azure-search-documents-ts

# Azure AI Search SDK for TypeScript Build search applications with vector, hybrid, and semantic search capabilities. ## Installation ```bash npm install @azure/search-documents @azure/identity ``` ## Environment Variables ```bash 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 ``` ## Authentication ```typescript 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); ``` ## Core Workflow ### Create Index with Vector Field ```typescript import { SearchIndex, SearchField, VectorSearch } from "@azure/search-documents"; const index: SearchIndex = { name: "products", fields: [ { name: "id", type: "Edm.String", key: true }, { name: "title", type: "Edm.String", searchable: true }, { name: "description", type: "Edm.String", searchable: true }, { name: "category", type: "Edm.String", filterable: true, facetable: true }, { name: "embedding", type: "Collection(Edm.Single)", searchable: true, vectorSearchDimensions: 1536, vectorSearchProfileName: "vector-profile", }, ], vectorSearch: { algorithms: [ { name: "hnsw-algorithm", kind: "hnsw" }, ], profiles: [ { name: "vector-profile", algorithmConfigurationName: "hnsw-algorithm" }, ], }, }; await indexClient.createOrUpdateIndex(index); ``` ### Index Documents ```typescript 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 ${result.results.length} documents`); ``` ### Full-Text Search ```typescript const results = await searchClient.search("widget", { select: ["id", "title", "description"], filter: "category eq 'Tools'", orderBy: ["title asc"], top: 10, }); for await (const result of results.results) { console.log(`${result.document.title}: ${result.score}`); } ``` ### Vector Search ```typescript const queryVector = await getEmbedding("useful tool"); // Your embedding function const results = await searchClient.search("*", { vectorSearchOptions: { queries: [ { kind: "vector", vector: queryVector, fields: ["embedding"], kNearestNeighborsCount: 10, }, ], }, select: ["id", "title", "description"], }); for await (const result of results.results) { console.log(`${result.document.title}: ${result.score}`); } ``` ### Hybrid Search (Text + Vector) ```typescript const queryVector = await getEmbedding("useful tool"); const results = await searchClient.search("tool", { vectorSearchOptions: { queries: [ { kind: "vector", vector: queryVector, fields: ["embedding"], kNearestNeighborsCount: 50, }, ], }, select: ["id", "title", "description"], top: 10, }); ``` ### Semantic Search ```typescript // Index must have semantic configuration const index: SearchIndex = { name: "products", fields: [...], semanticSearch: { configurations: [ { name: "semantic-config", prioritizedFields: { titleField: { name: "title" }, contentFields: [{ name: "description" }], }, }, ], }, }; // Search with semantic ranking const results = await searchClient.search("best tool for the job", { queryType: "semantic", semanticSearchOptions: { configurationName: "semantic-config", captions: { captionType: "extractive" }, answers: { answerType: "extractive", count: 3 }, }, select: ["id", "title", "description"], }); for await (const result of results.results) { console.log(`${result.document.title}`); console.log(` Caption: ${result.captions?.[0]?.text}`); console.log(` Reranker Score: ${result.rerankerScore}`); } ``` ## Filtering and Facets ```typescript // Filter syntax const results = await searchClient.search("*", { filter: "category eq 'Electronics' and price lt 100", facets: ["category,count:10", "brand"], }); // Access facets for (const [facetName, facetResults] of Object.entries(results.facets || {})) { console.log(`${facetName}:`); for (const facet of facetResults) { console.log(` ${facet.value}: ${facet.count}`); } } ``` ## Autocomplete and Suggestions ```typescript // 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 Operations ```typescript // 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 }); ``` ## Key Types ```typescript import { SearchClient, SearchIndexClient, SearchIndexerClient, SearchIndex, SearchField, SearchOptions, VectorSearch, SemanticSearch, SearchIterator, } from "@azure/search-documents"; ``` ## Best Practices 1. **Use hybrid search** - Combine vector + text for best results 2. **Enable semantic ranking** - Improves relevance for natural language queries 3. **Batch document uploads** - Use `uploadDocuments` with arrays, not single docs 4. **Use filters for security** - Implement document-level security with filters 5. **Index incrementally** - Use `mergeOrUploadDocuments` for updates 6. **Monitor query performance** - Use `includeTotalCount: true` sparingly in production ## When to Use Build search applications using Azure AI Search SDK for JavaScript (@azure/search-documents). Use when creating/managing indexes, implementing vector/hybrid search, semantic ranking, or building agentic retrieval with knowledge bases. Covers: Azure AI Search SDK for TypeScript, Environment Variables, Authentication, Create Index with Vector Field, Index Documents.