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- Introduction to Azure AI Search - Azure AI Search | Microsoft Learn
Azure AI Search is an AI-powered information retrieval platform, helps developers build rich search experiences and generative AI apps that combine large language models with enterprise data
- Azure AI Search FAQ - Azure AI Search | Microsoft Learn
How do I work with Azure AI Search? The primary workflow is create, load, and query an index Although you can use the Azure portal for most tasks, Azure AI Search is intended to be used programmatically, handling requests from client code
- Indexer overview - Azure AI Search | Microsoft Learn
An indexer in Azure AI Search is a crawler that extracts textual data from cloud data sources and populates a search index using field-to-field mappings between source data and a search index This approach is sometimes referred to as a 'pull model' because the search service pulls data in without you having to write any code that adds data to an index Indexers also drive skillset execution
- Azure Cosmos DB NoSQL indexer - Azure AI Search
To work through the examples in this article, you need the Azure portal or a REST client If you're using Azure portal, make sure that access to all public networks is enabled Other approaches for creating a Cosmos DB indexer include Azure SDKs
- Vector Search - Azure AI Search | Microsoft Learn
The search engine can process the filter before or after executing the vector query Vector database Azure AI Search stores the data that you query over Use it as a pure vector index when you need long-term memory or a knowledge base, grounding data for the retrieval-augmented generation (RAG) architecture, or an app that uses vectors
- Plan and Manage Costs - Azure AI Search | Microsoft Learn
This article explains how Azure AI Search is billed, including fixed and variable costs, and provides guidance for cost management Before you create a search service, use the Azure pricing calculator to estimate costs based on your planned capacity and features Another resource is a capacity-planning worksheet that models your expected index size, indexing throughput, and indexing costs As
- Quickstart: Search Explorer Query Tool - Azure AI Search
In this quickstart, you learn how to use Search explorer, a built-in query tool in the Azure portal for running queries against an Azure AI Search index Use this tool to test a query or filter expression or to confirm whether content exists in the index This quickstart uses an existing index to demonstrate Search explorer
- Chunk documents - Azure AI Search | Microsoft Learn
Azure AI Search has built-in solutions for chunking content, and also for vectorizing chunked content if you're using vector search The built-in approach takes a dependency on built-in indexers and skillsets that enable text splitting and embeddings generation
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