{"id":1213,"date":"2025-09-17T01:00:51","date_gmt":"2025-09-17T08:00:51","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/foundry\/?p=1213"},"modified":"2025-09-19T03:42:59","modified_gmt":"2025-09-19T10:42:59","slug":"ground-your-agents-faster-native-azure-ai-search-indexing-foundry","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/foundry\/ground-your-agents-faster-native-azure-ai-search-indexing-foundry\/","title":{"rendered":"Ground Your Agents Faster with Native Azure AI Search Indexing in Foundry"},"content":{"rendered":"<h2>TL;DR<\/h2>\n<p><span data-contrast=\"auto\">Azure AI Foundry now lets you ingest data directly from Azure Blob Storage, ADLS Gen2, or Microsoft OneLake and create an Azure AI Search index in just one click.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">When you create an agent in Azure AI Foundry one of the most powerful steps is \u201cAdd knowledge\u201d\u2014grounding your agent with your enterprise data so it can answer questions and act with context.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Previously, this required you to bring an existing Azure AI Search index and configure it before you could connect your data. That meant extra setup steps and more friction, especially if you were just experimenting.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Today, we\u2019re making this much simpler.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h2>Why This Matters<\/h2>\n<p>Grounding (a.k.a. retrieval augmentation) is one of the highest\u2011leverage steps in agent development. But the traditional workflow\u2014provision a search service, design an index, run an ingestion pipeline, create skillsets, then wire it to your agent\u2014adds friction when you simply want to test a hypothesis or enable a new scenario.<\/p>\n<p>Now you can collapse that entire path into a single, integrated flow inside Azure AI Foundry. You focus on: (1) choosing a data source, (2) selecting an embedding model, and (3) clicking create. Foundry orchestrates ingestion, chunking, embedding, and vector index creation for you.<\/p>\n<h2>What\u2019s New<\/h2>\n<p>You can now natively create an <strong>Azure AI Search vector index<\/strong> inside Foundry during the &#8220;Add knowledge&#8221; step of agent creation or editing.<\/p>\n<h3>Supported data sources (initial wave)<\/h3>\n<ul>\n<li>Azure Blob Storage<\/li>\n<li>Azure Data Lake Storage (ADLS) Gen2<\/li>\n<li>Microsoft OneLake (Fabric)<\/li>\n<\/ul>\n<h3>Key capabilities<\/h3>\n<table>\n<thead>\n<tr>\n<th>Capability<\/th>\n<th>Description<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Inline index creation<\/td>\n<td>No pre-existing Search index required.<\/td>\n<\/tr>\n<tr>\n<td>Automatic ingestion<\/td>\n<td>Content is pulled, chunked, and prepared for embeddings.<\/td>\n<\/tr>\n<tr>\n<td>Embedding model selection<\/td>\n<td>Choose from supported embedding models at creation time.<\/td>\n<\/tr>\n<tr>\n<td>Hybrid-ready<\/td>\n<td>Index configured for combined vector + keyword retrieval.<\/td>\n<\/tr>\n<tr>\n<td>Secure by design<\/td>\n<td>Respects Azure RBAC &amp; network isolation of underlying resources.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>How It Works<\/h2>\n<ol>\n<li>Open (or create) an agent in Azure AI Foundry.<\/li>\n<li>Select <strong>Add knowledge<\/strong>.<\/li>\n<li>Choose a supported data source (Blob \/ ADLS Gen2 \/ OneLake).<\/li>\n<li>Authorize the connection (if first time) and pick containers \/ paths.<\/li>\n<li>Select an Azure OpenAI embedding model (e.g., <code>text-embedding-*<\/code>).<\/li>\n<li>Click <strong>Create index &amp; ingest<\/strong>.<\/li>\n<li>Foundry: pulls content \u2192 chunks documents \u2192 generates embeddings \u2192 provisions (or reuses) an Azure AI Search index optimized for hybrid queries.<\/li>\n<li>Your agent can now answer grounded questions immediately.<\/li>\n<\/ol>\n<p><img decoding=\"async\" src=\"https:\/\/devblogs.microsoft.com\/foundry\/wp-content\/uploads\/sites\/89\/2025\/09\/azs-mini-wizard.gif\" alt=\"Animated walkthrough \u2013 creating an index from Blob storage during Add knowledge flow\" \/><\/p>\n<p><em>No separate indexing pipeline. No manual schema definition. No script to run. Just connect data and go.<\/em><\/p>\n<h2>Try It Today<\/h2>\n<p>Get started by our tutorial on <a href=\"https:\/\/learn.microsoft.com\/azure\/ai-foundry\/agents\/how-to\/tools\/azure-ai-search?tabs=azurecli\">How to create an Azure AI Search index in Foundry<\/a>.<\/p>\n<h2>Related Resources<\/h2>\n<ul>\n<li><a href=\"https:\/\/learn.microsoft.com\/azure\/search\/search-what-is-azure-search\">Azure AI Search Concepts<\/a><\/li>\n<li><a href=\"https:\/\/learn.microsoft.com\/azure\/search\/hybrid-search-overview\">Hybrid Retrieval Overview<\/a><\/li>\n<li><a href=\"https:\/\/learn.microsoft.com\/azure\/ai-foundry\/openai\/concepts\/models\">Embeddings Models in Foundry<\/a><\/li>\n<li><a href=\"https:\/\/techcommunity.microsoft.com\/blog\/azure-ai-foundry-blog\/agentic-retrieval-updates-in-azure-ai-search\/4450621\">Latest Agentic Retrieval (preview) in Azure AI Search<\/a><\/li>\n<\/ul>\n<hr \/>\n<p>Happy grounding\u2014can\u2019t wait to see what you build. Share launches with <strong>#AzureAIFoundry<\/strong>!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Instantly create a production-ready Azure AI Search vector index directly inside Azure AI Foundry when grounding your agent\u2014no prior search setup required.<\/p>\n","protected":false},"author":170596,"featured_media":1218,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[25,3,13,2,63,62],"class_list":["post-1213","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-microsoft-foundry","tag-agents","tag-ai-development","tag-azure-ai-search","tag-microsoft-foundry","tag-retrieval-augmented-generation","tag-vector-search"],"acf":[],"blog_post_summary":"<p>Instantly create a production-ready Azure AI Search vector index directly inside Azure AI Foundry when grounding your agent\u2014no prior search setup required.<\/p>\n","_links":{"self":[{"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/posts\/1213","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/users\/170596"}],"replies":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/comments?post=1213"}],"version-history":[{"count":0,"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/posts\/1213\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/media\/1218"}],"wp:attachment":[{"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/media?parent=1213"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/categories?post=1213"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/foundry\/wp-json\/wp\/v2\/tags?post=1213"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}