Auto-generated code for 8.x (#2369)

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/05e637284bc3bedd46e0b7c26ad983c4.asciidoc

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

docs/doc_examples/083b92e8ea264e49bf9fd40fc6a3094b.asciidoc

@@ -14,6 +14,7 @@ const response = await client.inference.put({
         min_number_of_allocations: 3,
         max_number_of_allocations: 10,
       },
+      num_threads: 1,
       model_id: ".multilingual-e5-small",
     },
   },

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/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

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

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/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/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/3a489743e49902df38e3368cae00717a.asciidoc

@@ -0,0 +1,8 @@
+// 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.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/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/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/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/4eeded40f30949e359714a5bb6c88612.asciidoc

@@ -11,7 +11,7 @@ const response = await client.reindex({
   },
   dest: {
     index: "elser-embeddings",
-    pipeline: "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/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/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

@@ -14,6 +14,7 @@ const response = await client.inference.put({
         min_number_of_allocations: 3,
         max_number_of_allocations: 10,
       },
+      num_threads: 1,
     },
   },
 });

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/60d3f9a99cc91b43aaa7524a9a74dba0.asciidoc

@@ -3,8 +3,8 @@
 
 [source, js]
 ----
-const response = await client.nodes.hotThreads({
-  node_id: "my-node,my-other-node",
+const response = await client.nodes.stats({
+  metric: "breaker",
 });
 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

@@ -11,7 +11,7 @@ const response = await client.reindex({
   },
   dest: {
     index: "google-vertex-ai-embeddings",
-    pipeline: "google_vertex_ai_embeddings",
+    pipeline: "google_vertex_ai_embeddings_pipeline",
   },
 });
 console.log(response);

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/745864ef2427188241a4702b94ea57be.asciidoc

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

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/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/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/8cad5d95a0e7c103f08be53d0b172558.asciidoc

@@ -9,7 +9,11 @@ const response = await client.inference.put({
   inference_config: {
     service: "elser",
     service_settings: {
-      num_allocations: 1,
+      adaptive_allocations: {
+        enabled: true,
+        min_number_of_allocations: 3,
+        max_number_of_allocations: 10,
+      },
       num_threads: 1,
     },
   },

docs/doc_examples/8d750dfc067b1184c32a2423c60e4d06.asciidoc

@@ -0,0 +1,17 @@
+// 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: {},
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/9169d19a80175ec94f80865d0f9bef4c.asciidoc

@@ -0,0 +1,36 @@
+// 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: {
+    rrf: {
+      retrievers: [
+        {
+          standard: {
+            query: {
+              multi_match: {
+                query: "Austria",
+                fields: ["city", "region"],
+              },
+            },
+          },
+        },
+        {
+          knn: {
+            field: "vector",
+            query_vector: [10, 22, 77],
+            k: 10,
+            num_candidates: 10,
+          },
+        },
+      ],
+      rank_constant: 1,
+      rank_window_size: 50,
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/968fb5b92aa65af09544f7c002b0953e.asciidoc

@@ -7,7 +7,7 @@ const response = await client.search({
   index: "semantic-embeddings",
   query: {
     semantic: {
-      field: "semantic_text",
+      field: "content",
       query: "How to avoid muscle soreness while running?",
     },
   },

docs/doc_examples/971fd23adb81bb5842c7750e0379336a.asciidoc

@@ -0,0 +1,26 @@
+// 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: {
+    text_similarity_reranker: {
+      retriever: {
+        standard: {
+          query: {
+            match: {
+              genre: "drama",
+            },
+          },
+        },
+      },
+      field: "plot",
+      inference_id: "my-msmarco-minilm-model",
+      inference_text: "films that explore psychological depths",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/9d66cb59711f24e6b4ff85608c9b5a1b.asciidoc

@@ -4,7 +4,9 @@
 [source, js]
 ----
 const response = await client.tasks.list({
-  filter_path: "nodes.*.tasks",
+  pretty: "true",
+  human: "true",
+  detailed: "true",
 });
 console.log(response);
 ----

docs/doc_examples/9dfe3b02bd15409b4b8b36e9756e8f94.asciidoc

@@ -0,0 +1,17 @@
+// 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.index({
+  index: "my-index",
+  id: "lake_tahoe",
+  document: {
+    inference_field: [
+      "Lake Tahoe is the largest alpine lake in North America",
+      "When hiking in the area, please be on alert for bears",
+    ],
+  },
+});
+console.log(response);
+----

docs/doc_examples/a1dda7e7c01be96a4acf7b725d70385f.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.search({
+  index: "index",
+  retriever: {
+    text_similarity_reranker: {
+      retriever: {
+        standard: {
+          query: {
+            match_phrase: {
+              text: "landmark in Paris",
+            },
+          },
+        },
+      },
+      field: "text",
+      inference_id: "my-cohere-rerank-model",
+      inference_text: "Most famous landmark in Paris",
+      rank_window_size: 100,
+      min_score: 0.5,
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/a3646b59da66b9ab68bdbc8dc2e6a9be.asciidoc

@@ -0,0 +1,32 @@
+// 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: {
+    standard: {
+      query: {
+        bool: {
+          should: [
+            {
+              match: {
+                region: "Austria",
+              },
+            },
+          ],
+          filter: [
+            {
+              term: {
+                year: "2019",
+              },
+            },
+          ],
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/a769d696bf12f5e9de4b3250646d250c.asciidoc

@@ -11,7 +11,7 @@ const response = await client.reindex({
   },
   dest: {
     index: "alibabacloud-ai-search-embeddings",
-    pipeline: "alibabacloud_ai_search_embeddings",
+    pipeline: "alibabacloud_ai_search_embeddings_pipeline",
   },
 });
 console.log(response);

docs/doc_examples/a95ae76fca7c3e273e4bd10323b3caa6.asciidoc

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

docs/doc_examples/aa814309ad5f1630886ba75255b444f5.asciidoc

@@ -0,0 +1,8 @@
+// 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.pendingTasks();
+console.log(response);
+----

docs/doc_examples/b09f155602f9b2a6c40fe7c4a5436b7a.asciidoc

@@ -8,7 +8,7 @@ const response = await client.search({
     day_of_week: {
       type: "keyword",
       script:
-        "\n        emit(doc['timestamp'].value.dayOfWeekEnum\n          .getDisplayName(TextStyle.FULL, Locale.ROOT))\n      ",
+        "\n        emit(doc['timestamp'].value.dayOfWeekEnum\n          .getDisplayName(TextStyle.FULL, Locale.ENGLISH))\n      ",
     },
   },
   size: 0,

docs/doc_examples/c8fa8d7e029792d539464fede18ce258.asciidoc

@@ -7,14 +7,10 @@ const response = await client.indices.create({
   index: "semantic-embeddings",
   mappings: {
     properties: {
-      semantic_text: {
+      content: {
         type: "semantic_text",
         inference_id: "my-elser-endpoint",
       },
-      content: {
-        type: "text",
-        copy_to: "semantic_text",
-      },
     },
   },
 });

docs/doc_examples/d3440ec81dde5f1a01c0206cb35e539c.asciidoc

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

docs/doc_examples/dbb8fa2b8af6db66cf75ca4b83c0fb21.asciidoc

@@ -0,0 +1,68 @@
+// 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: {
+      metrics: {
+        type: "object",
+        subobjects: "auto",
+        properties: {
+          inner: {
+            type: "object",
+            enabled: false,
+          },
+          nested: {
+            type: "nested",
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+
+const response1 = await client.index({
+  index: "my-index-000002",
+  id: "metric_1",
+  document: {
+    "metrics.time": 100,
+    "metrics.time.min": 10,
+    "metrics.time.max": 900,
+  },
+});
+console.log(response1);
+
+const response2 = await client.index({
+  index: "my-index-000002",
+  id: "metric_2",
+  document: {
+    metrics: {
+      time: 100,
+      "time.min": 10,
+      "time.max": 900,
+      inner: {
+        foo: "bar",
+        "path.to.some.field": "baz",
+      },
+      nested: [
+        {
+          id: 10,
+        },
+        {
+          id: 1,
+        },
+      ],
+    },
+  },
+});
+console.log(response2);
+
+const response3 = await client.indices.getMapping({
+  index: "my-index-000002",
+});
+console.log(response3);
+----

docs/doc_examples/dbce6cb1eaf9b2cc36b7f9a13afc63ea.asciidoc

@@ -33,6 +33,25 @@ const response = await client.simulate.ingest({
         ],
       },
     },
+    component_template_substitutions: {
+      "my-component-template": {
+        template: {
+          mappings: {
+            dynamic: "true",
+            properties: {
+              field3: {
+                type: "keyword",
+              },
+            },
+          },
+          settings: {
+            index: {
+              default_pipeline: "my-pipeline",
+            },
+          },
+        },
+      },
+    },
   },
 });
 console.log(response);

docs/doc_examples/dd7814258121d3c2e576a7f00469d7e3.asciidoc

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

docs/doc_examples/e017c2de6f93a8dd97f5c6e002dd5c4f.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.ml.postCalendarEvents({
+  calendar_id: "dst-germany",
+  events: [
+    {
+      description: "Fall 2024",
+      start_time: 1729994400000,
+      end_time: 1730167200000,
+      skip_result: false,
+      skip_model_update: false,
+      force_time_shift: -3600,
+    },
+    {
+      description: "Spring 2025",
+      start_time: 1743296400000,
+      end_time: 1743469200000,
+      skip_result: false,
+      skip_model_update: false,
+      force_time_shift: 3600,
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/e04267ffc50d916800b919c6cdc9622a.asciidoc

@@ -0,0 +1,13 @@
+// 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-000001",
+  settings: {
+    "index.mapping.ignore_above": 256,
+  },
+});
+console.log(response);
+----

docs/doc_examples/e3fe842951dc873d7d00c8f6a010c53f.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/search",
+});
+console.log(response);
+----

docs/doc_examples/e4b38973c74037335378d8480f1ce894.asciidoc

@@ -0,0 +1,44 @@
+// 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.simulate.ingest({
+  body: {
+    docs: [
+      {
+        _index: "my-index",
+        _id: "123",
+        _source: {
+          foo: "foo",
+        },
+      },
+      {
+        _index: "my-index",
+        _id: "456",
+        _source: {
+          bar: "rab",
+        },
+      },
+    ],
+    component_template_substitutions: {
+      "my-mappings_template": {
+        template: {
+          mappings: {
+            dynamic: "strict",
+            properties: {
+              foo: {
+                type: "keyword",
+              },
+              bar: {
+                type: "keyword",
+              },
+            },
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/e9625da419bff6470ffd9927c59ca159.asciidoc

@@ -5,7 +5,7 @@
 ----
 const response = await client.cat.threadPool({
   v: "true",
-  h: "id,name,active,rejected,completed",
+  h: "id,name,queue,active,rejected,completed",
 });
 console.log(response);
 ----

docs/doc_examples/ef46c42d473b2acc151a6a41272e0f14.asciidoc

@@ -12,7 +12,7 @@ const response = await client.indices.create({
         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/efbd4936cca1a752493d8fa2ba6ad1a3.asciidoc

@@ -11,7 +11,7 @@ const response = await client.indices.create({
         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/f03352bb1129938a89f97e4b650038dd.asciidoc

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

docs/doc_examples/f7b20e4bb8366f6d2e4486f3bf4211bc.asciidoc

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

docs/doc_examples/f86337e13526c968848cfe29a52d658f.asciidoc

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