Auto-generated code for 8.x

docs/doc_examples/e944653610f311fa06148d5b0afdf697.asciidoc → 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/1aa96eeaf63fc967e166d1a2fcdccccc.asciidoc → 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/9868ce609f4450702934fcbf4c340bf1.asciidoc → 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/840f8c863c30b04abcf2dd66b846f157.asciidoc → 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/46025fc47dfbfa410790df0dd6bdad8d.asciidoc → 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/be30ea12f605fd61acba689b68e00bbe.asciidoc → 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/7c63a1d2fbec5283e913ff39fafd0604.asciidoc → 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/da3cecc36a7313385d32c7f52ccfb7e3.asciidoc → 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/dfcdadcf91529d3a399e05684195028e.asciidoc → 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/5f8d90515995a5eee189d722abe3b111.asciidoc → 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/65e892a362d940e4a74965f21c15ca09.asciidoc → 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/60d689aae3f8de1e6830329dfd69a6a6.asciidoc → 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/cc28a3dafcd5056f2a3ec07f6fda5091.asciidoc → 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/a53ff77d83222c0e76453e630d64787e.asciidoc → 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/c9373ff5ed6b026173428fbb92ca2d9f.asciidoc → 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/3a7a6ab88a49b484fafb10c8eb09b562.asciidoc → 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/b468d0124dc485385a34504d5b7af82a.asciidoc → 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/1b60ad542abb511cbd926ac8c55b609c.asciidoc → 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/1745ac9e6d22a2ffe7ac381f9ba238f9.asciidoc → 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/a69c7c3412af73758f629e76263063b5.asciidoc → 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/a4ec42130f3c75fc9d1d5f7cb6222cd5.asciidoc → docs/doc_examples/745864ef2427188241a4702b94ea57be.asciidoc

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

docs/doc_examples/84490ee2c6c07dbd2101ce2e3751e1aa.asciidoc → 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);
+----