Auto-generated code for 8.x (#2500)
This commit is contained in:
Elastic Machine
46
docs/doc_examples/015e6e6132b6d6d44bddb06bc3b316ed.asciidoc
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46
docs/doc_examples/015e6e6132b6d6d44bddb06bc3b316ed.asciidoc
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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
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18
docs/doc_examples/0165d22da5f2fc7678392b31d8eb5566.asciidoc
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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",
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||||
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
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49
docs/doc_examples/0bc6155e0c88062a4d8490da49db3aa8.asciidoc
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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);
|
||||
----
|
||||
57
docs/doc_examples/0d689ac6e78be5d438f9b5d441be2b44.asciidoc
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57
docs/doc_examples/0d689ac6e78be5d438f9b5d441be2b44.asciidoc
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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);
|
||||
----
|
||||
@ -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,
|
||||
@ -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);
|
||||
----
|
||||
@ -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);
|
||||
----
|
||||
@ -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
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23
docs/doc_examples/30d051f534aeb884176eedb2c11dac85.asciidoc
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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);
|
||||
----
|
||||
@ -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",
|
||||
@ -9,7 +9,6 @@ const response = await client.indices.create({
|
||||
properties: {
|
||||
inference_field: {
|
||||
type: "semantic_text",
|
||||
inference_id: "my-elser-endpoint",
|
||||
},
|
||||
},
|
||||
},
|
||||
@ -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: {},
|
||||
});
|
||||
44
docs/doc_examples/76e02434835630cb830724beb92df354.asciidoc
Normal file
44
docs/doc_examples/76e02434835630cb830724beb92df354.asciidoc
Normal 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: [
|
||||
{
|
||||
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);
|
||||
----
|
||||
46
docs/doc_examples/78043831fd32004a82930c8ac8a1d809.asciidoc
Normal file
46
docs/doc_examples/78043831fd32004a82930c8ac8a1d809.asciidoc
Normal file
@ -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
23
docs/doc_examples/79d206a528be704050a437adce2496dd.asciidoc
Normal file
@ -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);
|
||||
----
|
||||
@ -4,7 +4,7 @@
|
||||
[source, js]
|
||||
----
|
||||
const response = await client.indices.create({
|
||||
index: "my-index-000002",
|
||||
index: "my-index-000003",
|
||||
mappings: {
|
||||
properties: {
|
||||
inference_field: {
|
||||
@ -9,7 +9,6 @@ const response = await client.indices.create({
|
||||
properties: {
|
||||
content: {
|
||||
type: "semantic_text",
|
||||
inference_id: "my-elser-endpoint",
|
||||
},
|
||||
},
|
||||
},
|
||||
13
docs/doc_examples/9313f534e1aa266cde7d4af74665497f.asciidoc
Normal file
13
docs/doc_examples/9313f534e1aa266cde7d4af74665497f.asciidoc
Normal file
@ -0,0 +1,13 @@
|
||||
// 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.connector.put({
|
||||
connector_id: "my-{service-name-stub}-connector",
|
||||
index_name: "my-elasticsearch-index",
|
||||
name: "Content synced from {service-name}",
|
||||
service_type: "{service-name-stub}",
|
||||
});
|
||||
console.log(response);
|
||||
----
|
||||
18
docs/doc_examples/96e88611f99e6834bd64b58dc8a282c1.asciidoc
Normal file
18
docs/doc_examples/96e88611f99e6834bd64b58dc8a282c1.asciidoc
Normal file
@ -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
37
docs/doc_examples/97c6c07f46f4177f0565a04bc50924a3.asciidoc
Normal 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: {
|
||||
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);
|
||||
----
|
||||
27
docs/doc_examples/a9f14efc26fdd3c37a71f06c310163d9.asciidoc
Normal file
27
docs/doc_examples/a9f14efc26fdd3c37a71f06c310163d9.asciidoc
Normal 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
37
docs/doc_examples/ac22cc2b0f4ad659055feed2852a2d59.asciidoc
Normal 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);
|
||||
----
|
||||
@ -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",
|
||||
14
docs/doc_examples/ae3473adaf1515afcf7773f26c018e5c.asciidoc
Normal file
14
docs/doc_examples/ae3473adaf1515afcf7773f26c018e5c.asciidoc
Normal file
@ -0,0 +1,14 @@
|
||||
// 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.connector.put({
|
||||
connector_id: "my-{service-name-stub}-connector",
|
||||
index_name: "my-elasticsearch-index",
|
||||
name: "Content synced from {service-name}",
|
||||
service_type: "{service-name-stub}",
|
||||
is_native: true,
|
||||
});
|
||||
console.log(response);
|
||||
----
|
||||
45
docs/doc_examples/bb2ba5d1885f87506f90dbb002e518f4.asciidoc
Normal file
45
docs/doc_examples/bb2ba5d1885f87506f90dbb002e518f4.asciidoc
Normal 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);
|
||||
----
|
||||
83
docs/doc_examples/bee3fda7bb07086243424b62e5b16ca7.asciidoc
Normal file
83
docs/doc_examples/bee3fda7bb07086243424b62e5b16ca7.asciidoc
Normal 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);
|
||||
----
|
||||
@ -1,11 +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.getSettings({
|
||||
flat_settings: "true",
|
||||
filter_path: "transient",
|
||||
});
|
||||
console.log(response);
|
||||
----
|
||||
154
docs/doc_examples/d4158d486e7fee2702a14068b69e3b33.asciidoc
Normal file
154
docs/doc_examples/d4158d486e7fee2702a14068b69e3b33.asciidoc
Normal 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);
|
||||
----
|
||||
16
docs/doc_examples/e22a1da3c622611be6855e534c0709ae.asciidoc
Normal file
16
docs/doc_examples/e22a1da3c622611be6855e534c0709ae.asciidoc
Normal file
@ -0,0 +1,16 @@
|
||||
// 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: "POST",
|
||||
path: "/_query_rules/my-ruleset/_test",
|
||||
body: {
|
||||
match_criteria: {
|
||||
query_string: "puggles",
|
||||
},
|
||||
},
|
||||
});
|
||||
console.log(response);
|
||||
----
|
||||
44
docs/doc_examples/e6f6d3aeea7ecea47cfd5c3d727f7004.asciidoc
Normal file
44
docs/doc_examples/e6f6d3aeea7ecea47cfd5c3d727f7004.asciidoc
Normal 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
94
docs/doc_examples/ee05714a83d75fb6858e3b9fcbeb8f8b.asciidoc
Normal 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);
|
||||
----
|
||||
Reference in New Issue
Block a user