Upgraded @types/node package to v18 (#2374)

(cherry picked from commit c274b1b32f)

.buildkite/make.mjs

@@ -125,6 +125,13 @@ async function codegen (args) {
   await $`cp -R ${join(import.meta.url, '..', '..', 'elastic-client-generator-js', 'output')}/* ${join(import.meta.url, '..', 'src', 'api')}`
   await $`mv ${join(import.meta.url, '..', 'src', 'api', 'reference.asciidoc')} ${join(import.meta.url, '..', 'docs', 'reference.asciidoc')}`
   await $`npm run build`
+
+  // run docs example generation
+  if (version === 'main') {
+    await $`node ./scripts/generate-docs-examples.js`
+  } else {
+    await $`node ./scripts/generate-docs-examples.js ${version.split('.').slice(0, 2).join('.')}`
+  }
 }
 
 function onError (err) {

.github/workflows/auto-merge.yml

@@ -0,0 +1,18 @@
+name: Automerge
+
+on:
+  pull_request_review:
+    types:
+      - submitted
+
+jobs:
+  automerge:
+    runs-on: ubuntu-latest
+    if: github.event.review.state == 'approved'
+    steps:
+      - uses: reitermarkus/automerge@v2
+        with:
+          token: ${{ secrets.GH_TOKEN }}
+          merge-method: squash
+          pull-request-author-associations: OWNER
+          review-author-associations: OWNER,CONTRIBUTOR

.github/workflows/nodejs.yml

@@ -2,7 +2,7 @@
 name: Node CI
 
 on:
-  pull_request_target: {}
+  pull_request: {}
 
 jobs:
   paths-filter:

docs/doc_examples/00fea15cbca83be9d5f1a024ff2ec708.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/my-e5-model",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "my-e5-model",
+  inference_config: {
     service: "elasticsearch",
     service_settings: {
       num_allocations: 1,

docs/doc_examples/01b23f09d2b7f140faf649eadbbf3ac3.asciidoc

@@ -26,7 +26,7 @@ const response1 = await client.cluster.putComponentTemplate({
           type: "keyword",
           script: {
             source:
-              "emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ROOT))",
+              "emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ENGLISH))",
           },
         },
       },

docs/doc_examples/01cd0ea360282a2c591a366679d7187d.asciidoc

@@ -0,0 +1,12 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.tasks.list({
+  human: "true",
+  detailed: "true",
+  actions: "indices:data/write/bulk",
+});
+console.log(response);
+----

docs/doc_examples/0393ca5a2942e1f00ed87546d0d50732.asciidoc

@@ -4,7 +4,7 @@
 [source, js]
 ----
 const response = await client.indices.create({
-  index: "my-index-000002",
+  index: "my-index-000003",
   mappings: {
     properties: {
       metrics: {
@@ -29,7 +29,7 @@ const response = await client.indices.create({
 console.log(response);
 
 const response1 = await client.indices.getMapping({
-  index: "my-index-000002",
+  index: "my-index-000003",
 });
 console.log(response1);
 ----

docs/doc_examples/04412d11783dac25b5fd2ec5407078a3.asciidoc

@@ -3,13 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector/_api_key_id",
-  body: {
-    api_key_id: "my-api-key-id",
-    api_key_secret_id: "my-connector-secret-id",
-  },
+const response = await client.connector.updateApiKeyId({
+  connector_id: "my-connector",
+  api_key_id: "my-api-key-id",
+  api_key_secret_id: "my-connector-secret-id",
 });
 console.log(response);
 ----

docs/doc_examples/04de2e3a9c00c2056b07bf9cf9e63a99.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/google_vertex_ai_embeddings",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "google_vertex_ai_embeddings",
+  inference_config: {
     service: "googlevertexai",
     service_settings: {
       service_account_json: "<service_account_json>",

docs/doc_examples/05e637284bc3bedd46e0b7c26ad983c4.asciidoc

@@ -0,0 +1,21 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.ingest.putPipeline({
+  id: "alibabacloud_ai_search_embeddings_pipeline",
+  processors: [
+    {
+      inference: {
+        model_id: "alibabacloud_ai_search_embeddings",
+        input_output: {
+          input_field: "content",
+          output_field: "content_embedding",
+        },
+      },
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/083b92e8ea264e49bf9fd40fc6a3094b.asciidoc

@@ -0,0 +1,23 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "my-e5-model",
+  inference_config: {
+    service: "elasticsearch",
+    service_settings: {
+      adaptive_allocations: {
+        enabled: true,
+        min_number_of_allocations: 3,
+        max_number_of_allocations: 10,
+      },
+      num_threads: 1,
+      model_id: ".multilingual-e5-small",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/0ad8edd10542ec2c4d5d8700d7e2ba97.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/amazon_bedrock_embeddings",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "amazon_bedrock_embeddings",
+  inference_config: {
     service: "amazonbedrock",
     service_settings: {
       access_key: "<aws_access_key>",

docs/doc_examples/0ade87c8cb0e3c188d2e3dce279d5cc2.asciidoc

@@ -3,29 +3,26 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-g-drive-connector/_filtering",
-  body: {
-    rules: [
-      {
-        field: "file_extension",
-        id: "exclude-txt-files",
-        order: 0,
-        policy: "exclude",
-        rule: "equals",
-        value: "txt",
-      },
-      {
-        field: "_",
-        id: "DEFAULT",
-        order: 1,
-        policy: "include",
-        rule: "regex",
-        value: ".*",
-      },
-    ],
-  },
+const response = await client.connector.updateFiltering({
+  connector_id: "my-g-drive-connector",
+  rules: [
+    {
+      field: "file_extension",
+      id: "exclude-txt-files",
+      order: 0,
+      policy: "exclude",
+      rule: "equals",
+      value: "txt",
+    },
+    {
+      field: "_",
+      id: "DEFAULT",
+      order: 1,
+      policy: "include",
+      rule: "regex",
+      value: ".*",
+    },
+  ],
 });
 console.log(response);
 ----

docs/doc_examples/0b4e50f1b5a0537cbb1a41276bb51c54.asciidoc

@@ -10,7 +10,7 @@ const response = await client.search({
       type: "keyword",
       script: {
         source:
-          "emit(doc['@timestamp'].value.dayOfWeekEnum\n        .getDisplayName(TextStyle.FULL, Locale.ROOT))",
+          "emit(doc['@timestamp'].value.dayOfWeekEnum\n        .getDisplayName(TextStyle.FULL, Locale.ENGLISH))",
       },
     },
   },

docs/doc_examples/0dfde6a9d953822fd4b3aa0121ddd8fb.asciidoc

@@ -5,6 +5,21 @@
 ----
 const response = await client.searchApplication.renderQuery({
   name: "my-app",
+  body: {
+    params: {
+      query_string: "my first query",
+      text_fields: [
+        {
+          name: "title",
+          boost: 5,
+        },
+        {
+          name: "description",
+          boost: 1,
+        },
+      ],
+    },
+  },
 });
 console.log(response);
 ----

docs/doc_examples/0fbca60a487f5f22a4d51d73b2434cc4.asciidoc

@@ -0,0 +1,20 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "elser-embeddings",
+  mappings: {
+    properties: {
+      content_embedding: {
+        type: "sparse_vector",
+      },
+      content: {
+        type: "text",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/10a16abe990288253ea25a1b1712fe3d.asciidoc

@@ -5,6 +5,11 @@
 ----
 const response = await client.security.queryUser({
   with_profile_uid: "true",
+  query: {
+    prefix: {
+      roles: "other",
+    },
+  },
 });
 console.log(response);
 ----

docs/doc_examples/13ecdf99114098c76b050397d9c3d4e6.asciidoc

@@ -3,13 +3,11 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "POST",
-  path: "/_inference/sparse_embedding/my-elser-model",
-  body: {
-    input:
-      "The sky above the port was the color of television tuned to a dead channel.",
-  },
+const response = await client.inference.inference({
+  task_type: "sparse_embedding",
+  inference_id: "my-elser-model",
+  input:
+    "The sky above the port was the color of television tuned to a dead channel.",
 });
 console.log(response);
 ----

docs/doc_examples/13fe12cdb73bc89f07a83f1e6b127511.asciidoc

@@ -0,0 +1,23 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "google-vertex-ai-embeddings",
+  mappings: {
+    properties: {
+      content_embedding: {
+        type: "dense_vector",
+        dims: 768,
+        element_type: "float",
+        similarity: "dot_product",
+      },
+      content: {
+        type: "text",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/14a33c364873c2f930ca83d0a3005389.asciidoc

@@ -0,0 +1,12 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.cluster.allocationExplain({
+  index: "my-index",
+  shard: 0,
+  primary: false,
+});
+console.log(response);
+----

docs/doc_examples/19c00c6b29bc7dbc5e92b3668da2da93.asciidoc

@@ -3,6 +3,25 @@
 
 [source, js]
 ----
-const response = await client.simulate.ingest({});
+const response = await client.simulate.ingest({
+  body: {
+    docs: [
+      {
+        _index: "my-index",
+        _id: "123",
+        _source: {
+          foo: "bar",
+        },
+      },
+      {
+        _index: "my-index",
+        _id: "456",
+        _source: {
+          foo: "rab",
+        },
+      },
+    ],
+  },
+});
 console.log(response);
 ----

docs/doc_examples/19d60e4890cc57151d596326484d9076.asciidoc

@@ -0,0 +1,11 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.transport.request({
+  method: "DELETE",
+  path: "/_ingest/geoip/database/my-database-id",
+});
+console.log(response);
+----

docs/doc_examples/1a56df055b94466ca76818e0858752c6.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/openai_embeddings",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "openai_embeddings",
+  inference_config: {
     service: "openai",
     service_settings: {
       api_key: "<api_key>",

docs/doc_examples/1a9e03ce0355872a7db27fedc783fbec.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/rerank/google_vertex_ai_rerank",
-  body: {
+const response = await client.inference.put({
+  task_type: "rerank",
+  inference_id: "google_vertex_ai_rerank",
+  inference_config: {
     service: "googlevertexai",
     service_settings: {
       service_account_json: "<service_account_json>",

docs/doc_examples/1c330f0fc9eac19d0edeb8c4017b9b93.asciidoc

@@ -4,7 +4,7 @@
 [source, js]
 ----
 const response = await client.ingest.putPipeline({
-  id: "hugging_face_embeddings",
+  id: "hugging_face_embeddings_pipeline",
   processors: [
     {
       inference: {

docs/doc_examples/1cbecd19be22979aefb45b4f160e77ea.asciidoc

@@ -0,0 +1,21 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.ingest.putPipeline({
+  id: "google_vertex_ai_embeddings_pipeline",
+  processors: [
+    {
+      inference: {
+        model_id: "google_vertex_ai_embeddings",
+        input_output: {
+          input_field: "content",
+          output_field: "content_embedding",
+        },
+      },
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/1dadb7efe27b6c0c231eb6535e413bd9.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/azure_ai_studio_embeddings",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "azure_ai_studio_embeddings",
+  inference_config: {
     service: "azureaistudio",
     service_settings: {
       api_key: "<api_key>",

docs/doc_examples/1e26353d546d733634187b8c3a7837a7.asciidoc

@@ -3,12 +3,8 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "GET",
-  path: "/_connector",
-  querystring: {
-    service_type: "sharepoint_online",
-  },
+const response = await client.connector.list({
+  service_type: "sharepoint_online",
 });
 console.log(response);
 ----

docs/doc_examples/20179a8889e949d6a8ee5fbf2ba35c96.asciidoc

@@ -0,0 +1,22 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "google-vertex-ai-embeddings",
+  knn: {
+    field: "content_embedding",
+    query_vector_builder: {
+      text_embedding: {
+        model_id: "google_vertex_ai_embeddings",
+        model_text: "Calculate fuel cost",
+      },
+    },
+    k: 10,
+    num_candidates: 100,
+  },
+  _source: ["id", "content"],
+});
+console.log(response);
+----

docs/doc_examples/21cd01cb90d3ea1acd0ab22d7edd2c88.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/azure_ai_studio_embeddings",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "azure_ai_studio_embeddings",
+  inference_config: {
     service: "azureaistudio",
     service_settings: {
       api_key: "<api_key>",

docs/doc_examples/24a037008e0fc2550ecb6a5d36c04a93.asciidoc

@@ -10,7 +10,7 @@ const response = await client.search({
     "date.day_of_week": {
       type: "keyword",
       script:
-        "emit(doc['date'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ROOT))",
+        "emit(doc['date'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ENGLISH))",
     },
   },
   aggs: {

docs/doc_examples/2968ffb8135f77ba3a9b876dd4918119.asciidoc

@@ -11,7 +11,7 @@ const response = await client.reindex({
   },
   dest: {
     index: "azure-ai-studio-embeddings",
-    pipeline: "azure_ai_studio_embeddings",
+    pipeline: "azure_ai_studio_embeddings_pipeline",
   },
 });
 console.log(response);

docs/doc_examples/2a1eece9a59ac1773edcf0a932c26de0.asciidoc

@@ -3,6 +3,12 @@
 
 [source, js]
 ----
-const response = await client.security.oidcLogout({});
+const response = await client.security.oidcLogout({
+  body: {
+    token:
+      "dGhpcyBpcyBub3QgYSByZWFsIHRva2VuIGJ1dCBpdCBpcyBvbmx5IHRlc3QgZGF0YS4gZG8gbm90IHRyeSB0byByZWFkIHRva2VuIQ==",
+    refresh_token: "vLBPvmAB6KvwvJZr27cS",
+  },
+});
 console.log(response);
 ----

docs/doc_examples/2acf75803494fef29f9ca70671aa6be1.asciidoc

@@ -0,0 +1,10 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.security.bulkDeleteRole({
+  names: ["my_admin_role", "superuser"],
+});
+console.log(response);
+----

docs/doc_examples/2afdf0d83724953aa2875b5fb37d60cc.asciidoc

@@ -6,6 +6,7 @@
 const response = await client.esql.asyncQueryGet({
   id: "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
   wait_for_completion_timeout: "30s",
+  body: null,
 });
 console.log(response);
 ----

docs/doc_examples/2e09666d3ad5ad9afc22763ee6e97a2b.asciidoc

@@ -0,0 +1,16 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.slm.putLifecycle({
+  policy_id: "hourly-snapshots",
+  schedule: "1h",
+  name: "<hourly-snap-{now/d}>",
+  repository: "my_repository",
+  config: {
+    indices: ["data-*", "important"],
+  },
+});
+console.log(response);
+----

docs/doc_examples/30fa37c9575fe81a0ea7c12cfc08e277.asciidoc

@@ -0,0 +1,25 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "bad_example_index",
+  mappings: {
+    properties: {
+      field_1: {
+        type: "text",
+        copy_to: "field_2",
+      },
+      field_2: {
+        type: "text",
+        copy_to: "field_3",
+      },
+      field_3: {
+        type: "text",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/310bdfb0d0d75bac7bff036a3fe51d4d.asciidoc

@@ -4,7 +4,7 @@
 [source, js]
 ----
 const response = await client.ingest.putPipeline({
-  id: "azure_ai_studio_embeddings",
+  id: "azure_ai_studio_embeddings_pipeline",
   processors: [
     {
       inference: {

docs/doc_examples/342ddf9121aeddd82fea2464665e25da.asciidoc

@@ -3,14 +3,11 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector",
-  body: {
-    index_name: "search-google-drive",
-    name: "My Connector",
-    service_type: "google_drive",
-  },
+const response = await client.connector.put({
+  connector_id: "my-connector",
+  index_name: "search-google-drive",
+  name: "My Connector",
+  service_type: "google_drive",
 });
 console.log(response);
 ----

docs/doc_examples/36ac0ef9ea63efc431580f7ade8ad53c.asciidoc

@@ -11,7 +11,7 @@ const response = await client.reindex({
   },
   dest: {
     index: "openai-embeddings",
-    pipeline: "openai_embeddings",
+    pipeline: "openai_embeddings_pipeline",
   },
 });
 console.log(response);

docs/doc_examples/398389933901b572a06a752bc780af7c.asciidoc

@@ -0,0 +1,21 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.inference.put({
+  task_type: "completion",
+  inference_id: "anthropic_completion",
+  inference_config: {
+    service: "anthropic",
+    service_settings: {
+      api_key: "<api_key>",
+      model_id: "<model_id>",
+    },
+    task_settings: {
+      max_tokens: 1024,
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/3a489743e49902df38e3368cae00717a.asciidoc

@@ -3,6 +3,6 @@
 
 [source, js]
 ----
-const response = await client.simulate.ingest({});
+const response = await client.nodes.hotThreads();
 console.log(response);
 ----

docs/doc_examples/3a4953663a5a3809b692c27446e16b7f.asciidoc

@@ -11,7 +11,7 @@ const response = await client.reindex({
   },
   dest: {
     index: "amazon-bedrock-embeddings",
-    pipeline: "amazon_bedrock_embeddings",
+    pipeline: "amazon_bedrock_embeddings_pipeline",
   },
 });
 console.log(response);

docs/doc_examples/3b6718257421b5419bf4cd6a7303c57e.asciidoc

@@ -0,0 +1,11 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.transport.request({
+  method: "GET",
+  path: "/_ingest/geoip/database/my-database-id",
+});
+console.log(response);
+----

docs/doc_examples/3c0d0c38e1c819a35a68cdba5ae8ccc4.asciidoc

@@ -0,0 +1,20 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "alibabacloud_ai_search_embeddings",
+  inference_config: {
+    service: "alibabacloud-ai-search",
+    service_settings: {
+      api_key: "<api_key>",
+      service_id: "<service_id>",
+      host: "<host>",
+      workspace: "<workspace>",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/3f1fe5f5f99b98d0891f38003e10b636.asciidoc

@@ -3,6 +3,12 @@
 
 [source, js]
 ----
-const response = await client.esql.asyncQuery({});
+const response = await client.esql.asyncQuery({
+  body: {
+    query:
+      "\n    FROM library\n    | EVAL year = DATE_TRUNC(1 YEARS, release_date)\n    | STATS MAX(page_count) BY year\n    | SORT year\n    | LIMIT 5\n  ",
+    wait_for_completion_timeout: "2s",
+  },
+});
 console.log(response);
 ----

docs/doc_examples/405511f7c1f12cc0a227b4563fe7b2e2.asciidoc

@@ -5,6 +5,7 @@
 ----
 const response = await client.esql.asyncQueryGet({
   id: "FkpMRkJGS1gzVDRlM3g4ZzMyRGlLbkEaTXlJZHdNT09TU2VTZVBoNDM3cFZMUToxMDM=",
+  body: null,
 });
 console.log(response);
 ----

docs/doc_examples/41175d304e660da2931764f9a4418fd3.asciidoc

@@ -3,16 +3,13 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector/_pipeline",
-  body: {
-    pipeline: {
-      extract_binary_content: true,
-      name: "my-connector-pipeline",
-      reduce_whitespace: true,
-      run_ml_inference: true,
-    },
+const response = await client.connector.updatePipeline({
+  connector_id: "my-connector",
+  pipeline: {
+    extract_binary_content: true,
+    name: "my-connector-pipeline",
+    reduce_whitespace: true,
+    run_ml_inference: true,
   },
 });
 console.log(response);

docs/doc_examples/430705509f8367aef92be413f702520b.asciidoc

@@ -3,12 +3,9 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector/_status",
-  body: {
-    status: "needs_configuration",
-  },
+const response = await client.connector.updateStatus({
+  connector_id: "my-connector",
+  status: "needs_configuration",
 });
 console.log(response);
 ----

docs/doc_examples/43d9e314431336a6f084cea76dfd6489.asciidoc

@@ -0,0 +1,18 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "restaurants",
+  retriever: {
+    knn: {
+      field: "vector",
+      query_vector: [10, 22, 77],
+      k: 10,
+      num_candidates: 10,
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/44385b61342e20ea05f254015b2b04d7.asciidoc

@@ -0,0 +1,10 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.security.bulkDeleteRole({
+  names: ["my_admin_role", "my_user_role"],
+});
+console.log(response);
+----

docs/doc_examples/4982c547be1ad9455ae836990aea92c5.asciidoc

@@ -0,0 +1,11 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.ml.startTrainedModelDeployment({
+  model_id: "my_model",
+  deployment_id: "my_model_for_search",
+});
+console.log(response);
+----

docs/doc_examples/4a72c68b96f44e80463084dfc0449d51.asciidoc

@@ -10,7 +10,7 @@ const response = await client.search({
       type: "keyword",
       script: {
         source:
-          "emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ROOT))",
+          "emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ENGLISH))",
       },
     },
   },

docs/doc_examples/4c9350ed09b28f00e297ebe73c3b95a2.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/my-msmarco-minilm-model",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "my-msmarco-minilm-model",
+  inference_config: {
     service: "elasticsearch",
     service_settings: {
       num_allocations: 1,

docs/doc_examples/4dc151eebefd484a28aed1a175743364.asciidoc

@@ -4,7 +4,7 @@
 [source, js]
 ----
 const response = await client.ingest.putPipeline({
-  id: "openai_embeddings",
+  id: "openai_embeddings_pipeline",
   processors: [
     {
       inference: {

docs/doc_examples/4e3414fc712b16311f9e433dd366f49d.asciidoc

@@ -3,9 +3,9 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "DELETE",
-  path: "/_inference/sparse_embedding/my-elser-model",
+const response = await client.inference.delete({
+  task_type: "sparse_embedding",
+  inference_id: "my-elser-model",
 });
 console.log(response);
 ----

docs/doc_examples/4eeded40f30949e359714a5bb6c88612.asciidoc

@@ -0,0 +1,18 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.reindex({
+  wait_for_completion: "false",
+  source: {
+    index: "test-data",
+    size: 50,
+  },
+  dest: {
+    index: "elser-embeddings",
+    pipeline: "elser_embeddings_pipeline",
+  },
+});
+console.log(response);
+----

docs/doc_examples/4f6694ef147a73b1163bde3c13779d26.asciidoc

@@ -0,0 +1,11 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.nodes.stats({
+  human: "true",
+  filter_path: "nodes.*.indexing_pressure",
+});
+console.log(response);
+----

docs/doc_examples/52f4c5eb08d39f98e2e2f5527ece9731.asciidoc

@@ -0,0 +1,20 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.inference.put({
+  task_type: "sparse_embedding",
+  inference_id: "alibabacloud_ai_search_sparse",
+  inference_config: {
+    service: "alibabacloud-ai-search",
+    service_settings: {
+      api_key: "<api_key>",
+      service_id: "ops-text-sparse-embedding-001",
+      host: "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
+      workspace: "default",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/533087d787b48878a0bf3fa8d0851b64.asciidoc

@@ -0,0 +1,11 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.transport.request({
+  method: "DELETE",
+  path: "/_ingest/geoip/database/example-database-id",
+});
+console.log(response);
+----

docs/doc_examples/548a9b6f447bb820380c1c23e57c18c3.asciidoc

@@ -4,7 +4,7 @@
 [source, js]
 ----
 const response = await client.ingest.putPipeline({
-  id: "cohere_embeddings",
+  id: "cohere_embeddings_pipeline",
   processors: [
     {
       inference: {

docs/doc_examples/54c12d5099d7b715c15f5bbf65b386a1.asciidoc

@@ -0,0 +1,22 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "alibabacloud-ai-search-embeddings",
+  mappings: {
+    properties: {
+      content_embedding: {
+        type: "dense_vector",
+        dims: 1024,
+        element_type: "float",
+      },
+      content: {
+        type: "text",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/551467688d8c701315d0a371850a4056.asciidoc

@@ -11,7 +11,7 @@ const response = await client.reindex({
   },
   dest: {
     index: "hugging-face-embeddings",
-    pipeline: "hugging_face_embeddings",
+    pipeline: "hugging_face_embeddings_pipeline",
   },
 });
 console.log(response);

docs/doc_examples/565386eee0951865a684e41fab53b40c.asciidoc

@@ -0,0 +1,22 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.inference.put({
+  task_type: "sparse_embedding",
+  inference_id: "my-elser-model",
+  inference_config: {
+    service: "elser",
+    service_settings: {
+      adaptive_allocations: {
+        enabled: true,
+        min_number_of_allocations: 3,
+        max_number_of_allocations: 10,
+      },
+      num_threads: 1,
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/57dc15e5ad663c342fd5c1d86fcd1b29.asciidoc

@@ -3,6 +3,12 @@
 
 [source, js]
 ----
-const response = await client.security.oidcPrepareAuthentication({});
+const response = await client.security.oidcPrepareAuthentication({
+  body: {
+    realm: "oidc1",
+    state: "lGYK0EcSLjqH6pkT5EVZjC6eIW5YCGgywj2sxROO",
+    nonce: "zOBXLJGUooRrbLbQk5YCcyC8AXw3iloynvluYhZ5",
+  },
+});
 console.log(response);
 ----

docs/doc_examples/58f6b72009512851843c7b7a20e9504a.asciidoc

@@ -0,0 +1,19 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "my-index-000002",
+  mappings: {
+    properties: {
+      inference_field: {
+        type: "semantic_text",
+        inference_id: "my-elser-endpoint-for-ingest",
+        search_inference_id: "my-elser-endpoint-for-search",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/59d736a4d064ed2013c7ead8e32e0998.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/completion/openai-completion",
-  body: {
+const response = await client.inference.put({
+  task_type: "completion",
+  inference_id: "openai-completion",
+  inference_config: {
     service: "openai",
     service_settings: {
       api_key: "<api_key>",

docs/doc_examples/5d3ee81bcf6ad57f39052c9065963cc3.asciidoc

@@ -0,0 +1,28 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "test_index",
+  mappings: {
+    dynamic: "strict",
+    properties: {
+      description: {
+        properties: {
+          notes: {
+            type: "text",
+            copy_to: ["description.notes_raw"],
+            analyzer: "standard",
+            search_analyzer: "standard",
+          },
+          notes_raw: {
+            type: "keyword",
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/5deeed427f35cbaee4b8ddc45002a9d7.asciidoc

@@ -0,0 +1,10 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.security.bulkDeleteRole({
+  names: ["my_admin_role", "not_an_existing_role"],
+});
+console.log(response);
+----

docs/doc_examples/5f79c42b0f74fdf71359cef82843fad3.asciidoc

@@ -1,47 +0,0 @@
-// This file is autogenerated, DO NOT EDIT
-// Use `node scripts/generate-docs-examples.js` to generate the docs examples
-
-[source, js]
-----
-const response = await client.search({
-  query: {
-    intervals: {
-      my_text: {
-        all_of: {
-          intervals: [
-            {
-              match: {
-                query: "the",
-              },
-            },
-            {
-              any_of: {
-                intervals: [
-                  {
-                    match: {
-                      query: "big",
-                    },
-                  },
-                  {
-                    match: {
-                      query: "big bad",
-                    },
-                  },
-                ],
-              },
-            },
-            {
-              match: {
-                query: "wolf",
-              },
-            },
-          ],
-          max_gaps: 0,
-          ordered: true,
-        },
-      },
-    },
-  },
-});
-console.log(response);
-----

docs/doc_examples/5fd002a018c589eb73fadad25889dbe9.asciidoc

@@ -29,7 +29,7 @@ const response = await client.transport.request({
       },
       {
         rule_id: "rule2",
-        type: "pinned",
+        type: "exclude",
         criteria: [
           {
             type: "contains",

docs/doc_examples/60d3f9a99cc91b43aaa7524a9a74dba0.asciidoc

@@ -3,8 +3,8 @@
 
 [source, js]
 ----
-const response = await client.indices.delete({
-  index: "my-index",
+const response = await client.nodes.stats({
+  metric: "breaker",
 });
 console.log(response);
 ----

docs/doc_examples/63bf3480627a89b4b4ede4150e1d6bc0.asciidoc

@@ -0,0 +1,59 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.security.bulkPutRole({
+  roles: {
+    my_admin_role: {
+      cluster: ["all"],
+      indices: [
+        {
+          names: ["index1", "index2"],
+          privileges: ["all"],
+          field_security: {
+            grant: ["title", "body"],
+          },
+          query: '{"match": {"title": "foo"}}',
+        },
+      ],
+      applications: [
+        {
+          application: "myapp",
+          privileges: ["admin", "read"],
+          resources: ["*"],
+        },
+      ],
+      run_as: ["other_user"],
+      metadata: {
+        version: 1,
+      },
+    },
+    my_user_role: {
+      cluster: ["all"],
+      indices: [
+        {
+          names: ["index1"],
+          privileges: ["read"],
+          field_security: {
+            grant: ["title", "body"],
+          },
+          query: '{"match": {"title": "foo"}}',
+        },
+      ],
+      applications: [
+        {
+          application: "myapp",
+          privileges: ["admin", "read"],
+          resources: ["*"],
+        },
+      ],
+      run_as: ["other_user"],
+      metadata: {
+        version: 1,
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/640621cea39cdeeb76fbc95bff31a18d.asciidoc

@@ -3,21 +3,18 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector/_last_sync",
-  body: {
-    last_access_control_sync_error: "Houston, we have a problem!",
-    last_access_control_sync_scheduled_at: "2023-11-09T15:13:08.231Z",
-    last_access_control_sync_status: "pending",
-    last_deleted_document_count: 42,
-    last_incremental_sync_scheduled_at: "2023-11-09T15:13:08.231Z",
-    last_indexed_document_count: 42,
-    last_sync_error: "Houston, we have a problem!",
-    last_sync_scheduled_at: "2024-11-09T15:13:08.231Z",
-    last_sync_status: "completed",
-    last_synced: "2024-11-09T15:13:08.231Z",
-  },
+const response = await client.connector.lastSync({
+  connector_id: "my-connector",
+  last_access_control_sync_error: "Houston, we have a problem!",
+  last_access_control_sync_scheduled_at: "2023-11-09T15:13:08.231Z",
+  last_access_control_sync_status: "pending",
+  last_deleted_document_count: 42,
+  last_incremental_sync_scheduled_at: "2023-11-09T15:13:08.231Z",
+  last_indexed_document_count: 42,
+  last_sync_error: "Houston, we have a problem!",
+  last_sync_scheduled_at: "2024-11-09T15:13:08.231Z",
+  last_sync_status: "completed",
+  last_synced: "2024-11-09T15:13:08.231Z",
 });
 console.log(response);
 ----

docs/doc_examples/6606d46685d10377b996b5f20f1229b5.asciidoc

@@ -3,12 +3,9 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector/_index_name",
-  body: {
-    index_name: "data-from-my-google-drive",
-  },
+const response = await client.connector.updateIndexName({
+  connector_id: "my-connector",
+  index_name: "data-from-my-google-drive",
 });
 console.log(response);
 ----

docs/doc_examples/69541f0bb81ab3797926bb2a00607cda.asciidoc

@@ -0,0 +1,19 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.inference.put({
+  task_type: "rerank",
+  inference_id: "my-msmarco-minilm-model",
+  inference_config: {
+    service: "elasticsearch",
+    service_settings: {
+      num_allocations: 1,
+      num_threads: 1,
+      model_id: "cross-encoder__ms-marco-minilm-l-6-v2",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/6b6e275efe3d2aafe0fc3443f2c96868.asciidoc

@@ -0,0 +1,18 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.reindex({
+  wait_for_completion: "false",
+  source: {
+    index: "test-data",
+    size: 50,
+  },
+  dest: {
+    index: "google-vertex-ai-embeddings",
+    pipeline: "google_vertex_ai_embeddings_pipeline",
+  },
+});
+console.log(response);
+----

docs/doc_examples/6ce6cac9df216c52371c2e77e6e07ba1.asciidoc

@@ -29,7 +29,7 @@ const response = await client.transport.request({
       },
       {
         rule_id: "my-rule2",
-        type: "pinned",
+        type: "exclude",
         criteria: [
           {
             type: "fuzzy",

docs/doc_examples/6ddd4e657efbf45def430a6419825796.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/completion/azure_ai_studio_completion",
-  body: {
+const response = await client.inference.put({
+  task_type: "completion",
+  inference_id: "azure_ai_studio_completion",
+  inference_config: {
     service: "azureaistudio",
     service_settings: {
       api_key: "<api_key>",

docs/doc_examples/6e6b78e6b689a5d6aa637271b6d084e2.asciidoc

@@ -0,0 +1,45 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "movies",
+  retriever: {
+    rrf: {
+      retrievers: [
+        {
+          standard: {
+            query: {
+              sparse_vector: {
+                field: "plot_embedding",
+                inference_id: "my-elser-model",
+                query: "films that explore psychological depths",
+              },
+            },
+          },
+        },
+        {
+          standard: {
+            query: {
+              multi_match: {
+                query: "crime",
+                fields: ["plot", "title"],
+              },
+            },
+          },
+        },
+        {
+          knn: {
+            field: "vector",
+            query_vector: [10, 22, 77],
+            k: 10,
+            num_candidates: 10,
+          },
+        },
+      ],
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/6f3b723bf6179b96c3413597ed7f49e1.asciidoc

@@ -3,6 +3,10 @@
 
 [source, js]
 ----
-const response = await client.security.bulkUpdateApiKeys({});
+const response = await client.security.bulkUpdateApiKeys({
+  body: {
+    ids: ["VuaCfGcBCdbkQm-e5aOx", "H3_AhoIBA9hmeQJdg7ij"],
+  },
+});
 console.log(response);
 ----

docs/doc_examples/7429b16221fe741fd31b0584786dd0b0.asciidoc

@@ -3,15 +3,13 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "POST",
-  path: "/_inference/text_embedding/my-cohere-endpoint",
-  body: {
-    input:
-      "The sky above the port was the color of television tuned to a dead channel.",
-    task_settings: {
-      input_type: "ingest",
-    },
+const response = await client.inference.inference({
+  task_type: "text_embedding",
+  inference_id: "my-cohere-endpoint",
+  input:
+    "The sky above the port was the color of television tuned to a dead channel.",
+  task_settings: {
+    input_type: "ingest",
   },
 });
 console.log(response);

docs/doc_examples/745864ef2427188241a4702b94ea57be.asciidoc

@@ -11,7 +11,7 @@ const response = await client.search({
       filter: {
         range: {
           price: {
-            to: "500",
+            lte: "500",
           },
         },
       },

docs/doc_examples/7594a9a85c8511701e281974cbc253e1.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/amazon_bedrock_embeddings",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "amazon_bedrock_embeddings",
+  inference_config: {
     service: "amazonbedrock",
     service_settings: {
       access_key: "<aws_access_key>",

docs/doc_examples/77518e8c6198acfe77c0934fd2fe65cb.asciidoc

@@ -3,6 +3,33 @@
 
 [source, js]
 ----
-const response = await client.textStructure.findMessageStructure({});
+const response = await client.textStructure.findMessageStructure({
+  body: {
+    messages: [
+      "[2024-03-05T10:52:36,256][INFO ][o.a.l.u.VectorUtilPanamaProvider] [laptop] Java vector incubator API enabled; uses preferredBitSize=128",
+      "[2024-03-05T10:52:41,038][INFO ][o.e.p.PluginsService     ] [laptop] loaded module [repository-url]",
+      "[2024-03-05T10:52:41,042][INFO ][o.e.p.PluginsService     ] [laptop] loaded module [rest-root]",
+      "[2024-03-05T10:52:41,043][INFO ][o.e.p.PluginsService     ] [laptop] loaded module [x-pack-core]",
+      "[2024-03-05T10:52:41,043][INFO ][o.e.p.PluginsService     ] [laptop] loaded module [x-pack-redact]",
+      "[2024-03-05T10:52:41,043][INFO ][o.e.p.PluginsService     ] [laptop] loaded module [ingest-user-agent]",
+      "[2024-03-05T10:52:41,044][INFO ][o.e.p.PluginsService     ] [laptop] loaded module [x-pack-monitoring]",
+      "[2024-03-05T10:52:41,044][INFO ][o.e.p.PluginsService     ] [laptop] loaded module [repository-s3]",
+      "[2024-03-05T10:52:41,044][INFO ][o.e.p.PluginsService     ] [laptop] loaded module [x-pack-analytics]",
+      "[2024-03-05T10:52:41,044][INFO ][o.e.p.PluginsService     ] [laptop] loaded module [x-pack-ent-search]",
+      "[2024-03-05T10:52:41,044][INFO ][o.e.p.PluginsService     ] [laptop] loaded module [x-pack-autoscaling]",
+      "[2024-03-05T10:52:41,044][INFO ][o.e.p.PluginsService     ] [laptop] loaded module [lang-painless]]",
+      "[2024-03-05T10:52:41,059][INFO ][o.e.p.PluginsService     ] [laptop] loaded module [lang-expression]",
+      "[2024-03-05T10:52:41,059][INFO ][o.e.p.PluginsService     ] [laptop] loaded module [x-pack-eql]",
+      "[2024-03-05T10:52:43,291][INFO ][o.e.e.NodeEnvironment    ] [laptop] heap size [16gb], compressed ordinary object pointers [true]",
+      "[2024-03-05T10:52:46,098][INFO ][o.e.x.s.Security         ] [laptop] Security is enabled",
+      "[2024-03-05T10:52:47,227][INFO ][o.e.x.p.ProfilingPlugin  ] [laptop] Profiling is enabled",
+      "[2024-03-05T10:52:47,259][INFO ][o.e.x.p.ProfilingPlugin  ] [laptop] profiling index templates will not be installed or reinstalled",
+      "[2024-03-05T10:52:47,755][INFO ][o.e.i.r.RecoverySettings ] [laptop] using rate limit [40mb] with [default=40mb, read=0b, write=0b, max=0b]",
+      "[2024-03-05T10:52:47,787][INFO ][o.e.d.DiscoveryModule    ] [laptop] using discovery type [multi-node] and seed hosts providers [settings]",
+      "[2024-03-05T10:52:49,188][INFO ][o.e.n.Node               ] [laptop] initialized",
+      "[2024-03-05T10:52:49,199][INFO ][o.e.n.Node               ] [laptop] starting ...",
+    ],
+  },
+});
 console.log(response);
 ----

docs/doc_examples/7752b677825523bfb0c38ad9325a6d47.asciidoc

@@ -3,12 +3,9 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "DELETE",
-  path: "/_connector/another-connector",
-  querystring: {
-    delete_sync_jobs: "true",
-  },
+const response = await client.connector.delete({
+  connector_id: "another-connector",
+  delete_sync_jobs: "true",
 });
 console.log(response);
 ----

docs/doc_examples/77b90f6787195767b6da60d8532714b4.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/azure_openai_embeddings",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "azure_openai_embeddings",
+  inference_config: {
     service: "azureopenai",
     service_settings: {
       api_key: "<api_key>",

docs/doc_examples/7888c509774a2abfe82ca370c43d8789.asciidoc

@@ -11,7 +11,7 @@ const response = await client.reindex({
   },
   dest: {
     index: "cohere-embeddings",
-    pipeline: "cohere_embeddings",
+    pipeline: "cohere_embeddings_pipeline",
   },
 });
 console.log(response);

docs/doc_examples/7b1b947bddd7e78f77da265f7e645a61.asciidoc

@@ -0,0 +1,51 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "my-index-000004",
+  mappings: {
+    properties: {
+      metrics: {
+        subobjects: "auto",
+        properties: {
+          time: {
+            type: "object",
+            properties: {
+              min: {
+                type: "long",
+              },
+            },
+          },
+          to: {
+            type: "object",
+            properties: {
+              inner_metrics: {
+                type: "object",
+                subobjects: "auto",
+                properties: {
+                  time: {
+                    type: "object",
+                    properties: {
+                      max: {
+                        type: "long",
+                      },
+                    },
+                  },
+                },
+              },
+            },
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+
+const response1 = await client.indices.getMapping({
+  index: "my-index-000004",
+});
+console.log(response1);
+----

docs/doc_examples/7f2d511cb64743c006225e5933a14bb4.asciidoc

@@ -19,6 +19,12 @@ const response = await client.security.putRole({
       clusters: ["my_remote_cluster"],
     },
   ],
+  remote_cluster: [
+    {
+      privileges: ["monitor_enrich"],
+      clusters: ["my_remote_cluster"],
+    },
+  ],
 });
 console.log(response);
 ----

docs/doc_examples/80dd7f5882c59b9c1c90e8351937441f.asciidoc

@@ -3,6 +3,28 @@
 
 [source, js]
 ----
-const response = await client.security.bulkUpdateApiKeys({});
+const response = await client.security.bulkUpdateApiKeys({
+  body: {
+    ids: ["VuaCfGcBCdbkQm-e5aOx", "H3_AhoIBA9hmeQJdg7ij"],
+    role_descriptors: {
+      "role-a": {
+        indices: [
+          {
+            names: ["*"],
+            privileges: ["write"],
+          },
+        ],
+      },
+    },
+    metadata: {
+      environment: {
+        level: 2,
+        trusted: true,
+        tags: ["production"],
+      },
+    },
+    expiration: "30d",
+  },
+});
 console.log(response);
 ----

docs/doc_examples/81aad155ff23b1b396833b1182c9d46b.asciidoc

@@ -0,0 +1,16 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.cat.shards({
+  v: "true",
+});
+console.log(response);
+
+const response1 = await client.cat.recovery({
+  v: "true",
+  active_only: "true",
+});
+console.log(response1);
+----

docs/doc_examples/82844ef45e11c0eece100d3109db3182.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/completion/amazon_bedrock_completion",
-  body: {
+const response = await client.inference.put({
+  task_type: "completion",
+  inference_id: "amazon_bedrock_completion",
+  inference_config: {
     service: "amazonbedrock",
     service_settings: {
       access_key: "<aws_access_key>",

docs/doc_examples/8417d8d35ec5fc5665dfb2f95d6d1101.asciidoc

@@ -17,7 +17,7 @@ const response = await client.search({
         {
           range: {
             "result.execution_time": {
-              from: "now-10s",
+              gte: "now-10s",
             },
           },
         },

docs/doc_examples/84237aa9da49ab4b4c4e2b21d2548df2.asciidoc

@@ -0,0 +1,11 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.snapshot.create({
+  repository: "my_repository",
+  snapshot: "_verify_integrity",
+});
+console.log(response);
+----

docs/doc_examples/8477e77e4fad19af66f03f81b8f2592b.asciidoc

@@ -0,0 +1,20 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "my-index",
+  query: {
+    semantic: {
+      field: "inference_field",
+      query: "mountain lake",
+      inner_hits: {
+        from: 1,
+        size: 1,
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/8566f5ecf4ae14802ba63c8cc7c629f8.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/mistral_embeddings",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "mistral_embeddings",
+  inference_config: {
     service: "mistral",
     service_settings: {
       api_key: "<api_key>",

docs/doc_examples/8619bd17bbfe33490b1f277007f654db.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/rerank/cohere-rerank",
-  body: {
+const response = await client.inference.put({
+  task_type: "rerank",
+  inference_id: "cohere-rerank",
+  inference_config: {
     service: "cohere",
     service_settings: {
       api_key: "<API-KEY>",

docs/doc_examples/894fce12d8f0d01e4c4083885a0c0077.asciidoc

@@ -11,7 +11,7 @@ const response = await client.reindex({
   },
   dest: {
     index: "mistral-embeddings",
-    pipeline: "mistral_embeddings",
+    pipeline: "mistral_embeddings_pipeline",
   },
 });
 console.log(response);

docs/doc_examples/8b144b3eb20872595fd7cbc6c245c7c8.asciidoc

@@ -3,8 +3,8 @@
 
 [source, js]
 ----
-const response = await client.nodes.hotThreads({
-  node_id: "my-node,my-other-node",
+const response = await client.security.queryRole({
+  sort: ["name"],
 });
 console.log(response);
 ----