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docs/doc_examples/2a67608dadbf220a2f040f3a79d3677d.asciidoc
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// 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.ingest.putPipeline({ | ||
id: "attachment", | ||
description: "Extract attachment information including original binary", | ||
processors: [ | ||
{ | ||
attachment: { | ||
field: "data", | ||
remove_binary: false, | ||
}, | ||
}, | ||
], | ||
}); | ||
console.log(response); | ||
const response1 = await client.index({ | ||
index: "my-index-000001", | ||
id: "my_id", | ||
pipeline: "attachment", | ||
document: { | ||
data: "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=", | ||
}, | ||
}); | ||
console.log(response1); | ||
const response2 = await client.get({ | ||
index: "my-index-000001", | ||
id: "my_id", | ||
}); | ||
console.log(response2); | ||
---- |
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17 changes: 0 additions & 17 deletions
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docs/doc_examples/49b31e23f8b9667b6a7b2734d55fb6ed.asciidoc
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docs/doc_examples/53d9d2ec9cb8d211772d764e76fe6890.asciidoc
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// 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.ingest.simulate({ | ||
id: "query_helper_pipeline", | ||
docs: [ | ||
{ | ||
_source: { | ||
content: | ||
"artificial intelligence in medicine articles published in the last 12 months", | ||
}, | ||
}, | ||
], | ||
}); | ||
console.log(response); | ||
---- |
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docs/doc_examples/5a70db31f587b7ffed5e9bc1445430cb.asciidoc
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22
docs/doc_examples/8cad5d95a0e7c103f08be53d0b172558.asciidoc
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docs/doc_examples/931817b168e055ecf738785c721125dd.asciidoc
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// 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.ingest.putPipeline({ | ||
id: "query_helper_pipeline", | ||
processors: [ | ||
{ | ||
script: { | ||
source: | ||
"ctx.prompt = 'Please generate an elasticsearch search query on index `articles_index` for the following natural language query. Dates are in the field `@timestamp`, document types are in the field `type` (options are `news`, `publication`), categories in the field `category` and can be multiple (options are `medicine`, `pharmaceuticals`, `technology`), and document names are in the field `title` which should use a fuzzy match. Ignore fields which cannot be determined from the natural language query context: ' + ctx.content", | ||
}, | ||
}, | ||
{ | ||
inference: { | ||
model_id: "openai_chat_completions", | ||
input_output: { | ||
input_field: "prompt", | ||
output_field: "query", | ||
}, | ||
}, | ||
}, | ||
{ | ||
remove: { | ||
field: "prompt", | ||
}, | ||
}, | ||
], | ||
}); | ||
console.log(response); | ||
---- |
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24 changes: 24 additions & 0 deletions
24
docs/doc_examples/d29031409016b2b798148ef173a196ae.asciidoc
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Original file line number | Diff line number | Diff line change |
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// 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: "test-index", | ||
query: { | ||
semantic: { | ||
field: "my_semantic_field", | ||
}, | ||
}, | ||
highlight: { | ||
fields: { | ||
my_semantic_field: { | ||
type: "semantic", | ||
number_of_fragments: 2, | ||
order: "score", | ||
}, | ||
}, | ||
}, | ||
}); | ||
console.log(response); | ||
---- |
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