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Build search applications with vector, hybrid, and semantic search capabilities.
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npx skills add https://github.com/BEKO2210/Firstbrain --skill azure-search-documents-ts명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.
Security audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any project.
Ingeniero de Sistemas de Andru.ia. Diseña, redacta y despliega nuevas habilidades (skills) dentro del repositorio siguiendo el Estándar de Diamante.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | azure-search-documents-ts |
| description | Build search applications with vector, hybrid, and semantic search capabilities. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | cloud-infrastructure |
| category | azure |
| risk | unknown |
| source | community |
| tags | ["skill","cloud-infrastructure","azure","search","documents"] |
Build search applications with vector, hybrid, and semantic search capabilities.
npm install @azure/search-documents @azure/identity
AZURE_SEARCH_ENDPOINT=https://<service-name>.search.windows.net
AZURE_SEARCH_INDEX_NAME=my-index
AZURE_SEARCH_ADMIN_KEY=<admin-key> # Optional if using Entra ID
import { SearchClient, SearchIndexClient } from "@azure/search-documents";
import { DefaultAzureCredential } from "@azure/identity";
const endpoint = process.env.AZURE_SEARCH_ENDPOINT!;
const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;
const credential = new DefaultAzureCredential();
// For searching
const searchClient = new SearchClient(endpoint, indexName, credential);
// For index management
const indexClient = new SearchIndexClient(endpoint, credential);
import { SearchIndex, SearchField, VectorSearch } from "@azure/search-documents";
const index: SearchIndex = {
name: ,
: [
{ : , : , : },
{ : , : , : },
{ : , : , : },
{ : , : , : , : },
{
: ,
: ,
: ,
: ,
: ,
},
],
: {
: [
{ : , : },
],
: [
{ : , : },
],
},
};
indexClient.(index);
const documents = [
{ id: "1", title: "Widget", description: "A useful widget", category: "Tools", embedding: [...] },
{ id: "2", title: "Gadget", description: "A cool gadget", category: "Electronics", embedding: [...] },
];
const result = await searchClient.uploadDocuments(documents);
console.log(`Indexed ${result.results.length} documents`);
const results = await searchClient.search("widget", {
select: ["id", "title", "description"],
filter: "category eq 'Tools'",
orderBy: ["title asc"],
top: 10,
});
for await (const result of results.results) {
console.log(`${result.document.title}: ${result.score}`);
}
const queryVector = await getEmbedding("useful tool"); // Your embedding function
const results = await searchClient.search("*", {
vectorSearchOptions: {
queries: [
{
kind: "vector",
vector: queryVector,
fields: ["embedding"],
kNearestNeighborsCount: 10,
},
],
},
select: ["id", "title", "description"],
});
for await (const result of results.results) {
console.log(`${result.document.title}: ${result.score}`);
}
const queryVector = await getEmbedding("useful tool");
const results = await searchClient.search("tool", {
vectorSearchOptions: {
queries: [
{
kind: "vector",
vector: queryVector,
fields: ["embedding"],
kNearestNeighborsCount: 50,
},
],
},
select: ["id", "title", "description"],
top: 10,
});
// Index must have semantic configuration
const index: SearchIndex = {
name: "products",
fields: [...],
semanticSearch: {
configurations: [
{
name: "semantic-config",
prioritizedFields: {
titleField: { name: "title" },
contentFields: [{ name: "description" }],
},
},
],
},
};
// Search with semantic ranking
const results = await searchClient.search("best tool for the job", {
queryType: "semantic",
semanticSearchOptions: {
configurationName: "semantic-config",
captions: { captionType: "extractive" },
answers: { answerType: "extractive", count: 3 },
},
select: ["id", "title", "description"],
});
for await (const result of results.results) {
console.log(`${result.document.title}`);
console.log(` Caption: ${result.captions?.[0]?.text}`);
console.log(` Reranker Score: ${result.rerankerScore}`);
}
// Filter syntax
const results = await searchClient.search("*", {
filter: "category eq 'Electronics' and price lt 100",
facets: ["category,count:10", "brand"],
});
// Access facets
for (const [facetName, facetResults] of Object.entries(results.facets || {})) {
console.log(`${facetName}:`);
for (const facet of facetResults) {
console.log(` ${facet.value}: ${facet.count}`);
}
}
// Create suggester in index
const index: SearchIndex = {
name: "products",
fields: [...],
suggesters: [
{ name: "sg", sourceFields: ["title", "description"] },
],
};
// Autocomplete
const autocomplete = await searchClient.autocomplete("wid", "sg", {
mode: "twoTerms",
top: 5,
});
// Suggestions
const suggestions = await searchClient.suggest("wid", "sg", {
select: ["title"],
top: 5,
});
// Batch upload, merge, delete
const batch = [
{ upload: { id: "1", title: "New Item" } },
{ merge: { id: "2", title: "Updated Title" } },
{ delete: { id: "3" } },
];
const result = await searchClient.indexDocuments({ actions: batch });
import {
SearchClient,
SearchIndexClient,
SearchIndexerClient,
SearchIndex,
SearchField,
SearchOptions,
VectorSearch,
SemanticSearch,
SearchIterator,
} from "@azure/search-documents";
uploadDocuments with arrays, not single docsmergeOrUploadDocuments for updatesincludeTotalCount: true sparingly in productionThis skill is applicable to execute the workflow or actions described in the overview.