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Auto-generated code for 8.17 (#2528)
1 parent dd0a304 commit b3e523a

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‎docs/doc_examples/160986f49758f4e8345d183a842f6351.asciidoc renamed to ‎docs/doc_examples/0c52af573c9401a2a687e86a4beb182b.asciidoc

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

‎docs/doc_examples/2a67608dadbf220a2f040f3a79d3677d.asciidoc

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// This file is autogenerated, DO NOT EDIT
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// Use `node scripts/generate-docs-examples.js` to generate the docs examples
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[source, js]
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----
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const response = await client.ingest.putPipeline({
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id: "attachment",
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description: "Extract attachment information including original binary",
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processors: [
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{
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attachment: {
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field: "data",
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remove_binary: false,
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},
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},
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],
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});
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console.log(response);
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const response1 = await client.index({
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index: "my-index-000001",
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id: "my_id",
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pipeline: "attachment",
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document: {
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data: "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=",
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},
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});
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console.log(response1);
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const response2 = await client.get({
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index: "my-index-000001",
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id: "my_id",
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});
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console.log(response2);
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----

‎docs/doc_examples/9f16fca9813304e398ee052aa857dbcd.asciidoc renamed to ‎docs/doc_examples/48e142e6c69014e0509d4c9251749d77.asciidoc

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

‎docs/doc_examples/49b31e23f8b9667b6a7b2734d55fb6ed.asciidoc

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‎docs/doc_examples/53d9d2ec9cb8d211772d764e76fe6890.asciidoc

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// This file is autogenerated, DO NOT EDIT
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// Use `node scripts/generate-docs-examples.js` to generate the docs examples
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[source, js]
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----
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const response = await client.ingest.simulate({
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id: "query_helper_pipeline",
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docs: [
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{
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_source: {
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content:
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"artificial intelligence in medicine articles published in the last 12 months",
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},
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},
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],
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});
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console.log(response);
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----

‎docs/doc_examples/5a70db31f587b7ffed5e9bc1445430cb.asciidoc

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‎docs/doc_examples/f4d0ef2e0f76babee83d999fe35127f2.asciidoc renamed to ‎docs/doc_examples/80135e8c644e34cc70ce8a4e7915d1a2.asciidoc

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

‎docs/doc_examples/8cad5d95a0e7c103f08be53d0b172558.asciidoc

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‎docs/doc_examples/931817b168e055ecf738785c721125dd.asciidoc

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// This file is autogenerated, DO NOT EDIT
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// Use `node scripts/generate-docs-examples.js` to generate the docs examples
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[source, js]
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----
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const response = await client.ingest.putPipeline({
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id: "query_helper_pipeline",
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processors: [
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{
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script: {
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source:
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"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",
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},
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},
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{
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inference: {
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model_id: "openai_chat_completions",
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input_output: {
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input_field: "prompt",
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output_field: "query",
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},
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},
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},
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{
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remove: {
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field: "prompt",
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},
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},
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],
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});
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console.log(response);
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----

‎docs/doc_examples/8593715fcc70315a0816b435551258e0.asciidoc renamed to ‎docs/doc_examples/93c77c65f1e11382f8043d0300e87b89.asciidoc

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

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