Auto-generated code for main (#2357)

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/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/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/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/19d60e4890cc57151d596326484d9076.asciidoc

@@ -3,9 +3,9 @@
 
 [source, js]
 ----
-const response = await client.ingest.deleteGeoipDatabase({
-  id: "my-database-id",
-  body: null,
+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/1b60ad542abb511cbd926ac8c55b609c.asciidoc

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

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/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/398389933901b572a06a752bc780af7c.asciidoc

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

docs/doc_examples/3b6718257421b5419bf4cd6a7303c57e.asciidoc

@@ -3,9 +3,9 @@
 
 [source, js]
 ----
-const response = await client.ingest.getGeoipDatabase({
-  id: "my-database-id",
-  body: null,
+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/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/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/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/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

@@ -3,9 +3,9 @@
 
 [source, js]
 ----
-const response = await client.ingest.deleteGeoipDatabase({
-  id: "example-database-id",
-  body: null,
+const response = await client.transport.request({
+  method: "DELETE",
+  path: "/_ingest/geoip/database/example-database-id",
 });
 console.log(response);
 ----

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/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/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/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/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/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/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/7c63a1d2fbec5283e913ff39fafd0604.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",
+  processors: [
+    {
+      inference: {
+        model_id: "google_vertex_ai_embeddings",
+        input_output: {
+          input_field: "content",
+          output_field: "content_embedding",
+        },
+      },
+    },
+  ],
+});
+console.log(response);
+----

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/82eff1d681a5d0d1538ef011bb32ab9a.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: "alibabacloud-ai-search-embeddings",
+    pipeline: "alibabacloud_ai_search_embeddings",
+  },
+});
+console.log(response);
+----

docs/doc_examples/840f8c863c30b04abcf2dd66b846f157.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: {
       adaptive_allocations: {

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/8e89fee0be6a436c4e3d7c152659c47e.asciidoc

@@ -3,23 +3,20 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector/_scheduling",
-  body: {
-    scheduling: {
-      access_control: {
-        enabled: true,
-        interval: "0 10 0 * * ?",
-      },
-      full: {
-        enabled: true,
-        interval: "0 20 0 * * ?",
-      },
-      incremental: {
-        enabled: false,
-        interval: "0 30 0 * * ?",
-      },
+const response = await client.connector.updateScheduling({
+  connector_id: "my-connector",
+  scheduling: {
+    access_control: {
+      enabled: true,
+      interval: "0 10 0 * * ?",
+    },
+    full: {
+      enabled: true,
+      interval: "0 20 0 * * ?",
+    },
+    incremental: {
+      enabled: false,
+      interval: "0 30 0 * * ?",
     },
   },
 });

docs/doc_examples/9326e323f7ffde678fa04d2d1de3d3bc.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: "alibabacloud-ai-search-embeddings",
+  knn: {
+    field: "content_embedding",
+    query_vector_builder: {
+      text_embedding: {
+        model_id: "alibabacloud_ai_search_embeddings",
+        model_text: "Calculate fuel cost",
+      },
+    },
+    k: 10,
+    num_candidates: 100,
+  },
+  _source: ["id", "content"],
+});
+console.log(response);
+----

docs/doc_examples/981b331db1404b39c1a612a135e4e76d.asciidoc

@@ -3,8 +3,9 @@
 
 [source, js]
 ----
-const response = await client.ingest.putGeoipDatabase({
-  id: "my-database-id",
+const response = await client.transport.request({
+  method: "PUT",
+  path: "/_ingest/geoip/database/my-database-id",
   body: {
     name: "GeoIP2-Domain",
     maxmind: {

docs/doc_examples/9868ce609f4450702934fcbf4c340bf1.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",
+  processors: [
+    {
+      inference: {
+        model_id: "alibabacloud_ai_search_embeddings",
+        input_output: {
+          input_field: "content",
+          output_field: "content_embedding",
+        },
+      },
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/986f892bfa4dfdf1da8455fdf84a4b0c.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: "ops-text-embedding-001",
+      host: "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
+      workspace: "default",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/99803d7b111b862c0c82e9908e549b16.asciidoc

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

docs/doc_examples/9a203aae3e1412d919546276fb52a5ca.asciidoc

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

docs/doc_examples/9c021836acf7c0370e289f611325868d.asciidoc

@@ -3,17 +3,14 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-spo-connector/_configuration",
-  body: {
-    values: {
-      tenant_id: "my-tenant-id",
-      tenant_name: "my-sharepoint-site",
-      client_id: "foo",
-      secret_value: "bar",
-      site_collections: "*",
-    },
+const response = await client.connector.updateConfiguration({
+  connector_id: "my-spo-connector",
+  values: {
+    tenant_id: "my-tenant-id",
+    tenant_name: "my-sharepoint-site",
+    client_id: "foo",
+    secret_value: "bar",
+    site_collections: "*",
   },
 });
 console.log(response);

docs/doc_examples/9d396afad93782699d7a929578c85284.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: "google_vertex_ai_embeddings",
+  inference_config: {
+    service: "googlevertexai",
+    service_settings: {
+      service_account_json: "<service_account_json>",
+      model_id: "text-embedding-004",
+      location: "<location>",
+      project_id: "<project_id>",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/9f16fca9813304e398ee052aa857dbcd.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/a225fc8c134cb21a85bc6025dac9368b.asciidoc

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

docs/doc_examples/a4a3c3cd09efa75168dab90105afb2e9.asciidoc

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

docs/doc_examples/a4ee2214d621bcfaf768c46d21325958.asciidoc

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

docs/doc_examples/a594f05459d9eecc8050c73fc8da336f.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/a69c7c3412af73758f629e76263063b5.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",
+  },
+});
+console.log(response);
+----

docs/doc_examples/a960b43e720b4934edb74ab4b085ca77.asciidoc

@@ -3,9 +3,6 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "GET",
-  path: "/_connector",
-});
+const response = await client.connector.list();
 console.log(response);
 ----

docs/doc_examples/aa676d54a59dee87ecd28bcc1edce59b.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: "rerank",
+  inference_id: "alibabacloud_ai_search_rerank",
+  inference_config: {
+    service: "alibabacloud-ai-search",
+    service_settings: {
+      api_key: "<api_key>",
+      service_id: "ops-bge-reranker-larger",
+      host: "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
+      workspace: "default",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/b16700002af3aa70639f3e88c733bf35.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.openPointInTime({
+  index: "my-index-000001",
+  keep_alive: "1m",
+  allow_partial_search_results: "true",
+});
+console.log(response);
+----

docs/doc_examples/b45a8c6fc746e9c90fd181e69a605fad.asciidoc

@@ -3,12 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "POST",
-  path: "/_inference/completion/openai_chat_completions",
-  body: {
-    input: "What is Elastic?",
-  },
+const response = await client.inference.inference({
+  task_type: "completion",
+  inference_id: "openai_chat_completions",
+  input: "What is Elastic?",
 });
 console.log(response);
 ----

docs/doc_examples/bdb671866e2f0195f8dfbdb7f20bf591.asciidoc

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

docs/doc_examples/c00c9412609832ebceb9e786dd9542df.asciidoc

@@ -3,13 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector/_name",
-  body: {
-    name: "Custom connector",
-    description: "This is my customized connector",
-  },
+const response = await client.connector.updateName({
+  connector_id: "my-connector",
+  name: "Custom connector",
+  description: "This is my customized connector",
 });
 console.log(response);
 ----

docs/doc_examples/c0ddfb2e6315f5bcf0d3ef414b5bbed3.asciidoc

@@ -3,13 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-spo-connector/_configuration",
-  body: {
-    values: {
-      secret_value: "foo-bar",
-    },
+const response = await client.connector.updateConfiguration({
+  connector_id: "my-spo-connector",
+  values: {
+    secret_value: "foo-bar",
   },
 });
 console.log(response);

docs/doc_examples/c18100d62ed31bc9e05f62900156e6a8.asciidoc

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

docs/doc_examples/c21eb4bc30087188241cbba6b6b89999.asciidoc

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

docs/doc_examples/ce13afc0c976c5e1f424b58e0c97fd64.asciidoc

@@ -3,16 +3,13 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector",
-  body: {
-    index_name: "search-google-drive",
-    name: "My Connector",
-    description: "My Connector to sync data to Elastic index from Google Drive",
-    service_type: "google_drive",
-    language: "english",
-  },
+const response = await client.connector.put({
+  connector_id: "my-connector",
+  index_name: "search-google-drive",
+  name: "My Connector",
+  description: "My Connector to sync data to Elastic index from Google Drive",
+  service_type: "google_drive",
+  language: "english",
 });
 console.log(response);
 ----

docs/doc_examples/cedb56a71cc743d80263ce352bb21720.asciidoc

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

docs/doc_examples/d03139a851888db53f8b7affd85eb495.asciidoc

@@ -3,9 +3,8 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector/_check_in",
+const response = await client.connector.checkIn({
+  connector_id: "my-connector",
 });
 console.log(response);
 ----

docs/doc_examples/d6a21afa4a94b9baa734eac430940bcf.asciidoc

@@ -3,13 +3,9 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "GET",
-  path: "/_connector",
-  querystring: {
-    from: "0",
-    size: "2",
-  },
+const response = await client.connector.list({
+  from: 0,
+  size: 2,
 });
 console.log(response);
 ----

docs/doc_examples/d7b61bfb6adb22986a43388b823894cc.asciidoc

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

docs/doc_examples/e9fc47015922d51c2b05e502ce9c622e.asciidoc

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

docs/doc_examples/eb54506fbc71a7d250e86b22d0600114.asciidoc

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

docs/doc_examples/eb96d7dd5f3116a50f7a86b729f1a934.asciidoc

@@ -3,15 +3,12 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector/_scheduling",
-  body: {
-    scheduling: {
-      full: {
-        enabled: true,
-        interval: "0 10 0 * * ?",
-      },
+const response = await client.connector.updateScheduling({
+  connector_id: "my-connector",
+  scheduling: {
+    full: {
+      enabled: true,
+      interval: "0 10 0 * * ?",
     },
   },
 });

docs/doc_examples/ecfd0d94dd14ef05dfa861f22544b388.asciidoc

@@ -3,12 +3,9 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector/_error",
-  body: {
-    error: "Houston, we have a problem!",
-  },
+const response = await client.connector.updateError({
+  connector_id: "my-connector",
+  error: "Houston, we have a problem!",
 });
 console.log(response);
 ----

docs/doc_examples/eee6110831c08b9c1b3f56b24656e95b.asciidoc

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

docs/doc_examples/f1b24217b1d9ba6ea5e4fa6e6f412022.asciidoc

@@ -3,13 +3,11 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "POST",
-  path: "/_inference/rerank/cohere_rerank",
-  body: {
-    input: ["luke", "like", "leia", "chewy", "r2d2", "star", "wars"],
-    query: "star wars main character",
-  },
+const response = await client.inference.inference({
+  task_type: "rerank",
+  inference_id: "cohere_rerank",
+  input: ["luke", "like", "leia", "chewy", "r2d2", "star", "wars"],
+  query: "star wars main character",
 });
 console.log(response);
 ----

docs/doc_examples/f2a5f77f929cc7b893b80f4bd5b1a192.asciidoc

@@ -3,9 +3,8 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "GET",
-  path: "/_connector/my-connector",
+const response = await client.connector.get({
+  connector_id: "my-connector",
 });
 console.log(response);
 ----

docs/doc_examples/f57ce7de0946e9416ddb9150e95f4b74.asciidoc

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

docs/doc_examples/fd620f09dbce62c6f0f603a366623607.asciidoc

@@ -3,19 +3,16 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-sql-connector/_filtering",
-  body: {
-    advanced_snippet: {
-      value: [
-        {
-          tables: ["users", "orders"],
-          query:
-            "SELECT users.id AS id, orders.order_id AS order_id FROM users JOIN orders ON users.id = orders.user_id",
-        },
-      ],
-    },
+const response = await client.connector.updateFiltering({
+  connector_id: "my-sql-connector",
+  advanced_snippet: {
+    value: [
+      {
+        tables: ["users", "orders"],
+        query:
+          "SELECT users.id AS id, orders.order_id AS order_id FROM users JOIN orders ON users.id = orders.user_id",
+      },
+    ],
   },
 });
 console.log(response);

docs/reference.asciidoc

@@ -5667,12 +5667,20 @@ client.inference.put({ inference_id })
 === ingest
 [discrete]
 ==== delete_geoip_database
-Deletes a geoip database configuration
+Deletes a geoip database configuration.
 [source,ts]
 ----
-client.ingest.deleteGeoipDatabase()
+client.ingest.deleteGeoipDatabase({ id })
 ----
 
+[discrete]
+==== Arguments
+
+* *Request (object):*
+** *`id` (string | string[])*: A list of geoip database configurations to delete
+** *`master_timeout` (Optional, string | -1 | 0)*: Period to wait for a connection to the master node.
+If no response is received before the timeout expires, the request fails and returns an error.
+** *`timeout` (Optional, string | -1 | 0)*: Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
 
 [discrete]
 ==== delete_pipeline
@@ -5708,12 +5716,21 @@ client.ingest.geoIpStats()
 
 [discrete]
 ==== get_geoip_database
-Returns geoip database configuration.
+Returns information about one or more geoip database configurations.
 [source,ts]
 ----
-client.ingest.getGeoipDatabase()
+client.ingest.getGeoipDatabase({ ... })
 ----
 
+[discrete]
+==== Arguments
+
+* *Request (object):*
+** *`id` (Optional, string | string[])*: List of database configuration IDs to retrieve.
+Wildcard (`*`) expressions are supported.
+To get all database configurations, omit this parameter or use `*`.
+** *`master_timeout` (Optional, string | -1 | 0)*: Period to wait for a connection to the master node.
+If no response is received before the timeout expires, the request fails and returns an error.
 
 [discrete]
 ==== get_pipeline
@@ -5752,12 +5769,23 @@ client.ingest.processorGrok()
 
 [discrete]
 ==== put_geoip_database
-Puts the configuration for a geoip database to be downloaded
+Returns information about one or more geoip database configurations.
 [source,ts]
 ----
-client.ingest.putGeoipDatabase()
+client.ingest.putGeoipDatabase({ id, name, maxmind })
 ----
 
+[discrete]
+==== Arguments
+
+* *Request (object):*
+** *`id` (string)*: ID of the database configuration to create or update.
+** *`name` (string)*: The provider-assigned name of the IP geolocation database to download.
+** *`maxmind` ({ account_id })*: The configuration necessary to identify which IP geolocation provider to use to download the database, as well as any provider-specific configuration necessary for such downloading.
+At present, the only supported provider is maxmind, and the maxmind provider requires that an account_id (string) is configured.
+** *`master_timeout` (Optional, string | -1 | 0)*: Period to wait for a connection to the master node.
+If no response is received before the timeout expires, the request fails and returns an error.
+** *`timeout` (Optional, string | -1 | 0)*: Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
 
 [discrete]
 ==== put_pipeline
@@ -5777,8 +5805,8 @@ client.ingest.putPipeline({ id })
 ** *`id` (string)*: ID of the ingest pipeline to create or update.
 ** *`_meta` (Optional, Record<string, User-defined value>)*: Optional metadata about the ingest pipeline. May have any contents. This map is not automatically generated by Elasticsearch.
 ** *`description` (Optional, string)*: Description of the ingest pipeline.
-** *`on_failure` (Optional, { append, attachment, bytes, circle, convert, csv, date, date_index_name, dissect, dot_expander, drop, enrich, fail, foreach, geoip, grok, gsub, inference, join, json, kv, lowercase, pipeline, remove, rename, reroute, script, set, set_security_user, sort, split, trim, uppercase, urldecode, user_agent }[])*: Processors to run immediately after a processor failure. Each processor supports a processor-level `on_failure` value. If a processor without an `on_failure` value fails, Elasticsearch uses this pipeline-level parameter as a fallback. The processors in this parameter run sequentially in the order specified. Elasticsearch will not attempt to run the pipeline's remaining processors.
-** *`processors` (Optional, { append, attachment, bytes, circle, convert, csv, date, date_index_name, dissect, dot_expander, drop, enrich, fail, foreach, geoip, grok, gsub, inference, join, json, kv, lowercase, pipeline, remove, rename, reroute, script, set, set_security_user, sort, split, trim, uppercase, urldecode, user_agent }[])*: Processors used to perform transformations on documents before indexing. Processors run sequentially in the order specified.
+** *`on_failure` (Optional, { append, attachment, bytes, circle, convert, csv, date, date_index_name, dissect, dot_expander, drop, enrich, fail, foreach, geoip, grok, gsub, html_strip, inference, join, json, kv, lowercase, pipeline, remove, rename, reroute, script, set, set_security_user, sort, split, trim, uppercase, urldecode, uri_parts, user_agent }[])*: Processors to run immediately after a processor failure. Each processor supports a processor-level `on_failure` value. If a processor without an `on_failure` value fails, Elasticsearch uses this pipeline-level parameter as a fallback. The processors in this parameter run sequentially in the order specified. Elasticsearch will not attempt to run the pipeline's remaining processors.
+** *`processors` (Optional, { append, attachment, bytes, circle, convert, csv, date, date_index_name, dissect, dot_expander, drop, enrich, fail, foreach, geoip, grok, gsub, html_strip, inference, join, json, kv, lowercase, pipeline, remove, rename, reroute, script, set, set_security_user, sort, split, trim, uppercase, urldecode, uri_parts, user_agent }[])*: Processors used to perform transformations on documents before indexing. Processors run sequentially in the order specified.
 ** *`version` (Optional, number)*: Version number used by external systems to track ingest pipelines. This parameter is intended for external systems only. Elasticsearch does not use or validate pipeline version numbers.
 ** *`master_timeout` (Optional, string | -1 | 0)*: Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
 ** *`timeout` (Optional, string | -1 | 0)*: Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
@@ -5791,16 +5819,16 @@ Executes an ingest pipeline against a set of provided documents.
 {ref}/simulate-pipeline-api.html[Endpoint documentation]
 [source,ts]
 ----
-client.ingest.simulate({ ... })
+client.ingest.simulate({ docs })
 ----
 
 [discrete]
 ==== Arguments
 
 * *Request (object):*
+** *`docs` ({ _id, _index, _source }[])*: Sample documents to test in the pipeline.
 ** *`id` (Optional, string)*: Pipeline to test.
 If you don’t specify a `pipeline` in the request body, this parameter is required.
-** *`docs` (Optional, { _id, _index, _source }[])*: Sample documents to test in the pipeline.
 ** *`pipeline` (Optional, { description, on_failure, processors, version, _meta })*: Pipeline to test.
 If you don’t specify the `pipeline` request path parameter, this parameter is required.
 If you specify both this and the request path parameter, the API only uses the request path parameter.