelsStats = async () => {
-  return await HttpLogic.values.http.get<GetMlModelsStatsResponse>('/api/ml/trained_models/_stats');
+  return await HttpLogic.values.http.get<GetMlModelsStatsResponse>(
+    '/internal/ml/trained_models/_stats',
+    { version: '1' }
+  );
 };
 
 export const MLModelsStatsApiLogic = createApiLogic(

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

@@ -18,8 +18,9 @@ describe('MLModelsApiLogic', () => {
     it('calls the ml api', async () => {
       http.get.mockResolvedValue(mlModels);
       const result = await getMLModels();
-      expect(http.get).toHaveBeenCalledWith('/api/ml/trained_models', {
+      expect(http.get).toHaveBeenCalledWith('/internal/ml/trained_models', {
         query: { size: 1000, with_pipelines: true },
+        version: '1',
       });
       expect(result).toEqual(mlModels);
     });

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

@@ -14,9 +14,13 @@ export type GetMlModelsArgs = number | undefined;
 export type GetMlModelsResponse = TrainedModelConfigResponse[];
 
 export const getMLModels = async (size: GetMlModelsArgs = 1000) => {
-  return await HttpLogic.values.http.get<TrainedModelConfigResponse[]>('/api/ml/trained_models', {
-    query: { size, with_pipelines: true },
-  });
+  return await HttpLogic.values.http.get<TrainedModelConfigResponse[]>(
+    '/internal/ml/trained_models',
+    {
+      query: { size, with_pipelines: true },
+      version: '1',
+    }
+  );
 };
 
 export const MLModelsApiLogic = createApiLogic(['ml_models_api_logic'], getMLModels, {

x-pack/plugins/infra/public/containers/logs/log_analysis/api/ml_cleanup.ts

@@ -24,8 +24,9 @@ export const callDeleteJobs = async <JobType extends string>(
   const { spaceId, logViewId, jobTypes } = requestArgs;
 
   // NOTE: Deleting the jobs via this API will delete the datafeeds at the same time
-  const deleteJobsResponse = await fetch('/api/ml/jobs/delete_jobs', {
+  const deleteJobsResponse = await fetch('/internal/ml/jobs/delete_jobs', {
     method: 'POST',
+    version: '1',
     body: JSON.stringify(
       deleteJobsRequestPayloadRT.encode({
         jobIds: jobTypes.map((jobType) => getJobId(spaceId, logViewId, jobType)),
@@ -37,7 +38,9 @@ export const callDeleteJobs = async <JobType extends string>(
 };
 
 export const callGetJobDeletionTasks = async (fetch: HttpHandler) => {
-  const jobDeletionTasksResponse = await fetch('/api/ml/jobs/deleting_jobs_tasks');
+  const jobDeletionTasksResponse = await fetch('/internal/ml/jobs/deleting_jobs_tasks', {
+    version: '1',
+  });
 
   return decodeOrThrow(getJobDeletionTasksResponsePayloadRT)(jobDeletionTasksResponse);
 };
@@ -55,8 +58,9 @@ export const callStopDatafeeds = async <JobType extends string>(
   const { spaceId, logViewId, jobTypes } = requestArgs;
 
   // Stop datafeed due to https://github.com/elastic/kibana/issues/44652
-  const stopDatafeedResponse = await fetch('/api/ml/jobs/stop_datafeeds', {
+  const stopDatafeedResponse = await fetch('/internal/ml/jobs/stop_datafeeds', {
     method: 'POST',
+    version: '1',
     body: JSON.stringify(
       stopDatafeedsRequestPayloadRT.encode({
         datafeedIds: jobTypes.map((jobType) => getDatafeedId(spaceId, logViewId, jobType)),

x-pack/plugins/infra/public/containers/logs/log_analysis/api/ml_get_jobs_summary_api.ts

@@ -22,8 +22,9 @@ export const callJobsSummaryAPI = async <JobType extends string>(
   fetch: HttpHandler
 ) => {
   const { spaceId, logViewId, jobTypes } = requestArgs;
-  const response = await fetch('/api/ml/jobs/jobs_summary', {
+  const response = await fetch('/internal/ml/jobs/jobs_summary', {
     method: 'POST',
+    version: '1',
     body: JSON.stringify(
       fetchJobStatusRequestPayloadRT.encode({
         jobIds: jobTypes.map((jobType) => getJobId(spaceId, logViewId, jobType)),

x-pack/plugins/infra/public/containers/logs/log_analysis/api/ml_get_module.ts

@@ -12,8 +12,9 @@ import { jobCustomSettingsRT } from '../../../../../common/log_analysis';
 import { decodeOrThrow } from '../../../../../common/runtime_types';
 
 export const callGetMlModuleAPI = async (moduleId: string, fetch: HttpHandler) => {
-  const response = await fetch(`/api/ml/modules/get_module/${moduleId}`, {
+  const response = await fetch(`/internal/ml/modules/get_module/${moduleId}`, {
     method: 'GET',
+    version: '1',
   });
 
   return decodeOrThrow(getMlModuleResponsePayloadRT)(response);

x-pack/plugins/infra/public/containers/logs/log_analysis/api/ml_setup_module_api.ts

@@ -38,8 +38,9 @@ export const callSetupMlModuleAPI = async (requestArgs: RequestArgs, fetch: Http
     useDedicatedIndex = false,
   } = requestArgs;
 
-  const response = await fetch(`/api/ml/modules/setup/${moduleId}`, {
+  const response = await fetch(`/internal/ml/modules/setup/${moduleId}`, {
     method: 'POST',
+    version: '1',
     body: JSON.stringify(
       setupMlModuleRequestPayloadRT.encode({
         start,

x-pack/plugins/infra/public/containers/logs/log_analysis/log_analysis_capabilities.tsx

@@ -24,7 +24,9 @@ export const useLogAnalysisCapabilities = () => {
     {
       cancelPreviousOn: 'resolution',
       createPromise: async () => {
-        const rawResponse = await services.http.fetch('/api/ml/ml_capabilities');
+        const rawResponse = await services.http.fetch('/internal/ml/ml_capabilities', {
+          version: '1',
+        });
 
         return decodeOrThrow(getMlCapabilitiesResponsePayloadRT)(rawResponse);
       },

x-pack/plugins/infra/public/containers/ml/api/ml_cleanup.ts

@@ -23,8 +23,9 @@ export const callDeleteJobs = async <JobType extends string>(
   const { spaceId, sourceId, jobTypes } = requestArgs;
 
   // NOTE: Deleting the jobs via this API will delete the datafeeds at the same time
-  const deleteJobsResponse = await fetch('/api/ml/jobs/delete_jobs', {
+  const deleteJobsResponse = await fetch('/internal/ml/jobs/delete_jobs', {
     method: 'POST',
+    version: '1',
     body: JSON.stringify(
       deleteJobsRequestPayloadRT.encode({
         jobIds: jobTypes.map((jobType) => getJobId(spaceId, sourceId, jobType)),
@@ -36,7 +37,9 @@ export const callDeleteJobs = async <JobType extends string>(
 };
 
 export const callGetJobDeletionTasks = async (fetch: HttpHandler) => {
-  const jobDeletionTasksResponse = await fetch('/api/ml/jobs/deleting_jobs_tasks');
+  const jobDeletionTasksResponse = await fetch('/internal/ml/jobs/deleting_jobs_tasks', {
+    version: '1',
+  });
 
   return decodeOrThrow(getJobDeletionTasksResponsePayloadRT)(jobDeletionTasksResponse);
 };
@@ -54,8 +57,9 @@ export const callStopDatafeeds = async <JobType extends string>(
   const { spaceId, sourceId, jobTypes } = requestArgs;
 
   // Stop datafeed due to https://github.com/elastic/kibana/issues/44652
-  const stopDatafeedResponse = await fetch('/api/ml/jobs/stop_datafeeds', {
+  const stopDatafeedResponse = await fetch('/internal/ml/jobs/stop_datafeeds', {
     method: 'POST',
+    version: '1',
     body: JSON.stringify(
       stopDatafeedsRequestPayloadRT.encode({
         datafeedIds: jobTypes.map((jobType) => getDatafeedId(spaceId, sourceId, jobType)),

x-pack/plugins/infra/public/containers/ml/api/ml_get_jobs_summary_api.ts

@@ -22,8 +22,9 @@ export const callJobsSummaryAPI = async <JobType extends string>(
   fetch: HttpHandler
 ) => {
   const { spaceId, sourceId, jobTypes } = requestArgs;
-  const response = await fetch('/api/ml/jobs/jobs_summary', {
+  const response = await fetch('/internal/ml/jobs/jobs_summary', {
     method: 'POST',
+    version: '1',
     body: JSON.stringify(
       fetchJobStatusRequestPayloadRT.encode({
         jobIds: jobTypes.map((jobType) => getJobId(spaceId, sourceId, jobType)),

x-pack/plugins/infra/public/containers/ml/api/ml_get_module.ts

@@ -12,8 +12,9 @@ import { jobCustomSettingsRT } from '../../../../common/log_analysis';
 import { decodeOrThrow } from '../../../../common/runtime_types';
 
 export const callGetMlModuleAPI = async (moduleId: string, fetch: HttpHandler) => {
-  const response = await fetch(`/api/ml/modules/get_module/${moduleId}`, {
+  const response = await fetch(`/internal/ml/modules/get_module/${moduleId}`, {
     method: 'GET',
+    version: '1',
   });
 
   return decodeOrThrow(getMlModuleResponsePayloadRT)(response);

x-pack/plugins/infra/public/containers/ml/api/ml_setup_module_api.ts

@@ -36,8 +36,9 @@ export const callSetupMlModuleAPI = async (requestArgs: RequestArgs, fetch: Http
     query,
   } = requestArgs;
 
-  const response = await fetch(`/api/ml/modules/setup/${moduleId}`, {
+  const response = await fetch(`/internal/ml/modules/setup/${moduleId}`, {
     method: 'POST',
+    version: '1',
     body: JSON.stringify(
       setupMlModuleRequestPayloadRT.encode({
         start,

x-pack/plugins/infra/public/containers/ml/infra_ml_capabilities.tsx

@@ -27,7 +27,9 @@ export const useInfraMLCapabilities = () => {
     {
       cancelPreviousOn: 'resolution',
       createPromise: async () => {
-        const rawResponse = await services.http.fetch('/api/ml/ml_capabilities');
+        const rawResponse = await services.http.fetch('/internal/ml/ml_capabilities', {
+          version: '1',
+        });
 
         return pipe(
           getMlCapabilitiesResponsePayloadRT.decode(rawResponse),

x-pack/plugins/ml/common/constants/app.ts

@@ -13,4 +13,5 @@ export const PLUGIN_ICON_SOLUTION = 'logoKibana';
 export const ML_APP_NAME = i18n.translate('xpack.ml.navMenu.mlAppNameText', {
   defaultMessage: 'Machine Learning',
 });
-export const ML_BASE_PATH = '/api/ml';
+export const ML_INTERNAL_BASE_PATH = '/internal/ml';
+export const ML_EXTERNAL_BASE_PATH = '/api/ml';

x-pack/plugins/ml/common/types/anomaly_detection_jobs/summary_job.ts

@@ -90,7 +90,7 @@ export interface MlSummaryJob {
 
   /**
    * Used in older implementations of the job config, where the datafeed was placed inside the job for convenience.
-   * This will be populated if the job's id has been passed to the /api/ml/jobs/jobs_summary endpoint.
+   * This will be populated if the job's id has been passed to the /internal/ml/jobs/jobs_summary endpoint.
    */
   fullJob?: CombinedJob;
 

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

@@ -58,6 +58,14 @@ export interface Module {
   kibana: KibanaObjects;
 }
 
+export interface RecognizeResult {
+  id: string;
+  title: string;
+  query: any;
+  description: string;
+  logo: Logo;
+}
+
 export interface FileBasedModule extends Omit<Module, 'jobs' | 'datafeeds' | 'kibana'> {
   jobs: Array<{ file: string; id: string }>;
   datafeeds: Array<{ file: string; job_id: string; id: string }>;

x-pack/plugins/ml/common/util/job_utils.ts

@@ -916,3 +916,20 @@ export function isKnownEmptyQuery(query: QueryDslQueryContainer) {
 
   return false;
 }
+
+/**
+ * Extract unique influencers from the job or collection of jobs
+ * @param jobs
+ */
+export function extractInfluencers(jobs: Job | Job[]): string[] {
+  if (!Array.isArray(jobs)) {
+    jobs = [jobs];
+  }
+  const influencers = new Set<string>();
+  for (const job of jobs) {
+    for (const influencer of job.analysis_config.influencers || []) {
+      influencers.add(influencer);
+    }
+  }
+  return Array.from(influencers);
+}

x-pack/plugins/ml/public/application/datavisualizer/index_based/index_data_visualizer.tsx

@@ -23,26 +23,18 @@ import { mlNodesAvailable, getMlNodeCount } from '../../ml_nodes_check/check_ml_
 import { checkPermission } from '../../capabilities/check_capabilities';
 import { MlPageHeader } from '../../components/page_header';
 
-interface RecognizerModule {
-  id: string;
-  title: string;
-  query: Record<string, object>;
-  description: string;
-  logo: {
-    icon: string;
-  };
-}
-
 export const IndexDataVisualizerPage: FC = () => {
   useTimefilter({ timeRangeSelector: false, autoRefreshSelector: false });
   const {
     services: {
-      http,
       docLinks,
       dataVisualizer,
       data: {
         dataViews: { get: getDataView },
       },
+      mlServices: {
+        mlApiServices: { recognizeIndex },
+      },
     },
   } = useMlKibana();
   const mlLocator = useMlLocator()!;
@@ -140,18 +132,14 @@ export const IndexDataVisualizerPage: FC = () => {
   const getAsyncRecognizedModuleCards = async (params: GetAdditionalLinksParams) => {
     const { dataViewId, dataViewTitle } = params;
     try {
-      const modules = await http.fetch<RecognizerModule[]>(
-        `/api/ml/modules/recognize/${dataViewTitle}`,
-        {
-          method: 'GET',
-        }
-      );
+      const modules = await recognizeIndex({ indexPatternTitle: dataViewTitle! });
+
       return modules?.map(
         (m): ResultLink => ({
           id: m.id,
           title: m.title,
           description: m.description,
-          icon: m.logo.icon,
+          icon: m.logo?.icon ?? '',
           type: 'index',
           getUrl: async () => {
             return await mlLocator.getUrl({

x-pack/plugins/ml/public/application/jobs/new_job/pages/components/time_range_step/time_range.tsx

@@ -13,6 +13,7 @@ import moment from 'moment';
 import { FullTimeRangeSelector, FROZEN_TIER_PREFERENCE } from '@kbn/ml-date-picker';
 import { useTimefilter, type GetTimeFieldRangeResponse } from '@kbn/ml-date-picker';
 import { useStorage } from '@kbn/ml-local-storage';
+import { ML_INTERNAL_BASE_PATH } from '../../../../../../../common/constants/app';
 import { WizardNav } from '../wizard_nav';
 import { StepProps, WIZARD_STEPS } from '../step_types';
 import { JobCreatorContext } from '../job_creator_context';
@@ -135,7 +136,7 @@ export const TimeRangeStep: FC<StepProps> = ({ setCurrentStep, isCurrentStep })
                 disabled={false}
                 callback={fullTimeRangeCallback}
                 timefilter={timefilter}
-                apiPath="/api/ml/fields_service/time_field_range"
+                apiPath={`${ML_INTERNAL_BASE_PATH}/fields_service/time_field_range`}
               />
             </EuiFlexItem>
             <EuiFlexItem />

x-pack/plugins/ml/public/application/services/anomaly_detector_service.ts

@@ -7,53 +7,32 @@
 
 import { Observable } from 'rxjs';
 import { map } from 'rxjs/operators';
-import { Job, JobId } from '../../../common/types/anomaly_detection_jobs';
-import { basePath } from './ml_api_service';
+import type { Job, JobId } from '../../../common/types/anomaly_detection_jobs';
 import { HttpService } from './http_service';
+import { type MlApiServices, mlApiServicesProvider } from './ml_api_service';
 
 export class AnomalyDetectorService {
-  private readonly apiBasePath = basePath() + '/anomaly_detectors';
+  private mlApiServices: MlApiServices;
 
-  constructor(private httpService: HttpService) {}
+  constructor(httpService: HttpService) {
+    this.mlApiServices = mlApiServicesProvider(httpService);
+  }
 
   /**
    * Fetches a single job object
    * @param jobId
    */
   getJobById$(jobId: JobId): Observable<Job> {
-    return this.httpService
-      .http$<{ count: number; jobs: Job[] }>({
-        path: `${this.apiBasePath}/${jobId}`,
-      })
-      .pipe(map((response) => response.jobs[0]));
+    return this.getJobs$([jobId]).pipe(map((jobs) => jobs[0]));
   }
 
   /**
    * Fetches anomaly detection jobs by ids
    * @param jobIds
    */
   getJobs$(jobIds: JobId[]): Observable<Job[]> {
-    return this.httpService
-      .http$<{ count: number; jobs: Job[] }>({
-        path: `${this.apiBasePath}/${jobIds.join(',')}`,
-      })
+    return this.mlApiServices
+      .getJobs$({ jobId: jobIds.join(',') })
       .pipe(map((response) => response.jobs));
   }
-
-  /**
-   * Extract unique influencers from the job or collection of jobs
-   * @param jobs
-   */
-  extractInfluencers(jobs: Job | Job[]): string[] {
-    if (!Array.isArray(jobs)) {
-      jobs = [jobs];
-    }
-    const influencers = new Set<string>();
-    for (const job of jobs) {
-      for (const influencer of job.analysis_config.influencers || []) {
-        influencers.add(influencer);
-      }
-    }
-    return Array.from(influencers);
-  }
 }