Auto-generated code for main (#2551)

docs/doc_examples/e20037f66bf54bcac7d10f536f031f34.asciidoc → 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/844928da2ff9a1394af5347a5e2e4f78.asciidoc → 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/160986f49758f4e8345d183a842f6351.asciidoc → docs/doc_examples/0c52af573c9401a2a687e86a4beb182b.asciidoc

@@ -10,7 +10,7 @@ const response = await client.ingest.putPipeline({
     {
       attachment: {
         field: "data",
-        remove_binary: false,
+        remove_binary: true,
       },
     },
   ],

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/2a67608dadbf220a2f040f3a79d3677d.asciidoc

@@ -0,0 +1,35 @@
+// 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: "attachment",
+  description: "Extract attachment information including original binary",
+  processors: [
+    {
+      attachment: {
+        field: "data",
+        remove_binary: false,
+      },
+    },
+  ],
+});
+console.log(response);
+
+const response1 = await client.index({
+  index: "my-index-000001",
+  id: "my_id",
+  pipeline: "attachment",
+  document: {
+    data: "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=",
+  },
+});
+console.log(response1);
+
+const response2 = await client.get({
+  index: "my-index-000001",
+  id: "my_id",
+});
+console.log(response2);
+----

docs/doc_examples/23af230e824f48b9cd56a4cf973d788c.asciidoc → 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/9f16fca9813304e398ee052aa857dbcd.asciidoc → docs/doc_examples/48e142e6c69014e0509d4c9251749d77.asciidoc

@@ -10,7 +10,8 @@ const response = await client.inference.put({
     service: "openai",
     service_settings: {
       api_key: "<api_key>",
-      model_id: "text-embedding-ada-002",
+      model_id: "text-embedding-3-small",
+      dimensions: 128,
     },
   },
 });