Auto-generated API code

Author: elasticmachine

docs/doc_examples/53d9d2ec9cb8d211772d764e76fe6890.asciidoc

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+// 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/931817b168e055ecf738785c721125dd.asciidoc

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+// 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/d29031409016b2b798148ef173a196ae.asciidoc

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+// 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);
+----