Auto-generated code for 8.15 (#2539)

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/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/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,
     },
   },
 });

docs/doc_examples/49b31e23f8b9667b6a7b2734d55fb6ed.asciidoc

@@ -1,17 +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.knnSearch({
-  index: "my-index",
-  knn: {
-    field: "image_vector",
-    query_vector: [0.3, 0.1, 1.2],
-    k: 10,
-    num_candidates: 100,
-  },
-  _source: ["name", "file_type"],
-});
-console.log(response);
-----

docs/doc_examples/53d9d2ec9cb8d211772d764e76fe6890.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.ingest.simulate({
+  id: "query_helper_pipeline",
+  docs: [
+    {
+      _source: {
+        content:
+          "artificial intelligence in medicine articles published in the last 12 months",
+      },
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/5a70db31f587b7ffed5e9bc1445430cb.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.indices.create({
-  index: "semantic-embeddings",
-  mappings: {
-    properties: {
-      semantic_text: {
-        type: "semantic_text",
-        inference_id: "my-elser-endpoint",
-      },
-      content: {
-        type: "text",
-        copy_to: "semantic_text",
-      },
-    },
-  },
-});
-console.log(response);
-----

docs/doc_examples/80135e8c644e34cc70ce8a4e7915d1a2.asciidoc

@@ -12,7 +12,7 @@ const response = await client.ingest.putPipeline({
         field: "data",
         indexed_chars: 11,
         indexed_chars_field: "max_size",
-        remove_binary: false,
+        remove_binary: true,
       },
     },
   ],

docs/doc_examples/8cad5d95a0e7c103f08be53d0b172558.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.inference.put({
-  task_type: "sparse_embedding",
-  inference_id: "my-elser-endpoint",
-  inference_config: {
-    service: "elser",
-    service_settings: {
-      adaptive_allocations: {
-        enabled: true,
-        min_number_of_allocations: 3,
-        max_number_of_allocations: 10,
-      },
-      num_threads: 1,
-    },
-  },
-});
-console.log(response);
-----

docs/doc_examples/931817b168e055ecf738785c721125dd.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.ingest.putPipeline({
+  id: "query_helper_pipeline",
+  processors: [
+    {
+      script: {
+        source:
+          "ctx.prompt = 'Please generate an elasticsearch search query on index `articles_index` for the following natural language query. Dates are in the field `@timestamp`, document types are in the field `type` (options are `news`, `publication`), categories in the field `category` and can be multiple (options are `medicine`, `pharmaceuticals`, `technology`), and document names are in the field `title` which should use a fuzzy match. Ignore fields which cannot be determined from the natural language query context: ' + ctx.content",
+      },
+    },
+    {
+      inference: {
+        model_id: "openai_chat_completions",
+        input_output: {
+          input_field: "prompt",
+          output_field: "query",
+        },
+      },
+    },
+    {
+      remove: {
+        field: "prompt",
+      },
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/93c77c65f1e11382f8043d0300e87b89.asciidoc

@@ -9,7 +9,7 @@ const response = await client.indices.create({
     properties: {
       infer_field: {
         type: "semantic_text",
-        inference_id: "my-elser-endpoint",
+        inference_id: ".elser-2-elasticsearch",
       },
       source_field: {
         type: "text",

docs/doc_examples/cecfaa659af6646b3b67d7b311586fa0.asciidoc

@@ -14,7 +14,7 @@ const response = await client.ingest.putPipeline({
           attachment: {
             target_field: "_ingest._value.attachment",
             field: "_ingest._value.data",
-            remove_binary: false,
+            remove_binary: true,
           },
         },
       },

docs/doc_examples/d29031409016b2b798148ef173a196ae.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.indices.create({
+  index: "test-index",
+  query: {
+    semantic: {
+      field: "my_semantic_field",
+    },
+  },
+  highlight: {
+    fields: {
+      my_semantic_field: {
+        type: "semantic",
+        number_of_fragments: 2,
+        order: "score",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/d5242b1ab0213f25e5e0742032274ce6.asciidoc

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

docs/doc_examples/e77c2f41a7eca765b0c5f734a66d919f.asciidoc

@@ -11,7 +11,7 @@ const response = await client.ingest.putPipeline({
       attachment: {
         field: "data",
         properties: ["content", "title"],
-        remove_binary: false,
+        remove_binary: true,
       },
     },
   ],

docs/doc_examples/ea8c4229afa6dd4f1321355542be9912.asciidoc

@@ -12,7 +12,7 @@ const response = await client.ingest.putPipeline({
         field: "data",
         indexed_chars: 11,
         indexed_chars_field: "max_size",
-        remove_binary: false,
+        remove_binary: true,
       },
     },
   ],