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elasticmachineelasticmachine
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Auto-generated code for 8.15 (#2370)
1 parent 7aca5cf commit 424cc94

28 files changed

+699
-15
lines changed

‎docs/doc_examples/1aa96eeaf63fc967e166d1a2fcdccccc.asciidoc renamed to ‎docs/doc_examples/0393ca5a2942e1f00ed87546d0d50732.asciidoc

+2-2
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@@ -4,7 +4,7 @@
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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-index-000002",
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index: "my-index-000003",
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mappings: {
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properties: {
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metrics: {
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console.log(response);
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const response1 = await client.indices.getMapping({
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index: "my-index-000002",
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index: "my-index-000003",
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});
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console.log(response1);
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----

‎docs/doc_examples/840f8c863c30b04abcf2dd66b846f157.asciidoc renamed to ‎docs/doc_examples/083b92e8ea264e49bf9fd40fc6a3094b.asciidoc

+1
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@@ -14,6 +14,7 @@ const response = await client.inference.put({
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min_number_of_allocations: 3,
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max_number_of_allocations: 10,
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},
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num_threads: 1,
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model_id: ".multilingual-e5-small",
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},
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},

‎docs/doc_examples/43d9e314431336a6f084cea76dfd6489.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: "restaurants",
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retriever: {
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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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console.log(response);
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----

‎docs/doc_examples/1b60ad542abb511cbd926ac8c55b609c.asciidoc renamed to ‎docs/doc_examples/565386eee0951865a684e41fab53b40c.asciidoc

+1
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@@ -14,6 +14,7 @@ const response = await client.inference.put({
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min_number_of_allocations: 3,
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max_number_of_allocations: 10,
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},
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num_threads: 1,
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},
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},
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});

‎docs/doc_examples/58f6b72009512851843c7b7a20e9504a.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-index-000002",
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mappings: {
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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-for-ingest",
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search_inference_id: "my-elser-endpoint-for-search",
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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/69541f0bb81ab3797926bb2a00607cda.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-msmarco-minilm-model",
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inference_config: {
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service: "elasticsearch",
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service_settings: {
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num_allocations: 1,
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num_threads: 1,
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model_id: "cross-encoder__ms-marco-minilm-l-6-v2",
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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/6e6b78e6b689a5d6aa637271b6d084e2.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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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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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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console.log(response);
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----

‎docs/doc_examples/a4ec42130f3c75fc9d1d5f7cb6222cd5.asciidoc renamed to ‎docs/doc_examples/745864ef2427188241a4702b94ea57be.asciidoc

+1-1
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@@ -11,7 +11,7 @@ const response = await client.search({
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filter: {
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range: {
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price: {
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to: "500",
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lte: "500",
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},
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},
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},

‎docs/doc_examples/7b1b947bddd7e78f77da265f7e645a61.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-index-000004",
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mappings: {
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properties: {
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metrics: {
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subobjects: "auto",
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properties: {
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time: {
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type: "object",
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properties: {
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min: {
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type: "long",
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},
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},
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},
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to: {
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type: "object",
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properties: {
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inner_metrics: {
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type: "object",
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subobjects: "auto",
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properties: {
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time: {
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type: "object",
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properties: {
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max: {
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type: "long",
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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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},
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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.indices.getMapping({
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index: "my-index-000004",
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});
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console.log(response1);
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----

‎docs/doc_examples/9aa2327ae315c39f2bce2bd22e0deb1b.asciidoc renamed to ‎docs/doc_examples/8417d8d35ec5fc5665dfb2f95d6d1101.asciidoc

+1-1
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@@ -17,7 +17,7 @@ const response = await client.search({
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{
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range: {
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"result.execution_time": {
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from: "now-10s",
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gte: "now-10s",
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},
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},
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},

‎docs/doc_examples/8477e77e4fad19af66f03f81b8f2592b.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-index",
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query: {
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semantic: {
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field: "inference_field",
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query: "mountain lake",
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inner_hits: {
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from: 1,
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size: 1,
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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/bdb671866e2f0195f8dfbdb7f20bf591.asciidoc renamed to ‎docs/doc_examples/8cad5d95a0e7c103f08be53d0b172558.asciidoc

+5-1
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@@ -9,7 +9,11 @@ const response = await client.inference.put({
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inference_config: {
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service: "elser",
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service_settings: {
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num_allocations: 1,
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adaptive_allocations: {
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enabled: true,
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min_number_of_allocations: 3,
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max_number_of_allocations: 10,
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},
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num_threads: 1,
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},
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},

‎docs/doc_examples/8d750dfc067b1184c32a2423c60e4d06.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-index",
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query: {
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semantic: {
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field: "inference_field",
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query: "mountain lake",
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inner_hits: {},
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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/9169d19a80175ec94f80865d0f9bef4c.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: "restaurants",
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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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multi_match: {
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query: "Austria",
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fields: ["city", "region"],
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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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rank_constant: 1,
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rank_window_size: 50,
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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/971fd23adb81bb5842c7750e0379336a.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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retriever: {
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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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match: {
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genre: "drama",
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},
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},
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},
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},
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field: "plot",
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inference_id: "my-msmarco-minilm-model",
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inference_text: "films that explore psychological depths",
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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/9dfe3b02bd15409b4b8b36e9756e8f94.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.index({
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index: "my-index",
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id: "lake_tahoe",
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document: {
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inference_field: [
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"Lake Tahoe is the largest alpine lake in North America",
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"When hiking in the area, please be on alert for bears",
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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/a1dda7e7c01be96a4acf7b725d70385f.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: "index",
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retriever: {
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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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match_phrase: {
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text: "landmark in Paris",
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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-cohere-rerank-model",
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inference_text: "Most famous landmark in Paris",
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rank_window_size: 100,
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min_score: 0.5,
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},
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},
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});
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console.log(response);
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----

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