{"id":4561,"date":"2025-05-29T10:16:41","date_gmt":"2025-05-29T17:16:41","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/azure-sql\/?p=4561"},"modified":"2025-06-19T09:00:27","modified_gmt":"2025-06-19T16:00:27","slug":"4561-2","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/azure-sql\/4561-2\/","title":{"rendered":"Vector Support Public Preview now extended to Azure SQL MI"},"content":{"rendered":"<p>We are thrilled to announce that <a href=\"https:\/\/learn.microsoft.com\/azure\/azure-sql\/managed-instance\/sql-managed-instance-paas-overview?view=azuresql\">Azure SQL Managed Instance<\/a> now supports Vector type and functions in public preview.\u00a0 This builds on the momentum from Azure SQL Database, where vector support has been in public preview since December. Check out the detailed <a href=\"https:\/\/devblogs.microsoft.com\/azure-sql\/exciting-announcement-public-preview-of-native-vector-support-in-azure-sql-database\/\">announcement<\/a><\/p>\n<h3>\ud83d\udca1 Why Choose Azure SQL Managed Instance for AI-Driven Modernization?<\/h3>\n<ul>\n<li><strong>Effortless Cloud Modernization<\/strong>\nSeamlessly migrate from on-premises, IaaS, or legacy environments with near 100% SQL Server compatibility\u2014no need to rearchitect applications or retrain teams.<\/li>\n<li><strong>AI-Ready SQL for Faster, Smarter App Development<\/strong>\nNative support for vector data types and functions enables direct storage and querying of embeddings, allowing developers to build intelligent, context-aware applications without external vector stores or complex infrastructure.<\/li>\n<li><strong>Fully Managed, Enterprise-Grade Platform<\/strong>\nEnjoy the scalability, security, and simplicity of a fully managed PaaS\u2014complete with built-in high availability, automated backups, advanced threat protection, and compliance with global standards.<\/li>\n<li><strong>Innovation-Ready Ecosystem<\/strong>\nDeep integration with Azure OpenAI, LangChain, and Semantic Kernel\u2014plus a free tier for development and POCs\u2014empowers teams to experiment, iterate, and innovate with confidence.<\/li>\n<\/ul>\n<h3>\ud83d\udd04 Quick Start: Use the Free Offer to Explore Vector Support in Azure SQL MI<\/h3>\n<p>Before we dive deeper, let\u2019s take a moment to explore how you can get hands-on experience with these capabilities\u2014at no cost. Azure offers a <a href=\"https:\/\/learn.microsoft.com\/azure\/azure-sql\/managed-instance\/free-offer?view=azuresql\">SQL Managed Instance free offer<\/a> , perfect for new customers exploring the platform or existing users needing a development environment for prototyping and proof-of-concept work.<\/p>\n<p>To take advantage of\u00a0<strong>native vector support<\/strong>, make sure your instance is configured with the <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/azure-sql\/managed-instance\/update-policy?view=azuresql&amp;tabs=azure-portal\" target=\"_blank\" rel=\"noopener\">\u201cAlways-up-to-date\u201d update policy<\/a>\u00a0, which ensures immediate access to the latest SQL engine features as they become available in Azure.<\/p>\n<p><a href=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2024\/08\/Screenshot-2024-08-22-125633.png\" data-featherlight=\"image\"><img decoding=\"async\" class=\"alignnone size-full wp-image-3545 lazyloaded\" src=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2024\/08\/Screenshot-2024-08-22-125633.png\" alt=\"Image Screenshot 2024 08 22 125633\" width=\"757\" height=\"415\" data-src=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2024\/08\/Screenshot-2024-08-22-125633.png\" srcset=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2024\/08\/Screenshot-2024-08-22-125633.png 757w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2024\/08\/Screenshot-2024-08-22-125633-300x164.png 300w\" sizes=\"(max-width: 757px) 100vw, 757px\" \/><\/a><\/p>\n<h3>\ud83d\udcd8 Practical Example: Using Azure SQL MI for Intelligent Recipe Discovery<\/h3>\n<p>To illustrate the power of vector type and functions, let&#8217;s consider a use case involving a recipe blogger. Imagine a blogger who wants to enhance their website&#8217;s search functionality by incorporating semantic search, hybrid search, and LLM augmentation. Here&#8217;s how they can achieve this using Managed Instance<\/p>\n<h3>\ud83e\udde0 Storing Embeddings with the dedicated VECTOR Data Type<\/h3>\n<p>SQL provides a dedicated <a href=\"https:\/\/learn.microsoft.com\/sql\/t-sql\/data-types\/vector-data-type?view=azuresqldb-current&amp;tabs=csharp-sample\">Vector data type<\/a> that simplifies the creation, storage, and querying of vector embeddings directly within a relational database<\/p>\n<p>In this use case, we generate text embeddings using the Azure OpenAI model\u00a0<strong><code>text-embedding-small<\/code><\/strong>. These embeddings are stored using the native\u00a0<strong><code>VECTOR<\/code><\/strong>\u00a0data type in Azure SQL Managed Instance. Specifically, we use a\u00a0<strong><code>vector(1536)<\/code><\/strong>\u00a0column in the\u00a0<code>Recipe<\/code>\u00a0table, as the model outputs embeddings with 1536 dimensions. To create rich, meaningful embeddings, we concatenate the\u00a0<strong>Title<\/strong>\u00a0and\u00a0<strong>Instructions<\/strong>\u00a0fields of each recipe\u2014capturing both the context and content in a single vector representation.<\/p>\n<p><a href=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordatatypemi.png\"><img decoding=\"async\" class=\"size-full wp-image-4562 aligncenter\" src=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordatatypemi.png\" alt=\"Vector embeddings stored in the table in the Vector Datatype\" width=\"1760\" height=\"422\" srcset=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordatatypemi.png 1760w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordatatypemi-300x72.png 300w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordatatypemi-1024x246.png 1024w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordatatypemi-768x184.png 768w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordatatypemi-1536x368.png 1536w\" sizes=\"(max-width: 1760px) 100vw, 1760px\" \/><\/a><\/p>\n<h3>\ud83d\udd0d Semantic Search with Vector Embeddings<\/h3>\n<p>Semantic search enables more meaningful and context-aware retrieval of data by leveraging vector embeddings that capture the\u00a0<strong>underlying semantic meaning<\/strong>\u00a0of text, rather than relying solely on keyword matching. This allows users to find recipes based on intent and content similarity.<\/p>\n<p>In this example, we use a stored procedure to generate an embedding for the user query\u2014<strong>&#8220;fruit-based desserts with dates&#8221;<\/strong>\u2014using the Azure OpenAI model. We then apply the built-in\u00a0<code>VECTOR_DISTANCE<\/code>\u00a0function to compare this embedding against those stored in the\u00a0<code>Recipe<\/code>\u00a0table, returning the most semantically relevant results.<\/p>\n<pre class=\"prettyprint language-sql\"><code class=\"language-sql\">-- Assuming you have a stored procedure to get embeddings for a given text\r\nDECLARE @e vector(1536)\r\nEXEC dbo.GET_EMBEDDINGS @model = 'text-embedding-3-small', @text = 'fruit based desserts with dates', @embedding = @e OUTPUT;\r\n\r\n-- Perform the semantic search\r\nSELECT TOP(10)\r\n\u00a0\u00a0\u00a0 ID,\r\n\u00a0\u00a0\u00a0 Title,\r\n\u00a0\u00a0\u00a0 Ingredients,\r\n\u00a0\u00a0\u00a0 Instructions,\r\n\u00a0\u00a0\u00a0 VECTOR_DISTANCE('cosine', @e, textEmbedding) AS Distance\r\nFROM Recipe\r\nORDER BY Distance;<\/code> Output:<\/pre>\n<p><a href=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordistancemi.png\"><img decoding=\"async\" class=\"aligncenter size-full wp-image-4564\" src=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordistancemi.png\" alt=\"vectordistancemi image\" width=\"1591\" height=\"694\" srcset=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordistancemi.png 1591w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordistancemi-300x131.png 300w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordistancemi-1024x447.png 1024w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordistancemi-768x335.png 768w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectordistancemi-1536x670.png 1536w\" sizes=\"(max-width: 1591px) 100vw, 1591px\" \/><\/a><\/p>\n<h3>\ud83e\udde0\u00a0 Combining Vector Similarity with SQL Filters<\/h3>\n<p>Azure SQL Managed Instance enables powerful search capabilities by allowing you to combine <strong>vector-based semantic search<\/strong>\u00a0with traditional\u00a0<strong>relational filters, joins, and aggregations<\/strong>\u2014all within a single query. This makes it possible to build rich, context-aware search experiences that go beyond simple keyword matching.<\/p>\n<p>For instance, to find\u00a0<strong>\u201cquick fix breakfast options\u201d<\/strong>\u00a0that meet specific ingredient and instruction criteria, you can use a query that blends vector similarity with SQL conditions for a more refined and relevant result set.<\/p>\n<pre class=\"prettyprint language-sql\"><code class=\"language-sql\">\r\n\r\n-- Declare the embedding for the search query\r\nDECLARE @searchEmbedding VECTOR(1536);\r\nEXEC dbo.GET_EMBEDDINGS @model = 'text-embedding-3-small', @text = 'Quick fix breakfast option', @embedding = @searchEmbedding OUTPUT;\r\n\r\n-- Comprehensive query with multiple filters\r\nSELECT TOP(10)\r\n    r.ID,\r\n    r.Title,\r\n    r.Ingredients,\r\n    r.Instructions,\r\n    VECTOR_DISTANCE('cosine', @searchEmbedding, r.textEmbedding) AS Distance,\r\n    CASE \r\n        WHEN LEN(r.Instructions) &gt; 200 THEN 'Detailed Instructions'\r\n        ELSE 'Short Instructions'\r\n    END AS InstructionLength,\r\n    CASE \r\n        WHEN CHARINDEX('breakfast', r.Title) &gt; 0 THEN 'Breakfast Recipe'\r\n        WHEN CHARINDEX('lunch', r.Title) &gt; 0 THEN 'Lunch Recipe'\r\n        ELSE 'Other Recipe'\r\n    END AS RecipeCategory\r\nFROM Recipe r\r\nWHERE\r\n    r.Title NOT LIKE '%dessert%' -- Exclude dessert recipes\r\n    AND LEN(r.Instructions) &gt; 50 -- Text length filter for detailed instructions\r\n    AND (r.Ingredients LIKE '%egg%' OR r.Ingredients LIKE '%milk%') -- Inclusion of specific ingredients\r\nORDER BY\r\n    Distance,  -- Order by distance\r\n    RecipeCategory DESC, -- Secondary order by recipe category\r\n    InstructionLength DESC; -- Tertiary order by instruction length<\/code><\/pre>\n<p>Output:<\/p>\n<h3><a href=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectorfilters-1.png\"><img decoding=\"async\" class=\"aligncenter size-full wp-image-4573\" src=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectorfilters-1.png\" alt=\"vectorfilters image\" width=\"1732\" height=\"289\" srcset=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectorfilters-1.png 1732w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectorfilters-1-300x50.png 300w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectorfilters-1-1024x171.png 1024w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectorfilters-1-768x128.png 768w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/vectorfilters-1-1536x256.png 1536w\" sizes=\"(max-width: 1732px) 100vw, 1732px\" \/><\/a><\/h3>\n<p>Note: SQL Managed Instance also supports Hybrid Search Via Full-Text Search . Read more <a href=\"https:\/\/devblogs.microsoft.com\/azure-sql\/enhancing-search-capabilities-in-sql-server-and-azure-sql-with-hybrid-search-and-rrf-re-ranking\/\">here<\/a><\/p>\n<h3>\ud83e\udd16 Augmenting LLMs with Vector-Based Context<\/h3>\n<p>In this use case, we\u00a0leverage embeddings retrieved from<strong> Azure SQL Managed Instance <\/strong>vector search to enrich the output of LLM models like <strong>GPT-4o<\/strong>\u00a0and\u00a0<strong>DALL\u00b7E3<\/strong>.<\/p>\n<p>By using the\u00a0<code>VECTOR_DISTANCE<\/code> function to retrieve semantically relevant recipes, we provide the LLM with meaningful context that enhances the quality and relevance of its responses. By passing the results of the vector distance function to the chat completions model, we can generate outputs in natural language, allowing users to interact with the system in a conversational manner.<\/p>\n<h3>\ud83e\uddd1\u200d\ud83c\udf73 Persona and Text-to-Image Generation<\/h3>\n<p>To make the interaction more engaging and memorable, we assign a\u00a0<strong>Gordon Ramsay persona<\/strong>\u00a0to the assistant using prompt engineering. This means the responses are not only informative but also delivered in a tone that reflects Ramsay\u2019s signature style\u2014<strong>direct, passionate, and occasionally cheeky<\/strong>, just like you&#8217;d expect from the world-renowned chef.<\/p>\n<p>For example, when a user says,\u00a0<em>\u201cHelp me make a dessert. I have a ton of coffee beans,\u201d<\/em>\u00a0the system:<\/p>\n<ul>\n<li>Uses vector search to retrieve relevant recipes from the database<\/li>\n<li>Feeds those results into\u00a0<strong>GPT-4o<\/strong>, which responds in Gordon Ramsay\u2019s voice\u2014perhaps saying something like,\u00a0<em>\u201cRight, let\u2019s not waste those beans\u2014how about a bold espresso mousse that\u2019ll knock your socks off?\u201d<\/em><\/li>\n<li>Uses\u00a0<strong>DALL\u00b7E 3<\/strong>\u00a0to generate a visual of the dessert or ingredients, adding a rich, visual layer to the interaction<\/li>\n<\/ul>\n<p>This fusion of\u00a0<strong>semantic retrieval<\/strong>,\u00a0<strong>persona-driven language generation<\/strong>, and\u00a0<strong>AI-generated imagery<\/strong>\u00a0creates a highly immersive and delightful user experience.<\/p>\n<p><img decoding=\"async\" class=\"aligncenter size-full wp-image-4565\" src=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/promptmi.png\" alt=\"promptmi image\" width=\"1726\" height=\"540\" srcset=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/promptmi.png 1726w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/promptmi-300x94.png 300w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/promptmi-1024x320.png 1024w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/promptmi-768x240.png 768w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/promptmi-1536x481.png 1536w\" sizes=\"(max-width: 1726px) 100vw, 1726px\" \/><\/p>\n<p>When the user asks &#8220;<em>Help me make a dessert. I have a ton of coffee beans<\/em>&#8221;\nThe GPT-4o model then generates detailed responses<\/p>\n<p><a href=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/ragmi.png\"><img decoding=\"async\" class=\"aligncenter size-full wp-image-4566\" src=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/ragmi.png\" alt=\"ragmi image\" width=\"1569\" height=\"790\" srcset=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/ragmi.png 1569w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/ragmi-300x151.png 300w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/ragmi-1024x516.png 1024w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/ragmi-768x387.png 768w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/ragmi-1536x773.png 1536w\" sizes=\"(max-width: 1569px) 100vw, 1569px\" \/><\/a><\/p>\n<p>and the <strong>DALL-E-3<\/strong> model creates images of the ingredients:<\/p>\n<p><a href=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/llmresponsemi2.png\"><img decoding=\"async\" class=\"aligncenter size-full wp-image-4567\" src=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/llmresponsemi2.png\" alt=\"llmresponsemi2 image\" width=\"1532\" height=\"697\" srcset=\"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/llmresponsemi2.png 1532w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/llmresponsemi2-300x136.png 300w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/llmresponsemi2-1024x466.png 1024w, https:\/\/devblogs.microsoft.com\/azure-sql\/wp-content\/uploads\/sites\/56\/2025\/04\/llmresponsemi2-768x349.png 768w\" sizes=\"(max-width: 1532px) 100vw, 1532px\" \/><\/a><\/p>\n<p>With native support for vector data types, seamless integration with large language models, and the ability to combine structured data with semantic search, SQL is no longer just a transactional engine\u2014it\u2019s a powerful AI-ready data platform built for modern, intelligent applications !<\/p>\n<h2 id=\"getting-started:\">Getting Started:<\/h2>\n<p>Ready to explore vector capabilities in Azure SQL Managed Instance? Here\u2019s everything you need to get hands-on:<\/p>\n<ul>\n<li>Take a look at the official documentation <a href=\"https:\/\/learn.microsoft.com\/en-us\/sql\/t-sql\/functions\/vector-functions-transact-sql?view=azuresqldb-current\" target=\"_blank\" rel=\"noopener\">here.<\/a><\/li>\n<li>You can also use this GitHub Repo full of samples:\u00a0<a href=\"https:\/\/github.com\/Azure-Samples\/azure-sql-db-vector-search\" target=\"_blank\" rel=\"noopener\">https:\/\/github.com\/Azure-Samples\/azure-sql-db-vector-search<\/a>.<\/li>\n<li>If you are looking for end-to-end samples, take a look here\u00a0<a href=\"https:\/\/aka.ms\/sqlai-samples\" target=\"_blank\" rel=\"noopener\">https:\/\/aka.ms\/sqlai-samples<\/a>\u00a0where you\u2019ll find:\n<ul>\n<li>Retrieval Augmented Generation (RAG) on your own data using LangChain<\/li>\n<li>RAG and Natural-Language-to-SQL (NL2SQL) together to build a complete chatbot on your own data, using Semantic Kernel<\/li>\n<li>A tool to quickly vectorize data you already have in your database and enable it for AI<\/li>\n<li>And much more!<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>We are thrilled to announce that Azure SQL Managed Instance now supports Vector type and functions in public preview.\u00a0 This builds on the momentum from Azure SQL Database, where vector support has been in public preview since December. Check out the detailed announcement \ud83d\udca1 Why Choose Azure SQL Managed Instance for AI-Driven Modernization? Effortless Cloud [&hellip;]<\/p>\n","protected":false},"author":159391,"featured_media":81,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[601,599,1,572,582,577,619,615],"tags":[590,602,510,636,314],"class_list":["post-4561","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-azure-openai","category-azure-sql","category-managed-instance","category-openai","category-rest-endpoint-invocation","category-t-sql","category-vectors","tag-ai","tag-azure-openai","tag-azure-sql-database","tag-azure-sql-managed-instance","tag-managed-instance"],"acf":[],"blog_post_summary":"<p>We are thrilled to announce that Azure SQL Managed Instance now supports Vector type and functions in public preview.\u00a0 This builds on the momentum from Azure SQL Database, where vector support has been in public preview since December. Check out the detailed announcement \ud83d\udca1 Why Choose Azure SQL Managed Instance for AI-Driven Modernization? Effortless Cloud [&hellip;]<\/p>\n","_links":{"self":[{"href":"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-json\/wp\/v2\/posts\/4561","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-json\/wp\/v2\/users\/159391"}],"replies":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-json\/wp\/v2\/comments?post=4561"}],"version-history":[{"count":0,"href":"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-json\/wp\/v2\/posts\/4561\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-json\/wp\/v2\/media\/81"}],"wp:attachment":[{"href":"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-json\/wp\/v2\/media?parent=4561"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-json\/wp\/v2\/categories?post=4561"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/azure-sql\/wp-json\/wp\/v2\/tags?post=4561"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}