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Auto-generated code for main (#2551)
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66 files changed

+2459
-651
lines changed

‎docs/doc_examples/e20037f66bf54bcac7d10f536f031f34.asciidoc renamed to ‎docs/doc_examples/0722b302b2b3275a988d858044f99d5d.asciidoc

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@@ -3,11 +3,8 @@
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[source, js]
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----
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const response = await client.indices.putSettings({
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index: "my-index-000001",
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settings: {
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"index.blocks.read_only_allow_delete": null,
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},
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const response = await client.indices.getMapping({
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index: "kibana_sample_data_ecommerce",
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});
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console.log(response);
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----

‎docs/doc_examples/844928da2ff9a1394af5347a5e2e4f78.asciidoc renamed to ‎docs/doc_examples/074e4602d1ca54412380a40867d078bc.asciidoc

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@@ -11,6 +11,8 @@ const response = await client.indices.putSettings({
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"index.indexing.slowlog.threshold.index.debug": "2s",
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"index.indexing.slowlog.threshold.index.trace": "500ms",
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"index.indexing.slowlog.source": "1000",
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"index.indexing.slowlog.reformat": true,
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"index.indexing.slowlog.include.user": true,
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},
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});
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console.log(response);

‎docs/doc_examples/082e78c7a2061a7c4a52b494e5ede0e8.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.indices.create({
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index: "my-rank-vectors-bit",
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mappings: {
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properties: {
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my_vector: {
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type: "rank_vectors",
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element_type: "bit",
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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.bulk({
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index: "my-rank-vectors-bit",
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refresh: "true",
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operations: [
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{
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index: {
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_id: "1",
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},
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},
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{
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my_vector: [127, -127, 0, 1, 42],
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},
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{
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index: {
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_id: "2",
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},
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},
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{
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my_vector: "8100012a7f",
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},
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],
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});
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console.log(response1);
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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/0e83f140237d75469a428ff403564bb5.asciidoc

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‎docs/doc_examples/1420a22aa817c7a996baaed0ad366d6f.asciidoc

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‎docs/doc_examples/246763219ec06172f7aa57bba28d344a.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.indices.create({
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index: "my-rank-vectors-bit",
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mappings: {
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properties: {
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my_vector: {
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type: "rank_vectors",
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element_type: "bit",
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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.bulk({
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index: "my-rank-vectors-bit",
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refresh: "true",
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operations: [
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{
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index: {
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_id: "1",
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},
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},
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{
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my_vector: [127, -127, 0, 1, 42],
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},
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{
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index: {
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_id: "2",
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},
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},
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{
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my_vector: "8100012a7f",
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},
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],
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});
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console.log(response1);
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const response2 = await client.search({
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index: "my-rank-vectors-bit",
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query: {
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script_score: {
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query: {
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match_all: {},
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},
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script: {
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source: "maxSimDotProduct(params.query_vector, 'my_vector')",
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params: {
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query_vector: [
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[
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0.35, 0.77, 0.95, 0.15, 0.11, 0.08, 0.58, 0.06, 0.44, 0.52, 0.21,
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0.62, 0.65, 0.16, 0.64, 0.39, 0.93, 0.06, 0.93, 0.31, 0.92, 0,
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0.66, 0.86, 0.92, 0.03, 0.81, 0.31, 0.2, 0.92, 0.95, 0.64, 0.19,
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0.26, 0.77, 0.64, 0.78, 0.32, 0.97, 0.84,
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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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console.log(response2);
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----

‎docs/doc_examples/2a21674c40f9b182a8944769d20b2357.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: "my-rank-vectors-float",
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query: {
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script_score: {
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query: {
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match_all: {},
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},
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script: {
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source: "maxSimDotProduct(params.query_vector, 'my_vector')",
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params: {
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query_vector: [
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[0.5, 10, 6],
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[-0.5, 10, 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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console.log(response);
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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/23af230e824f48b9cd56a4cf973d788c.asciidoc renamed to ‎docs/doc_examples/3312c82f81816bf76629db9582991812.asciidoc

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"index.search.slowlog.threshold.fetch.info": "800ms",
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"index.search.slowlog.threshold.fetch.debug": "500ms",
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"index.search.slowlog.threshold.fetch.trace": "200ms",
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"index.search.slowlog.include.user": true,
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},
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});
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console.log(response);

‎docs/doc_examples/37f367ca81a16d3aef4ef7126ec33a2e.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: "movies",
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size: 10,
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retriever: {
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rescorer: {
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rescore: {
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query: {
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window_size: 50,
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rescore_query: {
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script_score: {
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script: {
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source:
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"cosineSimilarity(params.queryVector, 'product-vector_final_stage') + 1.0",
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params: {
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queryVector: [-0.5, 90, -10, 14.8, -156],
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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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retriever: {
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rrf: {
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rank_window_size: 100,
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retrievers: [
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{
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standard: {
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query: {
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sparse_vector: {
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field: "plot_embedding",
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inference_id: "my-elser-model",
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query: "films that explore psychological depths",
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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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multi_match: {
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query: "crime",
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fields: ["plot", "title"],
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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: [10, 22, 77],
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k: 10,
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num_candidates: 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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});
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console.log(response);
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----

‎docs/doc_examples/3ea4c971b3f47735dcc207ee2645fa03.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.indices.updateAliases({
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actions: [
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{
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remove_index: {
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index: "my-index-2099.05.06-000001",
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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/3f9dcf2aa42f3ecfb5ebfe48c1774103.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: "kibana_sample_data_ecommerce",
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size: 0,
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aggs: {
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order_stats: {
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stats: {
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field: "taxful_total_price",
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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/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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