Auto-generated code for 8.15 (#2411)

docs/doc_examples/0e31b8ad176b31028becf9500989bcbd.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.inference.put({
+  task_type: "text_embedding",
+  inference_id: "watsonx-embeddings",
+  inference_config: {
+    service: "watsonxai",
+    service_settings: {
+      api_key: "<api_key>",
+      url: "<url>",
+      model_id: "ibm/slate-30m-english-rtrvr",
+      project_id: "<project_id>",
+      api_version: "2024-03-14",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/3ab8f65fcb55a0e3664c55749ec41efd.asciidoc

@@ -29,6 +29,7 @@ const response = await client.indices.create({
             "arabic_normalization",
             "persian_normalization",
             "persian_stop",
+            "persian_stem",
           ],
         },
       },

docs/doc_examples/40f287bf733420bbab134b74c7d0ea5d.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.index({
+  index: "amazon-reviews",
+  id: 1,
+  document: {
+    review_text:
+      "This product is lifechanging! I'm telling all my friends about it.",
+    review_vector: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
+  },
+});
+console.log(response);
+----

docs/doc_examples/4b91ad7c9b44e07db4a4e81390f19ad3.asciidoc

@@ -0,0 +1,14 @@
+// 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.streamInference({
+  task_type: "completion",
+  inference_id: "openai-completion",
+  body: {
+    input: "What is Elastic?",
+  },
+});
+console.log(response);
+----

docs/doc_examples/5ceb734e3affe00e2cdc29af748d95bf.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.inference.put({
+  task_type: "sparse_embedding",
+  inference_id: "small_chunk_size",
+  inference_config: {
+    service: "elasticsearch",
+    service_settings: {
+      num_allocations: 1,
+      num_threads: 1,
+    },
+    chunking_settings: {
+      strategy: "sentence",
+      max_chunk_size: 100,
+      sentence_overlap: 0,
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/5f16358ebb5d14b86f57612d5f92d923.asciidoc

@@ -9,7 +9,6 @@ const response = await client.indices.create({
     properties: {
       inference_field: {
         type: "semantic_text",
-        inference_id: "my-elser-endpoint",
       },
     },
   },

docs/doc_examples/76c73b54f3f1e5cb1c0fcccd7c3fd18e.asciidoc

@@ -0,0 +1,52 @@
+// 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.bulk({
+  operations: [
+    {
+      index: {
+        _index: "amazon-reviews",
+        _id: "2",
+      },
+    },
+    {
+      review_text: "This product is amazing! I love it.",
+      review_vector: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
+    },
+    {
+      index: {
+        _index: "amazon-reviews",
+        _id: "3",
+      },
+    },
+    {
+      review_text: "This product is terrible. I hate it.",
+      review_vector: [0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1],
+    },
+    {
+      index: {
+        _index: "amazon-reviews",
+        _id: "4",
+      },
+    },
+    {
+      review_text: "This product is great. I can do anything with it.",
+      review_vector: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
+    },
+    {
+      index: {
+        _index: "amazon-reviews",
+        _id: "5",
+      },
+    },
+    {
+      review_text:
+        "This product has ruined my life and the lives of my family and friends.",
+      review_vector: [0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1],
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/7a2fdfd7b0553d63440af7598f9ad867.asciidoc

@@ -4,7 +4,7 @@
 [source, js]
 ----
 const response = await client.indices.create({
-  index: "my-index-000002",
+  index: "my-index-000003",
   mappings: {
     properties: {
       inference_field: {

docs/doc_examples/7b9691bd34a02dd859562eb927f175e0.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.inference.put({
+  task_type: "sparse_embedding",
+  inference_id: "my-elser-model",
+  inference_config: {
+    service: "elasticsearch",
+    service_settings: {
+      adaptive_allocations: {
+        enabled: true,
+        min_number_of_allocations: 1,
+        max_number_of_allocations: 10,
+      },
+      num_threads: 1,
+      model_id: ".elser_model_2",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/7db09cab02d71f3a10d91071216d80fc.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: "amazon-reviews",
+  retriever: {
+    knn: {
+      field: "review_vector",
+      query_vector: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
+      k: 2,
+      num_candidates: 5,
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/8080cd9e24a8785728ce7c372ec4acf1.asciidoc

@@ -3,9 +3,10 @@
 
 [source, js]
 ----
-const response = await client.indices.putSettings({
-  index: ".watches",
-  settings: {
+const response = await client.transport.request({
+  method: "PUT",
+  path: "/_watcher/settings",
+  body: {
     "index.routing.allocation.include.role": "watcher",
   },
 });

docs/doc_examples/85f9fc6f98e8573efed9b034e853d5ae.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.inference.put({
+  task_type: "sparse_embedding",
+  inference_id: "use_existing_deployment",
+  inference_config: {
+    service: "elasticsearch",
+    service_settings: {
+      deployment_id: ".elser_model_2",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/8a0b5f759de3f27f0801c1176e616117.asciidoc

@@ -9,7 +9,6 @@ const response = await client.indices.create({
     properties: {
       content: {
         type: "semantic_text",
-        inference_id: "my-elser-endpoint",
       },
     },
   },

docs/doc_examples/96e88611f99e6834bd64b58dc8a282c1.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.indices.create({
+  index: "my-index-000002",
+  mappings: {
+    properties: {
+      inference_field: {
+        type: "semantic_text",
+        inference_id: "my-openai-endpoint",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/b3f442a7d9eb391121dcab991787f9d6.asciidoc

@@ -5,10 +5,16 @@
 ----
 const response = await client.indices.create({
   index: "idx",
-  mappings: {
-    _source: {
-      mode: "synthetic",
+  settings: {
+    index: {
+      mapping: {
+        source: {
+          mode: "synthetic",
+        },
+      },
     },
+  },
+  mappings: {
     properties: {
       binary: {
         type: "binary",

docs/doc_examples/c8aa8e8c0ac160b8c4efd1ac3b9f48f3.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: "amazon-reviews",
+  mappings: {
+    properties: {
+      review_vector: {
+        type: "dense_vector",
+        dims: 8,
+        index: true,
+        similarity: "cosine",
+      },
+      review_text: {
+        type: "text",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/ccc613951c61f0b17e1ed8a2d3ae54a2.asciidoc

@@ -58,6 +58,14 @@ const response = await client.simulate.ingest({
         composed_of: ["component_template_1", "component_template_2"],
       },
     },
+    mapping_addition: {
+      dynamic: "strict",
+      properties: {
+        foo: {
+          type: "keyword",
+        },
+      },
+    },
   },
 });
 console.log(response);

docs/doc_examples/f95a4d7ab02bf400246c8822f0245f02.asciidoc

@@ -5,7 +5,7 @@
 ----
 const response = await client.cat.mlTrainedModels({
   h: "c,o,l,ct,v",
-  v: "ture",
+  v: "true",
 });
 console.log(response);
 ----