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azure-search-documents-ts

Creates and manages Azure AI Search indexes with vector, hybrid, and semantic retrieval using TypeScript patterns.

Updated Apr 12, 2026
Search Tags:#typescript#embeddings#azure#semantic-search#vector-search#hybrid-search#search-indexing
One-click install
npx skills add https://github.com/davidrrowley/CortexYouV3 --skill azure-search-documents-ts
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: azure-search-documents-ts
Source: https://github.com/davidrrowley/CortexYouV3/tree/main/.agents/skills/azure-search-documents-ts
Command: npx skills add https://github.com/davidrrowley/CortexYouV3 --skill azure-search-documents-ts

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill gives clear, practical TypeScript patterns and operational guidance for building and managing Azure AI Search indexes, ingesting embeddings, and implementing vector, hybrid, and semantic search so teams can deliver relevant retrieval and QA features faster and with fewer integration errors.

Core Features & Use Cases

  • Index creation & management: Examples for creating vector-enabled indexes, semantic configurations, and HNSW algorithm settings.
  • Document ingestion & batching: Patterns for uploading, merging, and deleting documents including embeddings and incremental updates.
  • Retrieval modes: Ready-to-use approaches for vector search, hybrid (text + vector) search, semantic ranking with captions and answers, autocomplete, facets, and filtering.
  • Use cases: Building knowledge bases, product search with embeddings, semantic Q&A over document corpora, and hybrid recommender systems.

Quick Start

Use the azure-search-documents-ts skill to create a vector-capable index, upload documents with embeddings, and run a hybrid semantic plus vector search against your AZURE_SEARCH_ENDPOINT and index.

Frequently Asked Questions about azure-search-documents-ts

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement vector search in TypeScript using Azure AI Search?▼

You can implement vector search in TypeScript by creating vector-enabled indexes with HNSW algorithm settings and uploading documents with embeddings. This Skill provides patterns for configuring vectorSearch options and running vector queries using a typed SearchClient.

How do I configure semantic ranking for Azure AI Search indexes in TypeScript?▼

To configure semantic ranking in Azure AI Search, define semantic configurations in your index schema and enable semantic reranking. This Skill provides TypeScript patterns for semantic ranking, captions, and answers, plus hybrid text and vector retrieval modes.

Does Azure AI Search support hybrid search and autocomplete in TypeScript?▼

Yes, Azure AI Search supports hybrid search and autocomplete in TypeScript. This Skill provides ready-to-use approaches for hybrid text plus vector search, semantic ranking, autocomplete, facets, and filtering using a typed SearchClient.

Can I use DefaultAzureCredential and AzureKeyCredential for Azure AI Search authentication in TypeScript?▼

Yes, you can use DefaultAzureCredential or AzureKeyCredential for Azure AI Search authentication in TypeScript. This Skill includes patterns for secure authentication, error handling practices, and batch document ingestion including incremental updates.

What is the best way to batch upload embeddings to an Azure AI Search index in TypeScript?▼

The best way to batch upload embeddings is using document ingestion and batching patterns for uploading, merging, and deleting documents. This Skill provides TypeScript examples for incremental updates and managing Azure AI Search indexes with vector capabilities.

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