Auto-generated code for 8.16 (#2549)

docs/doc_examples/0722b302b2b3275a988d858044f99d5d.asciidoc

@@ -3,11 +3,8 @@
 
 [source, js]
 ----
-const response = await client.indices.putSettings({
-  index: "my-index-000001",
-  settings: {
-    "index.blocks.read_only_allow_delete": null,
-  },
+const response = await client.indices.getMapping({
+  index: "kibana_sample_data_ecommerce",
 });
 console.log(response);
 ----

docs/doc_examples/074e4602d1ca54412380a40867d078bc.asciidoc

@@ -11,6 +11,8 @@ const response = await client.indices.putSettings({
     "index.indexing.slowlog.threshold.index.debug": "2s",
     "index.indexing.slowlog.threshold.index.trace": "500ms",
     "index.indexing.slowlog.source": "1000",
+    "index.indexing.slowlog.reformat": true,
+    "index.indexing.slowlog.include.user": true,
   },
 });
 console.log(response);

docs/doc_examples/082e78c7a2061a7c4a52b494e5ede0e8.asciidoc

@@ -0,0 +1,42 @@
+// 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-rank-vectors-bit",
+  mappings: {
+    properties: {
+      my_vector: {
+        type: "rank_vectors",
+        element_type: "bit",
+      },
+    },
+  },
+});
+console.log(response);
+
+const response1 = await client.bulk({
+  index: "my-rank-vectors-bit",
+  refresh: "true",
+  operations: [
+    {
+      index: {
+        _id: "1",
+      },
+    },
+    {
+      my_vector: [127, -127, 0, 1, 42],
+    },
+    {
+      index: {
+        _id: "2",
+      },
+    },
+    {
+      my_vector: "8100012a7f",
+    },
+  ],
+});
+console.log(response1);
+----

docs/doc_examples/0e83f140237d75469a428ff403564bb5.asciidoc

@@ -1,15 +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.cluster.putSettings({
-  persistent: {
-    "cluster.routing.allocation.disk.watermark.low": "100gb",
-    "cluster.routing.allocation.disk.watermark.high": "50gb",
-    "cluster.routing.allocation.disk.watermark.flood_stage": "10gb",
-    "cluster.info.update.interval": "1m",
-  },
-});
-console.log(response);
-----

docs/doc_examples/1420a22aa817c7a996baaed0ad366d6f.asciidoc

@@ -1,22 +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({
-  index: "test-index",
-  query: {
-    nested: {
-      path: "inference_field.inference.chunks",
-      query: {
-        sparse_vector: {
-          field: "inference_field.inference.chunks.embeddings",
-          inference_id: "my-inference-id",
-          query: "mountain lake",
-        },
-      },
-    },
-  },
-});
-console.log(response);
-----

docs/doc_examples/246763219ec06172f7aa57bba28d344a.asciidoc

@@ -0,0 +1,67 @@
+// 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-rank-vectors-bit",
+  mappings: {
+    properties: {
+      my_vector: {
+        type: "rank_vectors",
+        element_type: "bit",
+      },
+    },
+  },
+});
+console.log(response);
+
+const response1 = await client.bulk({
+  index: "my-rank-vectors-bit",
+  refresh: "true",
+  operations: [
+    {
+      index: {
+        _id: "1",
+      },
+    },
+    {
+      my_vector: [127, -127, 0, 1, 42],
+    },
+    {
+      index: {
+        _id: "2",
+      },
+    },
+    {
+      my_vector: "8100012a7f",
+    },
+  ],
+});
+console.log(response1);
+
+const response2 = await client.search({
+  index: "my-rank-vectors-bit",
+  query: {
+    script_score: {
+      query: {
+        match_all: {},
+      },
+      script: {
+        source: "maxSimDotProduct(params.query_vector, 'my_vector')",
+        params: {
+          query_vector: [
+            [
+              0.35, 0.77, 0.95, 0.15, 0.11, 0.08, 0.58, 0.06, 0.44, 0.52, 0.21,
+              0.62, 0.65, 0.16, 0.64, 0.39, 0.93, 0.06, 0.93, 0.31, 0.92, 0,
+              0.66, 0.86, 0.92, 0.03, 0.81, 0.31, 0.2, 0.92, 0.95, 0.64, 0.19,
+              0.26, 0.77, 0.64, 0.78, 0.32, 0.97, 0.84,
+            ],
+          ],
+        },
+      },
+    },
+  },
+});
+console.log(response2);
+----

docs/doc_examples/2a21674c40f9b182a8944769d20b2357.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: "my-rank-vectors-float",
+  query: {
+    script_score: {
+      query: {
+        match_all: {},
+      },
+      script: {
+        source: "maxSimDotProduct(params.query_vector, 'my_vector')",
+        params: {
+          query_vector: [
+            [0.5, 10, 6],
+            [-0.5, 10, 10],
+          ],
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/3312c82f81816bf76629db9582991812.asciidoc

@@ -14,6 +14,7 @@ const response = await client.indices.putSettings({
     "index.search.slowlog.threshold.fetch.info": "800ms",
     "index.search.slowlog.threshold.fetch.debug": "500ms",
     "index.search.slowlog.threshold.fetch.trace": "200ms",
+    "index.search.slowlog.include.user": true,
   },
 });
 console.log(response);

docs/doc_examples/37f367ca81a16d3aef4ef7126ec33a2e.asciidoc

@@ -0,0 +1,67 @@
+// 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",
+  size: 10,
+  retriever: {
+    rescorer: {
+      rescore: {
+        query: {
+          window_size: 50,
+          rescore_query: {
+            script_score: {
+              script: {
+                source:
+                  "cosineSimilarity(params.queryVector, 'product-vector_final_stage') + 1.0",
+                params: {
+                  queryVector: [-0.5, 90, -10, 14.8, -156],
+                },
+              },
+            },
+          },
+        },
+      },
+      retriever: {
+        rrf: {
+          rank_window_size: 100,
+          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/3ea4c971b3f47735dcc207ee2645fa03.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.indices.updateAliases({
+  actions: [
+    {
+      remove_index: {
+        index: "my-index-2099.05.06-000001",
+      },
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/3f9dcf2aa42f3ecfb5ebfe48c1774103.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: "kibana_sample_data_ecommerce",
+  size: 0,
+  aggs: {
+    order_stats: {
+      stats: {
+        field: "taxful_total_price",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/6b67c6121efb86ee100d40c2646f77b5.asciidoc

@@ -4,9 +4,11 @@
 [source, js]
 ----
 const response = await client.indices.putSettings({
-  index: "my-index-000001",
+  index: "*",
   settings: {
     "index.search.slowlog.include.user": true,
+    "index.search.slowlog.threshold.fetch.warn": "30s",
+    "index.search.slowlog.threshold.query.warn": "30s",
   },
 });
 console.log(response);

docs/doc_examples/6fa02c2ad485bbe91f44b321158250f3.asciidoc

@@ -12,6 +12,13 @@ const response = await client.search({
       fields: ["my_field", "my_field._2gram", "my_field._3gram"],
     },
   },
+  highlight: {
+    fields: {
+      my_field: {
+        matched_fields: ["my_field._index_prefix"],
+      },
+    },
+  },
 });
 console.log(response);
 ----

docs/doc_examples/730045fae3743c39b612813a42c330c3.asciidoc

@@ -0,0 +1,24 @@
+// 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-000001",
+  query: {
+    prefix: {
+      full_name: {
+        value: "ki",
+      },
+    },
+  },
+  highlight: {
+    fields: {
+      full_name: {
+        matched_fields: ["full_name._index_prefix"],
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/7478ff69113fb53f41ea07cdf911fa67.asciidoc

@@ -0,0 +1,33 @@
+// 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: "kibana_sample_data_ecommerce",
+  size: 0,
+  aggs: {
+    daily_sales: {
+      date_histogram: {
+        field: "order_date",
+        calendar_interval: "day",
+      },
+      aggs: {
+        daily_revenue: {
+          sum: {
+            field: "taxful_total_price",
+          },
+        },
+        smoothed_revenue: {
+          moving_fn: {
+            buckets_path: "daily_revenue",
+            window: 3,
+            script: "MovingFunctions.unweightedAvg(values)",
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/74b229a6e020113e5749099451979c89.asciidoc

@@ -1,26 +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({
-  index: "test-index",
-  query: {
-    nested: {
-      path: "inference_field.inference.chunks",
-      query: {
-        knn: {
-          field: "inference_field.inference.chunks.embeddings",
-          query_vector_builder: {
-            text_embedding: {
-              model_id: "my_inference_id",
-              model_text: "mountain lake",
-            },
-          },
-        },
-      },
-    },
-  },
-});
-console.log(response);
-----

docs/doc_examples/7dd0d9cc6c5982a2c003d301e90feeba.asciidoc

@@ -0,0 +1,37 @@
+// 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: "kibana_sample_data_ecommerce",
+  size: 0,
+  aggs: {
+    daily_sales: {
+      date_histogram: {
+        field: "order_date",
+        calendar_interval: "day",
+        format: "yyyy-MM-dd",
+      },
+      aggs: {
+        revenue: {
+          sum: {
+            field: "taxful_total_price",
+          },
+        },
+        unique_customers: {
+          cardinality: {
+            field: "customer_id",
+          },
+        },
+        avg_basket_size: {
+          avg: {
+            field: "total_quantity",
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/8c639d3eef5c2de29e12bd9c6a42d3d4.asciidoc

@@ -0,0 +1,39 @@
+// 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: "kibana_sample_data_ecommerce",
+  size: 0,
+  aggs: {
+    categories: {
+      terms: {
+        field: "category.keyword",
+        size: 5,
+        order: {
+          total_revenue: "desc",
+        },
+      },
+      aggs: {
+        total_revenue: {
+          sum: {
+            field: "taxful_total_price",
+          },
+        },
+        avg_order_value: {
+          avg: {
+            field: "taxful_total_price",
+          },
+        },
+        total_items: {
+          sum: {
+            field: "total_quantity",
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/9cc952d4a03264b700136cbc45abc8c6.asciidoc

@@ -0,0 +1,30 @@
+// 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-rank-vectors-byte",
+  mappings: {
+    properties: {
+      my_vector: {
+        type: "rank_vectors",
+        element_type: "byte",
+      },
+    },
+  },
+});
+console.log(response);
+
+const response1 = await client.index({
+  index: "my-rank-vectors-byte",
+  id: 1,
+  document: {
+    my_vector: [
+      [1, 2, 3],
+      [4, 5, 6],
+    ],
+  },
+});
+console.log(response1);
+----

docs/doc_examples/b590241c4296299b836fbb5a95bdd2dc.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: "kibana_sample_data_ecommerce",
+  size: 0,
+  aggs: {
+    avg_order_value: {
+      avg: {
+        field: "taxful_total_price",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/b6d278737d27973e498ac61cda9e5126.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.search({
+  index: "kibana_sample_data_ecommerce",
+  size: 0,
+  aggs: {
+    daily_orders: {
+      date_histogram: {
+        field: "order_date",
+        calendar_interval: "day",
+        format: "yyyy-MM-dd",
+        min_doc_count: 0,
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/bccd4eb26b1a325d103b12e198a13c08.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.indices.getSettings({
+  index: "_all",
+  expand_wildcards: "all",
+  filter_path: "*.settings.index.*.slowlog",
+});
+console.log(response);
+----

docs/doc_examples/bdc55256fa5f701680631a149dbb75a9.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: "kibana_sample_data_ecommerce",
+  size: 0,
+  aggs: {
+    sales_by_category: {
+      terms: {
+        field: "category.keyword",
+        size: 5,
+        order: {
+          _count: "desc",
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/bdd28276618235487ac96bd6679bc206.asciidoc

@@ -0,0 +1,31 @@
+// 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: "kibana_sample_data_ecommerce",
+  size: 0,
+  aggs: {
+    daily_sales: {
+      date_histogram: {
+        field: "order_date",
+        calendar_interval: "day",
+      },
+      aggs: {
+        revenue: {
+          sum: {
+            field: "taxful_total_price",
+          },
+        },
+        cumulative_revenue: {
+          cumulative_sum: {
+            buckets_path: "revenue",
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/df81b88a2192dd6f9912e0c948a44487.asciidoc

@@ -7,7 +7,7 @@ const response = await client.inference.put({
   task_type: "sparse_embedding",
   inference_id: "elser_embeddings",
   inference_config: {
-    service: "elser",
+    service: "elasticsearch",
     service_settings: {
       num_allocations: 1,
       num_threads: 1,

docs/doc_examples/e375c7da666276c4df6664c6821cd5f4.asciidoc

@@ -0,0 +1,29 @@
+// 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-rank-vectors-float",
+  mappings: {
+    properties: {
+      my_vector: {
+        type: "rank_vectors",
+      },
+    },
+  },
+});
+console.log(response);
+
+const response1 = await client.index({
+  index: "my-rank-vectors-float",
+  id: 1,
+  document: {
+    my_vector: [
+      [0.5, 10, 6],
+      [-0.5, 10, 10],
+    ],
+  },
+});
+console.log(response1);
+----

docs/doc_examples/ec4b43c3ebd8816799fa004596b2f0cb.asciidoc

@@ -4,9 +4,10 @@
 [source, js]
 ----
 const response = await client.indices.putSettings({
-  index: "my-index-000001",
+  index: "*",
   settings: {
     "index.indexing.slowlog.include.user": true,
+    "index.indexing.slowlog.threshold.index.warn": "30s",
   },
 });
 console.log(response);