Azure AI Search SDK for TypeScript Build search applications with vector, hybrid, and semantic search capabilities. Installation npm install @azure/search-documents @azure/identity Environment Variables 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 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 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 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 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 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) 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 // 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 // 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 // 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 // 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 import { SearchClient , SearchIndexClient , SearchIndexerClient , SearchIndex , SearchField , SearchOptions , VectorSearch , SemanticSearch , SearchIterator , } from "@azure/search-documents" ; Best Practices Use hybrid search - Combine vector + text for best results Enable semantic ranking - Improves relevance for natural language queries Batch document uploads - Use uploadDocuments with arrays, not single docs Use filters for security - Implement document-level security with filters Index incrementally - Use mergeOrUploadDocuments for updates Monitor query performance - Use includeTotalCount: true sparingly in production When to Use This skill is applicable to execute the workflow or actions described in the overview.