[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71RM7XQVFPKKC4CPACBJC0N":3,"skill-same:01M71RM7XQVFPKKC4CPACBJC0N":64},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":13,"repositoryHtmlUrl":47,"repositoryHomepageUrl":6,"ownerHtmlUrl":48,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":49,"install":50,"resources":63},"01M71RM7XQVFPKKC4CPACBJC0N","azure-search-documents-ts",null,"Build search applications using Azure AI Search SDK for JavaScript (@azure\u002Fsearch-documents). Use when creating\u002Fmanaging indexes, implementing vector\u002Fhybrid search, semantic ranking, or building ag...","javiertarazon\u002Fide-agente-free-jt7-","javiertarazon","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F153141207?v=4","01M71R52TB3S9HXKH6WSNT8A7V","01M71R52VG5KS2DKDDH4GDQ30A",0,"MIT","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fazure-search-documents-ts\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"search","search documents","azure","documents","azure search documents ts","sdk","azure ai","build","javascript","unknown","when","use","ranking","implementing","vector","applications","for","hybrid","community","Hybrid Search","2026-11-10 13:02:02",false,"---\nname: azure-search-documents-ts\ndescription: \"Build search applications using Azure AI Search SDK for JavaScript (@azure\u002Fsearch-documents). Use when creating\u002Fmanaging indexes, implementing vector\u002Fhybrid search, semantic ranking, or building ag...\"\npackage: \"@azure\u002Fsearch-documents\"\nrisk: unknown\nsource: community\n---\n\n# Azure AI Search SDK for TypeScript\n\nBuild search applications with vector, hybrid, and semantic search capabilities.\n\n## Installation\n\n```bash\nnpm install @azure\u002Fsearch-documents @azure\u002Fidentity\n```\n\n## Environment Variables\n\n```bash\nAZURE_SEARCH_ENDPOINT=https:\u002F\u002F\u003Cservice-name>.search.windows.net\nAZURE_SEARCH_INDEX_NAME=my-index\nAZURE_SEARCH_ADMIN_KEY=\u003Cadmin-key>  # Optional if using Entra ID\n```\n\n## Authentication\n\n```typescript\nimport { SearchClient, SearchIndexClient } from \"@azure\u002Fsearch-documents\";\nimport { DefaultAzureCredential } from \"@azure\u002Fidentity\";\n\nconst endpoint = process.env.AZURE_SEARCH_ENDPOINT!;\nconst indexName = process.env.AZURE_SEARCH_INDEX_NAME!;\nconst credential = new DefaultAzureCredential();\n\n\u002F\u002F For searching\nconst searchClient = new SearchClient(endpoint, indexName, credential);\n\n\u002F\u002F For index management\nconst indexClient = new SearchIndexClient(endpoint, credential);\n```\n\n## Core Workflow\n\n### Create Index with Vector Field\n\n```typescript\nimport { SearchIndex, SearchField, VectorSearch } from \"@azure\u002Fsearch-documents\";\n\nconst index: SearchIndex = {\n  name: \"products\",\n  fields: [\n    { name: \"id\", type: \"Edm.String\", key: true },\n    { name: \"title\", type: \"Edm.String\", searchable: true },\n    { name: \"description\", type: \"Edm.String\", searchable: true },\n    { name: \"category\", type: \"Edm.String\", filterable: true, facetable: true },\n    {\n      name: \"embedding\",\n      type: \"Collection(Edm.Single)\",\n      searchable: true,\n      vectorSearchDimensions: 1536,\n      vectorSearchProfileName: \"vector-profile\",\n    },\n  ],\n  vectorSearch: {\n    algorithms: [\n      { name: \"hnsw-algorithm\", kind: \"hnsw\" },\n    ],\n    profiles: [\n      { name: \"vector-profile\", algorithmConfigurationName: \"hnsw-algorithm\" },\n    ],\n  },\n};\n\nawait indexClient.createOrUpdateIndex(index);\n```\n\n### Index Documents\n\n```typescript\nconst documents = [\n  { id: \"1\", title: \"Widget\", description: \"A useful widget\", category: \"Tools\", embedding: [...] },\n  { id: \"2\", title: \"Gadget\", description: \"A cool gadget\", category: \"Electronics\", embedding: [...] },\n];\n\nconst result = await searchClient.uploadDocuments(documents);\nconsole.log(`Indexed ${result.results.length} documents`);\n```\n\n### Full-Text Search\n\n```typescript\nconst results = await searchClient.search(\"widget\", {\n  select: [\"id\", \"title\", \"description\"],\n  filter: \"category eq 'Tools'\",\n  orderBy: [\"title asc\"],\n  top: 10,\n});\n\nfor await (const result of results.results) {\n  console.log(`${result.document.title}: ${result.score}`);\n}\n```\n\n### Vector Search\n\n```typescript\nconst queryVector = await getEmbedding(\"useful tool\"); \u002F\u002F Your embedding function\n\nconst results = await searchClient.search(\"*\", {\n  vectorSearchOptions: {\n    queries: [\n      {\n        kind: \"vector\",\n        vector: queryVector,\n        fields: [\"embedding\"],\n        kNearestNeighborsCount: 10,\n      },\n    ],\n  },\n  select: [\"id\", \"title\", \"description\"],\n});\n\nfor await (const result of results.results) {\n  console.log(`${result.document.title}: ${result.score}`);\n}\n```\n\n### Hybrid Search (Text + Vector)\n\n```typescript\nconst queryVector = await getEmbedding(\"useful tool\");\n\nconst results = await searchClient.search(\"tool\", {\n  vectorSearchOptions: {\n    queries: [\n      {\n        kind: \"vector\",\n        vector: queryVector,\n        fields: [\"embedding\"],\n        kNearestNeighborsCount: 50,\n      },\n    ],\n  },\n  select: [\"id\", \"title\", \"description\"],\n  top: 10,\n});\n```\n\n### Semantic Search\n\n```typescript\n\u002F\u002F Index must have semantic configuration\nconst index: SearchIndex = {\n  name: \"products\",\n  fields: [...],\n  semanticSearch: {\n    configurations: [\n      {\n        name: \"semantic-config\",\n        prioritizedFields: {\n          titleField: { name: \"title\" },\n          contentFields: [{ name: \"description\" }],\n        },\n      },\n    ],\n  },\n};\n\n\u002F\u002F Search with semantic ranking\nconst results = await searchClient.search(\"best tool for the job\", {\n  queryType: \"semantic\",\n  semanticSearchOptions: {\n    configurationName: \"semantic-config\",\n    captions: { captionType: \"extractive\" },\n    answers: { answerType: \"extractive\", count: 3 },\n  },\n  select: [\"id\", \"title\", \"description\"],\n});\n\nfor await (const result of results.results) {\n  console.log(`${result.document.title}`);\n  console.log(`  Caption: ${result.captions?.[0]?.text}`);\n  console.log(`  Reranker Score: ${result.rerankerScore}`);\n}\n```\n\n## Filtering and Facets\n\n```typescript\n\u002F\u002F Filter syntax\nconst results = await searchClient.search(\"*\", {\n  filter: \"category eq 'Electronics' and price lt 100\",\n  facets: [\"category,count:10\", \"brand\"],\n});\n\n\u002F\u002F Access facets\nfor (const [facetName, facetResults] of Object.entries(results.facets || {})) {\n  console.log(`${facetName}:`);\n  for (const facet of facetResults) {\n    console.log(`  ${facet.value}: ${facet.count}`);\n  }\n}\n```\n\n## Autocomplete and Suggestions\n\n```typescript\n\u002F\u002F Create suggester in index\nconst index: SearchIndex = {\n  name: \"products\",\n  fields: [...],\n  suggesters: [\n    { name: \"sg\", sourceFields: [\"title\", \"description\"] },\n  ],\n};\n\n\u002F\u002F Autocomplete\nconst autocomplete = await searchClient.autocomplete(\"wid\", \"sg\", {\n  mode: \"twoTerms\",\n  top: 5,\n});\n\n\u002F\u002F Suggestions\nconst suggestions = await searchClient.suggest(\"wid\", \"sg\", {\n  select: [\"title\"],\n  top: 5,\n});\n```\n\n## Batch Operations\n\n```typescript\n\u002F\u002F Batch upload, merge, delete\nconst batch = [\n  { upload: { id: \"1\", title: \"New Item\" } },\n  { merge: { id: \"2\", title: \"Updated Title\" } },\n  { delete: { id: \"3\" } },\n];\n\nconst result = await searchClient.indexDocuments({ actions: batch });\n```\n\n## Key Types\n\n```typescript\nimport {\n  SearchClient,\n  SearchIndexClient,\n  SearchIndexerClient,\n  SearchIndex,\n  SearchField,\n  SearchOptions,\n  VectorSearch,\n  SemanticSearch,\n  SearchIterator,\n} from \"@azure\u002Fsearch-documents\";\n```\n\n## Best Practices\n\n1. **Use hybrid search** - Combine vector + text for best results\n2. **Enable semantic ranking** - Improves relevance for natural language queries\n3. **Batch document uploads** - Use `uploadDocuments` with arrays, not single docs\n4. **Use filters for security** - Implement document-level security with filters\n5. **Index incrementally** - Use `mergeOrUploadDocuments` for updates\n6. **Monitor query performance** - Use `includeTotalCount: true` sparingly in production\n\n## When to Use\nThis skill is applicable to execute the workflow or actions described in the overview.\n","# Azure AI Search SDK for TypeScript\n\nBuild search applications with vector, hybrid, and semantic search capabilities.\n\n## Installation\n\n```bash\nnpm install @azure\u002Fsearch-documents @azure\u002Fidentity\n```\n\n## Environment Variables\n\n```bash\nAZURE_SEARCH_ENDPOINT=https:\u002F\u002F\u003Cservice-name>.search.windows.net\nAZURE_SEARCH_INDEX_NAME=my-index\nAZURE_SEARCH_ADMIN_KEY=\u003Cadmin-key>  # Optional if using Entra ID\n```\n\n## Authentication\n\n```typescript\nimport { SearchClient, SearchIndexClient } from \"@azure\u002Fsearch-documents\";\nimport { DefaultAzureCredential } from \"@azure\u002Fidentity\";\n\nconst endpoint = process.env.AZURE_SEARCH_ENDPOINT!;\nconst indexName = process.env.AZURE_SEARCH_INDEX_NAME!;\nconst credential = new DefaultAzureCredential();\n\n\u002F\u002F For searching\nconst searchClient = new SearchClient(endpoint, indexName, credential);\n\n\u002F\u002F For index management\nconst indexClient = new SearchIndexClient(endpoint, credential);\n```\n\n## Core Workflow\n\n### Create Index with Vector Field\n\n```typescript\nimport { SearchIndex, SearchField, VectorSearch } from \"@azure\u002Fsearch-documents\";\n\nconst index: SearchIndex = {\n  name: \"products\",\n  fields: [\n    { name: \"id\", type: \"Edm.String\", key: true },\n    { name: \"title\", type: \"Edm.String\", searchable: true },\n    { name: \"description\", type: \"Edm.String\", searchable: true },\n    { name: \"category\", type: \"Edm.String\", filterable: true, facetable: true },\n    {\n      name: \"embedding\",\n      type: \"Collection(Edm.Single)\",\n      searchable: true,\n      vectorSearchDimensions: 1536,\n      vectorSearchProfileName: \"vector-profile\",\n    },\n  ],\n  vectorSearch: {\n    algorithms: [\n      { name: \"hnsw-algorithm\", kind: \"hnsw\" },\n    ],\n    profiles: [\n      { name: \"vector-profile\", algorithmConfigurationName: \"hnsw-algorithm\" },\n    ],\n  },\n};\n\nawait indexClient.createOrUpdateIndex(index);\n```\n\n### Index Documents\n\n```typescript\nconst documents = [\n  { id: \"1\", title: \"Widget\", description: \"A useful widget\", category: \"Tools\", embedding: [...] },\n  { id: \"2\", title: \"Gadget\", description: \"A cool gadget\", category: \"Electronics\", embedding: [...] },\n];\n\nconst result = await searchClient.uploadDocuments(documents);\nconsole.log(`Indexed ${result.results.length} documents`);\n```\n\n### Full-Text Search\n\n```typescript\nconst results = await searchClient.search(\"widget\", {\n  select: [\"id\", \"title\", \"description\"],\n  filter: \"category eq 'Tools'\",\n  orderBy: [\"title asc\"],\n  top: 10,\n});\n\nfor await (const result of results.results) {\n  console.log(`${result.document.title}: ${result.score}`);\n}\n```\n\n### Vector Search\n\n```typescript\nconst queryVector = await getEmbedding(\"useful tool\"); \u002F\u002F Your embedding function\n\nconst results = await searchClient.search(\"*\", {\n  vectorSearchOptions: {\n    queries: [\n      {\n        kind: \"vector\",\n        vector: queryVector,\n        fields: [\"embedding\"],\n        kNearestNeighborsCount: 10,\n      },\n    ],\n  },\n  select: [\"id\", \"title\", \"description\"],\n});\n\nfor await (const result of results.results) {\n  console.log(`${result.document.title}: ${result.score}`);\n}\n```\n\n### Hybrid Search (Text + Vector)\n\n```typescript\nconst queryVector = await getEmbedding(\"useful tool\");\n\nconst results = await searchClient.search(\"tool\", {\n  vectorSearchOptions: {\n    queries: [\n      {\n        kind: \"vector\",\n        vector: queryVector,\n        fields: [\"embedding\"],\n        kNearestNeighborsCount: 50,\n      },\n    ],\n  },\n  select: [\"id\", \"title\", \"description\"],\n  top: 10,\n});\n```\n\n### Semantic Search\n\n```typescript\n\u002F\u002F Index must have semantic configuration\nconst index: SearchIndex = {\n  name: \"products\",\n  fields: [...],\n  semanticSearch: {\n    configurations: [\n      {\n        name: \"semantic-config\",\n        prioritizedFields: {\n          titleField: { name: \"title\" },\n          contentFields: [{ name: \"description\" }],\n        },\n      },\n    ],\n  },\n};\n\n\u002F\u002F Search with semantic ranking\nconst results = await searchClient.search(\"best tool for the job\", {\n  queryType: \"semantic\",\n  semanticSearchOptions: {\n    configurationName: \"semantic-config\",\n    captions: { captionType: \"extractive\" },\n    answers: { answerType: \"extractive\", count: 3 },\n  },\n  select: [\"id\", \"title\", \"description\"],\n});\n\nfor await (const result of results.results) {\n  console.log(`${result.document.title}`);\n  console.log(`  Caption: ${result.captions?.[0]?.text}`);\n  console.log(`  Reranker Score: ${result.rerankerScore}`);\n}\n```\n\n## Filtering and Facets\n\n```typescript\n\u002F\u002F Filter syntax\nconst results = await searchClient.search(\"*\", {\n  filter: \"category eq 'Electronics' and price lt 100\",\n  facets: [\"category,count:10\", \"brand\"],\n});\n\n\u002F\u002F Access facets\nfor (const [facetName, facetResults] of Object.entries(results.facets || {})) {\n  console.log(`${facetName}:`);\n  for (const facet of facetResults) {\n    console.log(`  ${facet.value}: ${facet.count}`);\n  }\n}\n```\n\n## Autocomplete and Suggestions\n\n```typescript\n\u002F\u002F Create suggester in index\nconst index: SearchIndex = {\n  name: \"products\",\n  fields: [...],\n  suggesters: [\n    { name: \"sg\", sourceFields: [\"title\", \"description\"] },\n  ],\n};\n\n\u002F\u002F Autocomplete\nconst autocomplete = await searchClient.autocomplete(\"wid\", \"sg\", {\n  mode: \"twoTerms\",\n  top: 5,\n});\n\n\u002F\u002F Suggestions\nconst suggestions = await searchClient.suggest(\"wid\", \"sg\", {\n  select: [\"title\"],\n  top: 5,\n});\n```\n\n## Batch Operations\n\n```typescript\n\u002F\u002F Batch upload, merge, delete\nconst batch = [\n  { upload: { id: \"1\", title: \"New Item\" } },\n  { merge: { id: \"2\", title: \"Updated Title\" } },\n  { delete: { id: \"3\" } },\n];\n\nconst result = await searchClient.indexDocuments({ actions: batch });\n```\n\n## Key Types\n\n```typescript\nimport {\n  SearchClient,\n  SearchIndexClient,\n  SearchIndexerClient,\n  SearchIndex,\n  SearchField,\n  SearchOptions,\n  VectorSearch,\n  SemanticSearch,\n  SearchIterator,\n} from \"@azure\u002Fsearch-documents\";\n```\n\n## Best Practices\n\n1. **Use hybrid search** - Combine vector + text for best results\n2. **Enable semantic ranking** - Improves relevance for natural language queries\n3. **Batch document uploads** - Use `uploadDocuments` with arrays, not single docs\n4. **Use filters for security** - Implement document-level security with filters\n5. **Index incrementally** - Use `mergeOrUploadDocuments` for updates\n6. **Monitor query performance** - Use `includeTotalCount: true` sparingly in production\n\n## When to Use\nThis skill is applicable to execute the workflow or actions described in the overview.\n",{"name":5,"risk":27,"source":36,"package":43,"description":7},"@azure\u002Fsearch-documents","release\u002Fv4.2.11-panel-pro","a92ea02bcf7e2b3dbaf46d49696c29a3cb490d4f",".github\u002Fskills\u002Fazure-search-documents-ts","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-","https:\u002F\u002Fgithub.com\u002Fjaviertarazon","2026-07-12 07:10:45",{"source":8,"skillName":5,"commands":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add javiertarazon\u002Fide-agente-free-jt7- --skill azure-search-documents-ts",{"tool":56,"command":57},"bunx","bunx skills add javiertarazon\u002Fide-agente-free-jt7- --skill 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