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Auto-generated code for 8.15 (#2501)

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This commit is contained in:
Elastic Machine
2024-12-02 18:07:32 +00:00
committed by GitHub
parent 7d4ae5e3dd
commit c69fbac3ab
60 changed files with 1007 additions and 106 deletions
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46
docs/doc_examples/015e6e6132b6d6d44bddb06bc3b316ed.asciidoc Normal file
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@ -0,0 +1,46 @@
// 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.search({
index: "retrievers_example",
retriever: {
rrf: {
retrievers: [
{
standard: {
query: {
range: {
year: {
gt: 2023,
},
},
},
},
},
{
standard: {
query: {
term: {
topic: "elastic",
},
},
},
},
],
rank_window_size: 10,
rank_constant: 1,
},
},
_source: false,
aggs: {
topics: {
terms: {
field: "topic",
},
},
},
});
console.log(response);
----

18
docs/doc_examples/0165d22da5f2fc7678392b31d8eb5566.asciidoc Normal file
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@ -0,0 +1,18 @@
// 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.inference.put({
task_type: "rerank",
inference_id: "my-rerank-model",
inference_config: {
service: "cohere",
service_settings: {
model_id: "rerank-english-v3.0",
api_key: "{{COHERE_API_KEY}}",
},
},
});
console.log(response);
----

49
docs/doc_examples/0bc6155e0c88062a4d8490da49db3aa8.asciidoc Normal file
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@ -0,0 +1,49 @@
// 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.search({
index: "retrievers_example_nested",
retriever: {
rrf: {
retrievers: [
{
standard: {
query: {
nested: {
path: "nested_field",
inner_hits: {
name: "nested_vector",
_source: false,
fields: ["nested_field.paragraph_id"],
},
query: {
knn: {
field: "nested_field.nested_vector",
query_vector: [1, 0, 0.5],
k: 10,
},
},
},
},
},
},
{
standard: {
query: {
term: {
topic: "ai",
},
},
},
},
],
rank_window_size: 10,
rank_constant: 1,
},
},
_source: ["topic"],
});
console.log(response);
----

8
docs/doc_examples/0bee07a581c5776e068f6f4efad5a399.asciidoc
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@ -3,8 +3,12 @@
[source, js]
----
const response = await client.esql.asyncQuery({
format: "json",
const response = await client.transport.request({
method: "POST",
path: "/_query/async",
querystring: {
format: "json",
},
body: {
query:
"\n FROM my-index-000001,cluster_one:my-index-000001,cluster_two:my-index*\n | STATS COUNT(http.response.status_code) BY user.id\n | LIMIT 2\n ",

57
docs/doc_examples/0d689ac6e78be5d438f9b5d441be2b44.asciidoc Normal file
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@ -0,0 +1,57 @@
// 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.search({
index: "retrievers_example",
retriever: {
rrf: {
retrievers: [
{
standard: {
query: {
term: {
topic: "elastic",
},
},
},
},
{
rrf: {
retrievers: [
{
standard: {
query: {
query_string: {
query:
"(information retrieval) OR (artificial intelligence)",
default_field: "text",
},
},
},
},
{
knn: {
field: "vector",
query_vector: [0.23, 0.67, 0.89],
k: 3,
num_candidates: 5,
},
},
],
rank_window_size: 10,
rank_constant: 1,
},
},
],
rank_window_size: 10,
rank_constant: 1,
},
},
_source: false,
size: 1,
explain: true,
});
console.log(response);
----

5
docs/doc_examples/0dfde6a9d953822fd4b3aa0121ddd8fb.asciidoc
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@ -3,8 +3,9 @@
[source, js]
----
const response = await client.searchApplication.renderQuery({
name: "my-app",
const response = await client.transport.request({
method: "POST",
path: "/_application/search_application/my-app/_render_query",
body: {
params: {
query_string: "my first query",

2
docs/doc_examples/16634cfa7916cf4e8048a1d70e6240f2.asciidoc
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@ -11,7 +11,7 @@ const response = await client.searchApplication.put({
script: {
lang: "mustache",
source:
'\n {\n "query": {\n "bool": {\n "must": [\n {{#query}}\n \n {{/query}}\n ],\n "filter": {{#toJson}}_es_filters{{/toJson}}\n }\n },\n "_source": {\n "includes": ["title", "plot"]\n },\n "highlight": {\n "fields": {\n "title": { "fragment_size": 0 },\n "plot": { "fragment_size": 200 }\n }\n },\n "aggs": {{#toJson}}_es_aggs{{/toJson}},\n "from": {{from}},\n "size": {{size}},\n "sort": {{#toJson}}_es_sort_fields{{/toJson}}\n }\n ',
'\n {\n "query": {\n "bool": {\n "must": [\n {{#query}}\n {{/query}}\n ],\n "filter": {{#toJson}}_es_filters{{/toJson}}\n }\n },\n "_source": {\n "includes": ["title", "plot"]\n },\n "highlight": {\n "fields": {\n "title": { "fragment_size": 0 },\n "plot": { "fragment_size": 200 }\n }\n },\n "aggs": {{#toJson}}_es_aggs{{/toJson}},\n "from": {{from}},\n "size": {{size}},\n "sort": {{#toJson}}_es_sort_fields{{/toJson}}\n }\n ',
params: {
query: "",
_es_filters: {},

2
docs/doc_examples/c02c2916b97b6fa7db82dbc7f0378310.asciidoc → docs/doc_examples/17b1647c8509543f2388c886f2584a20.asciidoc
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@ -16,7 +16,7 @@ const response = await client.search({
},
},
field: "text",
inference_id: "my-cohere-rerank-model",
inference_id: "elastic-rerank",
inference_text: "How often does the moon hide the sun?",
rank_window_size: 100,
min_score: 0.5,

4
docs/doc_examples/19c00c6b29bc7dbc5e92b3668da2da93.asciidoc
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@ -3,7 +3,9 @@
[source, js]
----
const response = await client.simulate.ingest({
const response = await client.transport.request({
method: "POST",
path: "/_ingest/_simulate",
body: {
docs: [
{

4
docs/doc_examples/4dab4c5168047ba596af1beb0e55b845.asciidoc → docs/doc_examples/22b176a184517cf1b5801f5eb4f17f97.asciidoc
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@ -3,8 +3,8 @@
[source, js]
----
const response = await client.cluster.getSettings({
flat_settings: "true",
const response = await client.indices.rollover({
alias: "datastream",
});
console.log(response);
----

28
docs/doc_examples/2577acb462b95bd4394523cf2f8a661f.asciidoc
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@ -1,28 +0,0 @@
// 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.esql.query({
format: "txt",
query:
"\n FROM library\n | SORT page_count DESC\n | KEEP name, author\n | LOOKUP era ON author\n | LIMIT 5\n ",
tables: {
era: {
author: {
keyword: [
"Frank Herbert",
"Peter F. Hamilton",
"Vernor Vinge",
"Alastair Reynolds",
"James S.A. Corey",
],
},
era: {
keyword: ["The New Wave", "Diamond", "Diamond", "Diamond", "Hadron"],
},
},
},
});
console.log(response);
----

4
docs/doc_examples/2a1eece9a59ac1773edcf0a932c26de0.asciidoc
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@ -3,7 +3,9 @@
[source, js]
----
const response = await client.security.oidcLogout({
const response = await client.transport.request({
method: "POST",
path: "/_security/oidc/logout",
body: {
token:
"dGhpcyBpcyBub3QgYSByZWFsIHRva2VuIGJ1dCBpdCBpcyBvbmx5IHRlc3QgZGF0YS4gZG8gbm90IHRyeSB0byByZWFkIHRva2VuIQ==",

10
docs/doc_examples/2afdf0d83724953aa2875b5fb37d60cc.asciidoc
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@ -3,10 +3,12 @@
[source, js]
----
const response = await client.esql.asyncQueryGet({
id: "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
wait_for_completion_timeout: "30s",
body: null,
const response = await client.transport.request({
method: "GET",
path: "/_query/async/FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
querystring: {
wait_for_completion_timeout: "30s",
},
});
console.log(response);
----

16
docs/doc_examples/2bb41b0b4876ce98cd0cd8fb6d337f18.asciidoc
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@ -1,16 +0,0 @@
// 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.cluster.putSettings({
persistent: {
"cluster.indices.close.enable": false,
"indices.recovery.max_bytes_per_sec": "50mb",
},
transient: {
"*": null,
},
});
console.log(response);
----

23
docs/doc_examples/30d051f534aeb884176eedb2c11dac85.asciidoc Normal file
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@ -0,0 +1,23 @@
// 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.inference.put({
task_type: "rerank",
inference_id: "my-elastic-rerank",
inference_config: {
service: "elasticsearch",
service_settings: {
model_id: ".rerank-v1",
num_threads: 1,
adaptive_allocations: {
enabled: true,
min_number_of_allocations: 1,
max_number_of_allocations: 4,
},
},
},
});
console.log(response);
----

4
docs/doc_examples/3f1fe5f5f99b98d0891f38003e10b636.asciidoc
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@ -3,7 +3,9 @@
[source, js]
----
const response = await client.esql.asyncQuery({
const response = await client.transport.request({
method: "POST",
path: "/_query/async",
body: {
query:
"\n FROM library\n | EVAL year = DATE_TRUNC(1 YEARS, release_date)\n | STATS MAX(page_count) BY year\n | SORT year\n | LIMIT 5\n ",

6
docs/doc_examples/405511f7c1f12cc0a227b4563fe7b2e2.asciidoc
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@ -3,9 +3,9 @@
[source, js]
----
const response = await client.esql.asyncQueryGet({
id: "FkpMRkJGS1gzVDRlM3g4ZzMyRGlLbkEaTXlJZHdNT09TU2VTZVBoNDM3cFZMUToxMDM=",
body: null,
const response = await client.transport.request({
method: "GET",
path: "/_query/async/FkpMRkJGS1gzVDRlM3g4ZzMyRGlLbkEaTXlJZHdNT09TU2VTZVBoNDM3cFZMUToxMDM=",
});
console.log(response);
----

6
docs/doc_examples/4b91ad7c9b44e07db4a4e81390f19ad3.asciidoc
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@ -3,9 +3,9 @@
[source, js]
----
const response = await client.inference.streamInference({
task_type: "completion",
inference_id: "openai-completion",
const response = await client.transport.request({
method: "POST",
path: "/_inference/completion/openai-completion/_stream",
body: {
input: "What is Elastic?",
},

10
docs/doc_examples/191074b2eebd5f74e628c2ada4b6d2e4.asciidoc → docs/doc_examples/4edfb5934d14ad7655bd7e19a112b5c0.asciidoc
View File

@ -8,11 +8,6 @@ const response = await client.search({
query: {
bool: {
must: [
{
term: {
"category.keyword": "Main Course",
},
},
{
term: {
tags: "vegetarian",
@ -27,6 +22,11 @@ const response = await client.search({
},
],
should: [
{
term: {
category: "Main Course",
},
},
{
multi_match: {
query: "curry spicy",

4
docs/doc_examples/57dc15e5ad663c342fd5c1d86fcd1b29.asciidoc
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@ -3,7 +3,9 @@
[source, js]
----
const response = await client.security.oidcPrepareAuthentication({
const response = await client.transport.request({
method: "POST",
path: "/_security/oidc/prepare",
body: {
realm: "oidc1",
state: "lGYK0EcSLjqH6pkT5EVZjC6eIW5YCGgywj2sxROO",

8
docs/doc_examples/5bba213a7f543190139d1a69ab2ed076.asciidoc
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@ -3,8 +3,12 @@
[source, js]
----
const response = await client.esql.asyncQuery({
format: "json",
const response = await client.transport.request({
method: "POST",
path: "/_query/async",
querystring: {
format: "json",
},
body: {
query:
"\n FROM cluster_one:my-index*,cluster_two:logs*\n | STATS COUNT(http.response.status_code) BY user.id\n | LIMIT 2\n ",

1
docs/doc_examples/19f1f9f25933f8e7aba59a10881c648b.asciidoc → docs/doc_examples/5f16358ebb5d14b86f57612d5f92d923.asciidoc
View File

@ -9,7 +9,6 @@ const response = await client.indices.create({
properties: {
inference_field: {
type: "semantic_text",
inference_id: "my-elser-endpoint",
},
},
},

2
docs/doc_examples/2fd458d37aab509fe2d970c0b6e2a10f.asciidoc → docs/doc_examples/68d7f7d4d268ee98caead5aef19933d6.asciidoc
View File

@ -45,7 +45,7 @@ console.log(response);
const response1 = await client.indices.putIndexTemplate({
name: 2,
index_patterns: ["k8s*"],
index_patterns: ["k9s*"],
composed_of: ["destination_template"],
data_stream: {},
});

2
docs/doc_examples/6b6fd0a5942dfb9762ad2790cf421a80.asciidoc
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@ -11,7 +11,7 @@ const response = await client.searchApplication.put({
script: {
lang: "mustache",
source:
'\n {\n "query": {\n "bool": {\n "must": [\n {{#query}}\n \n {{/query}}\n ],\n "filter": {{#toJson}}_es_filters{{/toJson}}\n }\n },\n "_source": {\n "includes": ["title", "plot"]\n },\n "aggs": {{#toJson}}_es_aggs{{/toJson}},\n "from": {{from}},\n "size": {{size}},\n "sort": {{#toJson}}_es_sort_fields{{/toJson}}\n }\n ',
'\n {\n "query": {\n "bool": {\n "must": [\n {{#query}}\n {{/query}}\n ],\n "filter": {{#toJson}}_es_filters{{/toJson}}\n }\n },\n "_source": {\n "includes": ["title", "plot"]\n },\n "aggs": {{#toJson}}_es_aggs{{/toJson}},\n "from": {{from}},\n "size": {{size}},\n "sort": {{#toJson}}_es_sort_fields{{/toJson}}\n }\n ',
params: {
query: "",
_es_filters: {},

4
docs/doc_examples/6f3b723bf6179b96c3413597ed7f49e1.asciidoc
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@ -3,7 +3,9 @@
[source, js]
----
const response = await client.security.bulkUpdateApiKeys({
const response = await client.transport.request({
method: "POST",
path: "/_security/api_key/_bulk_update",
body: {
ids: ["VuaCfGcBCdbkQm-e5aOx", "H3_AhoIBA9hmeQJdg7ij"],
},

44
docs/doc_examples/76e02434835630cb830724beb92df354.asciidoc Normal file
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@ -0,0 +1,44 @@
// 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.search({
index: "retrievers_example",
retriever: {
rrf: {
retrievers: [
{
knn: {
field: "vector",
query_vector: [0.23, 0.67, 0.89],
k: 3,
num_candidates: 5,
},
},
{
text_similarity_reranker: {
retriever: {
standard: {
query: {
term: {
topic: "ai",
},
},
},
},
field: "text",
inference_id: "my-rerank-model",
inference_text:
"Can I use generative AI to identify user intent and improve search relevance?",
},
},
],
rank_window_size: 10,
rank_constant: 1,
},
},
_source: false,
});
console.log(response);
----

4
docs/doc_examples/77518e8c6198acfe77c0934fd2fe65cb.asciidoc
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@ -3,7 +3,9 @@
[source, js]
----
const response = await client.textStructure.findMessageStructure({
const response = await client.transport.request({
method: "POST",
path: "/_text_structure/find_message_structure",
body: {
messages: [
"[2024-03-05T10:52:36,256][INFO ][o.a.l.u.VectorUtilPanamaProvider] [laptop] Java vector incubator API enabled; uses preferredBitSize=128",

46
docs/doc_examples/78043831fd32004a82930c8ac8a1d809.asciidoc Normal file
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@ -0,0 +1,46 @@
// 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.search({
index: "retrievers_example",
retriever: {
text_similarity_reranker: {
retriever: {
rrf: {
retrievers: [
{
standard: {
query: {
query_string: {
query:
"(information retrieval) OR (artificial intelligence)",
default_field: "text",
},
},
},
},
{
knn: {
field: "vector",
query_vector: [0.23, 0.67, 0.89],
k: 3,
num_candidates: 5,
},
},
],
rank_window_size: 10,
rank_constant: 1,
},
},
field: "text",
inference_id: "my-rerank-model",
inference_text:
"What are the state of the art applications of AI in information retrieval?",
},
},
_source: false,
});
console.log(response);
----

23
docs/doc_examples/79d206a528be704050a437adce2496dd.asciidoc Normal file
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@ -0,0 +1,23 @@
// 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.inference.put({
task_type: "rerank",
inference_id: "my-elastic-rerank",
inference_config: {
service: "elasticsearch",
service_settings: {
model_id: ".rerank-v1",
num_threads: 1,
adaptive_allocations: {
enabled: true,
min_number_of_allocations: 1,
max_number_of_allocations: 10,
},
},
},
});
console.log(response);
----

2
docs/doc_examples/58f6b72009512851843c7b7a20e9504a.asciidoc → docs/doc_examples/7a2fdfd7b0553d63440af7598f9ad867.asciidoc
View File

@ -4,7 +4,7 @@
[source, js]
----
const response = await client.indices.create({
index: "my-index-000002",
index: "my-index-000003",
mappings: {
properties: {
inference_field: {

11
docs/doc_examples/7ba29f0be2297b54a640b0a17d7ef5ca.asciidoc Normal file
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@ -0,0 +1,11 @@
// 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.transport.request({
method: "DELETE",
path: "/_ingest/ip_location/database/my-database-id",
});
console.log(response);
----

4
docs/doc_examples/80dd7f5882c59b9c1c90e8351937441f.asciidoc
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@ -3,7 +3,9 @@
[source, js]
----
const response = await client.security.bulkUpdateApiKeys({
const response = await client.transport.request({
method: "POST",
path: "/_security/api_key/_bulk_update",
body: {
ids: ["VuaCfGcBCdbkQm-e5aOx", "H3_AhoIBA9hmeQJdg7ij"],
role_descriptors: {

1
docs/doc_examples/c8fa8d7e029792d539464fede18ce258.asciidoc → docs/doc_examples/8a0b5f759de3f27f0801c1176e616117.asciidoc
View File

@ -9,7 +9,6 @@ const response = await client.indices.create({
properties: {
content: {
type: "semantic_text",
inference_id: "my-elser-endpoint",
},
},
},

17
docs/doc_examples/91e106a2affbc8df32cd940684a779ed.asciidoc Normal file
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@ -0,0 +1,17 @@
// 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.transport.request({
method: "PUT",
path: "/_ingest/ip_location/database/my-database-1",
body: {
name: "GeoIP2-Domain",
maxmind: {
account_id: "1234567",
},
},
});
console.log(response);
----

18
docs/doc_examples/96e88611f99e6834bd64b58dc8a282c1.asciidoc Normal file
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@ -0,0 +1,18 @@
// 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: "my-index-000002",
mappings: {
properties: {
inference_field: {
type: "semantic_text",
inference_id: "my-openai-endpoint",
},
},
},
});
console.log(response);
----

37
docs/doc_examples/97c6c07f46f4177f0565a04bc50924a3.asciidoc Normal file
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@ -0,0 +1,37 @@
// 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.search({
index: "retrievers_example",
retriever: {
rrf: {
retrievers: [
{
standard: {
query: {
query_string: {
query: "(information retrieval) OR (artificial intelligence)",
default_field: "text",
},
},
},
},
{
knn: {
field: "vector",
query_vector: [0.23, 0.67, 0.89],
k: 3,
num_candidates: 5,
},
},
],
rank_window_size: 10,
rank_constant: 1,
},
},
_source: false,
});
console.log(response);
----

11
docs/doc_examples/99fb82d49ac477e6a9dfdd71f9465374.asciidoc Normal file
View File

@ -0,0 +1,11 @@
// 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.transport.request({
method: "DELETE",
path: "/_ingest/ip_location/database/example-database-id",
});
console.log(response);
----

6
docs/doc_examples/9afa0844883b7471883aa378a8dd10b4.asciidoc
View File

@ -3,9 +3,9 @@
[source, js]
----
const response = await client.searchApplication.postBehavioralAnalyticsEvent({
collection_name: "my_analytics_collection",
event_type: "search_click",
const response = await client.transport.request({
method: "POST",
path: "/_application/analytics/my_analytics_collection/event/search_click",
body: {
session: {
id: "1797ca95-91c9-4e2e-b1bd-9c38e6f386a9",

4
docs/doc_examples/9c01db07c9ac395b6370e3b33965c21f.asciidoc
View File

@ -3,7 +3,9 @@
[source, js]
----
const response = await client.security.oidcAuthenticate({
const response = await client.transport.request({
method: "POST",
path: "/_security/oidc/authenticate",
body: {
redirect_uri:
"https://oidc-kibana.elastic.co:5603/api/security/oidc/callback?code=jtI3Ntt8v3_XvcLzCFGq&state=4dbrihtIAt3wBTwo6DxK-vdk-sSyDBV8Yf0AjdkdT5I",

5
docs/doc_examples/a162eb50853331c80596f5994e9d1c38.asciidoc
View File

@ -3,8 +3,9 @@
[source, js]
----
const response = await client.searchApplication.renderQuery({
name: "my_search_application",
const response = await client.transport.request({
method: "POST",
path: "/_application/search_application/my_search_application/_render_query",
body: {
params: {
query_string: "rock climbing",

27
docs/doc_examples/a9f14efc26fdd3c37a71f06c310163d9.asciidoc Normal file
View File

@ -0,0 +1,27 @@
// 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.search({
retriever: {
text_similarity_reranker: {
retriever: {
standard: {
query: {
match: {
text: "How often does the moon hide the sun?",
},
},
},
},
field: "text",
inference_id: "my-elastic-rerank",
inference_text: "How often does the moon hide the sun?",
rank_window_size: 100,
min_score: 0.5,
},
},
});
console.log(response);
----

37
docs/doc_examples/ac22cc2b0f4ad659055feed2852a2d59.asciidoc Normal file
View File

@ -0,0 +1,37 @@
// 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.search({
index: "retrievers_example",
retriever: {
text_similarity_reranker: {
retriever: {
text_similarity_reranker: {
retriever: {
knn: {
field: "vector",
query_vector: [0.23, 0.67, 0.89],
k: 3,
num_candidates: 5,
},
},
rank_window_size: 100,
field: "text",
inference_id: "my-rerank-model",
inference_text:
"What are the state of the art applications of AI in information retrieval?",
},
},
rank_window_size: 10,
field: "text",
inference_id: "my-other-more-expensive-rerank-model",
inference_text:
"Applications of Large Language Models in technology and their impact on user satisfaction",
},
},
_source: false,
});
console.log(response);
----

2
docs/doc_examples/7b9691bd34a02dd859562eb927f175e0.asciidoc → docs/doc_examples/ad9889fd8a4b5930e312a51f3bc996dc.asciidoc
View File

@ -12,7 +12,7 @@ const response = await client.inference.put({
adaptive_allocations: {
enabled: true,
min_number_of_allocations: 1,
max_number_of_allocations: 10,
max_number_of_allocations: 4,
},
num_threads: 1,
model_id: ".elser_model_2",

6
docs/doc_examples/b0bddf2ffaa83049b195829c06b875cd.asciidoc
View File

@ -3,9 +3,9 @@
[source, js]
----
const response = await client.searchApplication.renderQuery({
name: "my_search_application",
body: null,
const response = await client.transport.request({
method: "POST",
path: "/_application/search_application/my_search_application/_render_query",
});
console.log(response);
----

11
docs/doc_examples/b62eaa20c4e0e48134a6d1d1b3c30b26.asciidoc
View File

@ -208,10 +208,13 @@ const response = await client.bulk({
});
console.log(response);
const response1 = await client.textStructure.findFieldStructure({
index: "test-logs",
field: "message",
body: null,
const response1 = await client.transport.request({
method: "GET",
path: "/_text_structure/find_field_structure",
querystring: {
index: "test-logs",
field: "message",
},
});
console.log(response1);
----

45
docs/doc_examples/bb2ba5d1885f87506f90dbb002e518f4.asciidoc Normal file
View File

@ -0,0 +1,45 @@
// 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.search({
index: "retrievers_example",
retriever: {
rrf: {
retrievers: [
{
standard: {
query: {
query_string: {
query: "(information retrieval) OR (artificial intelligence)",
default_field: "text",
},
},
},
},
{
knn: {
field: "vector",
query_vector: [0.23, 0.67, 0.89],
k: 3,
num_candidates: 5,
},
},
],
rank_window_size: 10,
rank_constant: 1,
},
},
highlight: {
fields: {
text: {
fragment_size: 150,
number_of_fragments: 3,
},
},
},
_source: false,
});
console.log(response);
----

4
docs/doc_examples/bcdfaa4487747249699a86a0dcd22f5e.asciidoc
View File

@ -3,7 +3,9 @@
[source, js]
----
const response = await client.simulate.ingest({
const response = await client.transport.request({
method: "POST",
path: "/_ingest/_simulate",
body: {
docs: [
{

83
docs/doc_examples/bee3fda7bb07086243424b62e5b16ca7.asciidoc Normal file
View File

@ -0,0 +1,83 @@
// 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: "retrievers_example_nested",
mappings: {
properties: {
nested_field: {
type: "nested",
properties: {
paragraph_id: {
type: "keyword",
},
nested_vector: {
type: "dense_vector",
dims: 3,
similarity: "l2_norm",
index: true,
},
},
},
topic: {
type: "keyword",
},
},
},
});
console.log(response);
const response1 = await client.index({
index: "retrievers_example_nested",
id: 1,
document: {
nested_field: [
{
paragraph_id: "1a",
nested_vector: [-1.12, -0.59, 0.78],
},
{
paragraph_id: "1b",
nested_vector: [-0.12, 1.56, 0.42],
},
{
paragraph_id: "1c",
nested_vector: [1, -1, 0],
},
],
topic: ["ai"],
},
});
console.log(response1);
const response2 = await client.index({
index: "retrievers_example_nested",
id: 2,
document: {
nested_field: [
{
paragraph_id: "2a",
nested_vector: [0.23, 1.24, 0.65],
},
],
topic: ["information_retrieval"],
},
});
console.log(response2);
const response3 = await client.index({
index: "retrievers_example_nested",
id: 3,
document: {
topic: ["ai"],
},
});
console.log(response3);
const response4 = await client.indices.refresh({
index: "retrievers_example_nested",
});
console.log(response4);
----

4
docs/doc_examples/c580990a70028bb49cca8a6bde86bbf6.asciidoc
View File

@ -3,7 +3,9 @@
[source, js]
----
const response = await client.security.bulkUpdateApiKeys({
const response = await client.transport.request({
method: "POST",
path: "/_security/api_key/_bulk_update",
body: {
ids: ["VuaCfGcBCdbkQm-e5aOx", "H3_AhoIBA9hmeQJdg7ij"],
role_descriptors: {},

4
docs/doc_examples/ccc613951c61f0b17e1ed8a2d3ae54a2.asciidoc
View File

@ -3,7 +3,9 @@
[source, js]
----
const response = await client.simulate.ingest({
const response = await client.transport.request({
method: "POST",
path: "/_ingest/_simulate",
body: {
docs: [
{

4
docs/doc_examples/d35c8cf7a98b3f112e1de8797ec6689d.asciidoc
View File

@ -3,7 +3,9 @@
[source, js]
----
const response = await client.security.oidcPrepareAuthentication({
const response = await client.transport.request({
method: "POST",
path: "/_security/oidc/prepare",
body: {
iss: "http://127.0.0.1:8080",
login_hint: "this_is_an_opaque_string",

154
docs/doc_examples/d4158d486e7fee2702a14068b69e3b33.asciidoc Normal file
View File

@ -0,0 +1,154 @@
// 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.putIndexTemplate({
name: "datastream_template",
index_patterns: ["datastream*"],
data_stream: {},
template: {
lifecycle: {
downsampling: [
{
after: "1m",
fixed_interval: "1h",
},
],
},
settings: {
index: {
mode: "time_series",
},
},
mappings: {
properties: {
"@timestamp": {
type: "date",
},
kubernetes: {
properties: {
container: {
properties: {
cpu: {
properties: {
usage: {
properties: {
core: {
properties: {
ns: {
type: "long",
},
},
},
limit: {
properties: {
pct: {
type: "float",
},
},
},
nanocores: {
type: "long",
time_series_metric: "gauge",
},
node: {
properties: {
pct: {
type: "float",
},
},
},
},
},
},
},
memory: {
properties: {
available: {
properties: {
bytes: {
type: "long",
time_series_metric: "gauge",
},
},
},
majorpagefaults: {
type: "long",
},
pagefaults: {
type: "long",
time_series_metric: "gauge",
},
rss: {
properties: {
bytes: {
type: "long",
time_series_metric: "gauge",
},
},
},
usage: {
properties: {
bytes: {
type: "long",
time_series_metric: "gauge",
},
limit: {
properties: {
pct: {
type: "float",
},
},
},
node: {
properties: {
pct: {
type: "float",
},
},
},
},
},
workingset: {
properties: {
bytes: {
type: "long",
time_series_metric: "gauge",
},
},
},
},
},
name: {
type: "keyword",
},
start_time: {
type: "date",
},
},
},
host: {
type: "keyword",
time_series_dimension: true,
},
namespace: {
type: "keyword",
time_series_dimension: true,
},
node: {
type: "keyword",
time_series_dimension: true,
},
pod: {
type: "keyword",
time_series_dimension: true,
},
},
},
},
},
},
});
console.log(response);
----

15
docs/doc_examples/d4df39f72d3a3b80cd4042f6a21c3f19.asciidoc Normal file
View File

@ -0,0 +1,15 @@
// 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.transport.request({
method: "PUT",
path: "/_ingest/ip_location/database/my-database-2",
body: {
name: "standard_location",
ipinfo: {},
},
});
console.log(response);
----

6
docs/doc_examples/d01a590fa9ea8a0cb34ed8dda502296c.asciidoc → docs/doc_examples/d8c053ee26c1533ce936ec81101d8e1b.asciidoc
View File

@ -3,9 +3,9 @@
[source, js]
----
const response = await client.cluster.getSettings({
flat_settings: "true",
filter_path: "transient",
const response = await client.transport.request({
method: "GET",
path: "/_ingest/ip_location/database/my-database-id",
});
console.log(response);
----

5
docs/doc_examples/dd71b0c9f9197684ff29c61062c55660.asciidoc
View File

@ -3,6 +3,9 @@
[source, js]
----
const response = await client.security.getSettings();
const response = await client.transport.request({
method: "GET",
path: "/_security/settings",
});
console.log(response);
----

4
docs/doc_examples/e3019fd5f23458ae49ad9854c97d321c.asciidoc
View File

@ -3,7 +3,9 @@
[source, js]
----
const response = await client.security.oidcPrepareAuthentication({
const response = await client.transport.request({
method: "POST",
path: "/_security/oidc/prepare",
body: {
realm: "oidc1",
},

4
docs/doc_examples/e4b38973c74037335378d8480f1ce894.asciidoc
View File

@ -3,7 +3,9 @@
[source, js]
----
const response = await client.simulate.ingest({
const response = await client.transport.request({
method: "POST",
path: "/_ingest/_simulate",
body: {
docs: [
{

44
docs/doc_examples/e6f6d3aeea7ecea47cfd5c3d727f7004.asciidoc Normal file
View File

@ -0,0 +1,44 @@
// 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.search({
index: "retrievers_example",
retriever: {
rrf: {
retrievers: [
{
standard: {
query: {
query_string: {
query: "(information retrieval) OR (artificial intelligence)",
default_field: "text",
},
},
},
},
{
knn: {
field: "vector",
query_vector: [0.23, 0.67, 0.89],
k: 3,
num_candidates: 5,
},
},
],
rank_window_size: 10,
rank_constant: 1,
},
},
collapse: {
field: "year",
inner_hits: {
name: "topic related documents",
_source: ["year"],
},
},
_source: false,
});
console.log(response);
----

94
docs/doc_examples/ee05714a83d75fb6858e3b9fcbeb8f8b.asciidoc Normal file
View File

@ -0,0 +1,94 @@
// 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: "retrievers_example",
mappings: {
properties: {
vector: {
type: "dense_vector",
dims: 3,
similarity: "l2_norm",
index: true,
},
text: {
type: "text",
},
year: {
type: "integer",
},
topic: {
type: "keyword",
},
},
},
});
console.log(response);
const response1 = await client.index({
index: "retrievers_example",
id: 1,
document: {
vector: [0.23, 0.67, 0.89],
text: "Large language models are revolutionizing information retrieval by boosting search precision, deepening contextual understanding, and reshaping user experiences in data-rich environments.",
year: 2024,
topic: ["llm", "ai", "information_retrieval"],
},
});
console.log(response1);
const response2 = await client.index({
index: "retrievers_example",
id: 2,
document: {
vector: [0.12, 0.56, 0.78],
text: "Artificial intelligence is transforming medicine, from advancing diagnostics and tailoring treatment plans to empowering predictive patient care for improved health outcomes.",
year: 2023,
topic: ["ai", "medicine"],
},
});
console.log(response2);
const response3 = await client.index({
index: "retrievers_example",
id: 3,
document: {
vector: [0.45, 0.32, 0.91],
text: "AI is redefining security by enabling advanced threat detection, proactive risk analysis, and dynamic defenses against increasingly sophisticated cyber threats.",
year: 2024,
topic: ["ai", "security"],
},
});
console.log(response3);
const response4 = await client.index({
index: "retrievers_example",
id: 4,
document: {
vector: [0.34, 0.21, 0.98],
text: "Elastic introduces Elastic AI Assistant, the open, generative AI sidekick powered by ESRE to democratize cybersecurity and enable users of every skill level.",
year: 2023,
topic: ["ai", "elastic", "assistant"],
},
});
console.log(response4);
const response5 = await client.index({
index: "retrievers_example",
id: 5,
document: {
vector: [0.11, 0.65, 0.47],
text: "Learn how to spin up a deployment of our hosted Elasticsearch Service and use Elastic Observability to gain deeper insight into the behavior of your applications and systems.",
year: 2024,
topic: ["documentation", "observability", "elastic"],
},
});
console.log(response5);
const response6 = await client.indices.refresh({
index: "retrievers_example",
});
console.log(response6);
----

4
docs/doc_examples/f6f647eb644a2d236637ff05f833cb73.asciidoc
View File

@ -3,7 +3,9 @@
[source, js]
----
const response = await client.connector.secretPost({
const response = await client.transport.request({
method: "POST",
path: "/_connector/_secret",
body: {
value: "encoded_api_key",
},