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Auto-generated code for 8.17 (#2503)
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+1661
-820
lines changed

‎docs/doc_examples/015e6e6132b6d6d44bddb06bc3b316ed.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.search({
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index: "retrievers_example",
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retriever: {
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rrf: {
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retrievers: [
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{
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standard: {
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query: {
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range: {
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year: {
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gt: 2023,
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},
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},
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},
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},
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},
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{
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standard: {
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query: {
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term: {
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topic: "elastic",
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},
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},
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},
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},
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],
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rank_window_size: 10,
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rank_constant: 1,
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},
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},
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_source: false,
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aggs: {
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topics: {
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terms: {
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field: "topic",
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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/0165d22da5f2fc7678392b31d8eb5566.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.inference.put({
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task_type: "rerank",
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inference_id: "my-rerank-model",
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inference_config: {
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service: "cohere",
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service_settings: {
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model_id: "rerank-english-v3.0",
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api_key: "{{COHERE_API_KEY}}",
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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/0bc6155e0c88062a4d8490da49db3aa8.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.search({
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index: "retrievers_example_nested",
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retriever: {
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rrf: {
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retrievers: [
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{
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standard: {
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query: {
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nested: {
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path: "nested_field",
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inner_hits: {
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name: "nested_vector",
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_source: false,
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fields: ["nested_field.paragraph_id"],
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},
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query: {
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knn: {
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field: "nested_field.nested_vector",
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query_vector: [1, 0, 0.5],
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k: 10,
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},
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},
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},
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},
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},
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},
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{
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standard: {
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query: {
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term: {
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topic: "ai",
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},
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},
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},
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},
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],
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rank_window_size: 10,
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rank_constant: 1,
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},
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},
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_source: ["topic"],
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});
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console.log(response);
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----

‎docs/doc_examples/0d689ac6e78be5d438f9b5d441be2b44.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.search({
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index: "retrievers_example",
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retriever: {
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rrf: {
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retrievers: [
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{
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standard: {
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query: {
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term: {
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topic: "elastic",
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},
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},
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},
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},
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{
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rrf: {
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retrievers: [
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{
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standard: {
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query: {
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query_string: {
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query:
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"(information retrieval) OR (artificial intelligence)",
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default_field: "text",
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},
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},
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},
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},
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{
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knn: {
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field: "vector",
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query_vector: [0.23, 0.67, 0.89],
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k: 3,
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num_candidates: 5,
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},
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},
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],
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rank_window_size: 10,
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rank_constant: 1,
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},
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},
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],
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rank_window_size: 10,
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rank_constant: 1,
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},
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},
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_source: false,
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size: 1,
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explain: true,
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});
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console.log(response);
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----

‎docs/doc_examples/c02c2916b97b6fa7db82dbc7f0378310.asciidoc renamed to ‎docs/doc_examples/17b1647c8509543f2388c886f2584a20.asciidoc

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},
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},
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field: "text",
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inference_id: "my-cohere-rerank-model",
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inference_id: "elastic-rerank",
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inference_text: "How often does the moon hide the sun?",
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rank_window_size: 100,
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min_score: 0.5,

‎docs/doc_examples/4dab4c5168047ba596af1beb0e55b845.asciidoc renamed to ‎docs/doc_examples/22b176a184517cf1b5801f5eb4f17f97.asciidoc

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[source, js]
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----
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const response = await client.cluster.getSettings({
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flat_settings: "true",
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const response = await client.indices.rollover({
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alias: "datastream",
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});
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console.log(response);
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----

‎docs/doc_examples/2577acb462b95bd4394523cf2f8a661f.asciidoc

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‎docs/doc_examples/2bb41b0b4876ce98cd0cd8fb6d337f18.asciidoc

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This file was deleted.

‎docs/doc_examples/30d051f534aeb884176eedb2c11dac85.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.inference.put({
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task_type: "rerank",
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inference_id: "my-elastic-rerank",
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inference_config: {
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service: "elasticsearch",
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service_settings: {
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model_id: ".rerank-v1",
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num_threads: 1,
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adaptive_allocations: {
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enabled: true,
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min_number_of_allocations: 1,
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max_number_of_allocations: 4,
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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/191074b2eebd5f74e628c2ada4b6d2e4.asciidoc renamed to ‎docs/doc_examples/4edfb5934d14ad7655bd7e19a112b5c0.asciidoc

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@@ -8,11 +8,6 @@ const response = await client.search({
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query: {
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bool: {
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must: [
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{
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term: {
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"category.keyword": "Main Course",
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},
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},
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{
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term: {
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tags: "vegetarian",
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},
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],
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should: [
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{
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term: {
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category: "Main Course",
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},
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},
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{
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multi_match: {
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query: "curry spicy",

‎docs/doc_examples/19f1f9f25933f8e7aba59a10881c648b.asciidoc renamed to ‎docs/doc_examples/5f16358ebb5d14b86f57612d5f92d923.asciidoc

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properties: {
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inference_field: {
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type: "semantic_text",
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inference_id: "my-elser-endpoint",
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},
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},
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},

‎docs/doc_examples/2fd458d37aab509fe2d970c0b6e2a10f.asciidoc renamed to ‎docs/doc_examples/68d7f7d4d268ee98caead5aef19933d6.asciidoc

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const response1 = await client.indices.putIndexTemplate({
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name: 2,
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index_patterns: ["k8s*"],
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index_patterns: ["k9s*"],
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composed_of: ["destination_template"],
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data_stream: {},
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});

‎docs/doc_examples/76e02434835630cb830724beb92df354.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.search({
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index: "retrievers_example",
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retriever: {
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rrf: {
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retrievers: [
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{
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knn: {
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field: "vector",
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query_vector: [0.23, 0.67, 0.89],
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k: 3,
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num_candidates: 5,
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},
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},
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{
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text_similarity_reranker: {
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retriever: {
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standard: {
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query: {
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term: {
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topic: "ai",
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},
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},
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},
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},
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field: "text",
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inference_id: "my-rerank-model",
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inference_text:
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"Can I use generative AI to identify user intent and improve search relevance?",
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},
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},
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],
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rank_window_size: 10,
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rank_constant: 1,
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},
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},
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_source: false,
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});
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console.log(response);
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----

‎docs/doc_examples/78043831fd32004a82930c8ac8a1d809.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]
5+
----
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const response = await client.search({
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index: "retrievers_example",
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retriever: {
9+
text_similarity_reranker: {
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retriever: {
11+
rrf: {
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retrievers: [
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{
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standard: {
15+
query: {
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query_string: {
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query:
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"(information retrieval) OR (artificial intelligence)",
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default_field: "text",
20+
},
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},
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},
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},
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{
25+
knn: {
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field: "vector",
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query_vector: [0.23, 0.67, 0.89],
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k: 3,
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num_candidates: 5,
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},
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},
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],
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rank_window_size: 10,
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rank_constant: 1,
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},
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},
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field: "text",
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inference_id: "my-rerank-model",
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inference_text:
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"What are the state of the art applications of AI in information retrieval?",
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},
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},
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_source: false,
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});
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console.log(response);
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----

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