القائمة
Build search applications with vector, hybrid, and semantic search capabilities.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/BEKO2210/Firstbrain --skill azure-search-documents-tsيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
| 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.
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