{"id":40147,"date":"2025-08-20T17:53:40","date_gmt":"2025-08-20T15:53:40","guid":{"rendered":"https:\/\/www.dbi-services.com\/blog\/?p=40147"},"modified":"2025-08-20T18:03:11","modified_gmt":"2025-08-20T16:03:11","slug":"sql-server-2025-vector-indexes-semantic-search-performance","status":"publish","type":"post","link":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/","title":{"rendered":"SQL-Server 2025: Vector Indexes &amp; Semantic Search Performance"},"content":{"rendered":"<p>When performing search operations on large datasets, performance is a key consideration for ensuring that an application remains efficient and user-friendly. This consideration equally applies when working with embedded data. That\u2019s why in this blog post we\u2019ll explore the different possibilities for vector search and show how to leverage the latest features of SQL Server 2025 Preview to accelerate your semantic search workloads.<\/p>\n\n\n<h2 class=\"wp-block-heading\" id=\"h-exact-and-approximate-search\">Exact and Approximate Search:<\/h2>\n\n\n\n<p>First, let\u2019s talk about the different functions available in SQL-Server 2025 Preview and the concepts behind them.<\/p>\n\n\n\n<p>If you read my last <a href=\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-generate-data-embeddings-for-semantic-search\/\">blog <\/a>post, you may remember that we used the VECTOR_DISTANCE() function to search for black pants I might wear during my summer vacation. This function calculates the Exact Nearest Neighbors (ENN) for the vector of the search text. In practice, it calculates the vector distance between the given vector and all other vectors, then sorts the results and returns the top (K-) Nearest Neighbors (NN). In the context of OLTP systems and B-tree indexes, you can compare this process to an index scan operation with additional sorting and computation overhead. As you can already anticipate, calculating the Exact Nearest Neighbors can be quite resource-intensive depending on the amount of data. In the Microsoft documentation, the general recommendation is to perform an exact search when you have fewer than 50\u2019000 vectors &#8211; noting that you can also prefilter the number of vectors using predicates.<\/p>\n\n\n\n<p>When working with large vector datasets the VECTOR_SEARCH() function comes into play. Instead of scanning the entire dataset and calculating the distance for each vector, the VECTOR_SEARCH() function approximates the nearest neighbors (ANN) for a given vector using vector index structures. While ANN requires far fewer resources than exact K-NN, it comes with a trade-off in terms of accuracy.<\/p>\n\n\n\n<p>This trade-off in accuracy is measured by the recall value. Recall represents the overlap between the approximate neighbors found and the true K-NN set. For example, if we search for the 10 nearest neighbors of a vector and ANN returns 7 results that are also in the exact K-NN set, the recall would be 0.7 (7 \/10 = 0.9). The closer the recall is to 1, the more accurate the ANN results are.<\/p>\n\n\n\n<p>In SQL-Server 2025 Preview, a vector index is required to perform ANN searches with the VECTOR_SEARCH() function. SQL-Server 2025 Preview uses the DiskANN algorithm to create these graph-based vector indexes. DiskANN is designed to leverage SSDs and minimal memory resources for storing and handling graph indexes, while still maintaining high accuracy with a recall of around 0.95.<\/p>\n\n\n\n<p>Note: While the VECTOR_DISTANCE&nbsp; function is available in SQL-Server 2025 preview and Azure SQL, the VECTOR_SEARCH function is currently only available in SQL-Server 2025 Preview.<\/p>\n\n\n\n<p>You can get more information&#8217;s from Microsoft&#8217;s <a href=\"https:\/\/learn.microsoft.com\/en-us\/sql\/relational-databases\/vectors\/vectors-sql-server?view=sql-server-ver17\">documentation<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-let-s-take-a-practical-look-at-the-features\">Let\u2019s take a practical look at the features:<\/h2>\n\n\n<p>First of all we have to enable some trace flags to be able to use the preview features:<\/p>\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: sql; title: ; notranslate\" title=\"\">\n\tDBCC TRACEON(466, 474, 13981, -1);\n\tDBCC TRACESTATUS;\n<\/pre><\/div>\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"383\" height=\"270\" src=\"http:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/1-2.png\" alt=\"\" class=\"wp-image-40148\" srcset=\"https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/1-2.png 383w, https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/1-2-300x211.png 300w\" sizes=\"auto, (max-width: 383px) 100vw, 383px\" \/><\/figure>\n\n\n<p>Then I\u2019m creating a vector index on my \u201cembedding\u201d column in my \u201cdbo.products\u201d table. I\u2019m using the DiskANN algorithm which is currently the only one which is supported. Further I\u2019m using the \u201ccosine\u201d metric for the similarity calculation. You can also use \u201cEuclidean Distance\u201d or \u201cDot Product\u201d as similarity metrics.<\/p>\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: sql; title: ; notranslate\" title=\"\">\nCREATE VECTOR INDEX vec_idx ON &#x5B;dbo].&#x5B;products](&#x5B;embedding]) \nWITH (METRIC = &#039;cosine&#039;, TYPE = &#039;diskann&#039;);\n<\/pre><\/div>\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"502\" height=\"257\" src=\"http:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/2-2.png\" alt=\"\" class=\"wp-image-40149\" srcset=\"https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/2-2.png 502w, https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/2-2-300x154.png 300w\" sizes=\"auto, (max-width: 502px) 100vw, 502px\" \/><\/figure>\n\n\n\n<p>Then we are ready to go and we can test the VECTOR_SEARCH function with the query below:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: sql; title: ; notranslate\" title=\"\">\n--Test Approximate Search\n\nDECLARE @SemanticSearchText Nvarchar(max) = &#039;Im looking for black pants for men which I can wear during my summer vacation.&#039;\nDECLARE @qv VECTOR(1536) = AI_GENERATE_EMBEDDINGS(@SemanticSearchText USE MODEL OpenAITextEmbedding3Small);\n\nSELECT\n\t\tproduct_id,\n\t\tproduct_name, \n\t\tDescription,\n\t\tprice,\n\t\ts.season,\n\t\tg.gender\n\t\tFROM\n\t\t\tVECTOR_SEARCH(\n\t\t\t\tTABLE = &#x5B;dbo].&#x5B;products] AS p, \n\t\t\t\tCOLUMN = &#x5B;embedding], \n\t\t\t\tSIMILAR_TO = @qv, \n\t\t\t\tMETRIC = &#039;cosine&#039;, \n\t\t\t\tTOP_N = 10\n\t\t\t) AS vs\n\t\tinner join dbo.season s on p.season_id = s.season_id\n\t\tinner join dbo.gender g on p.gender_id = g.gender_id\n\tORDER BY vs.distance\n\n<\/pre><\/div>\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"616\" height=\"477\" src=\"https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/3-4.png\" alt=\"\" class=\"wp-image-40158\" style=\"width:667px;height:auto\" srcset=\"https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/3-4.png 616w, https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/3-4-300x232.png 300w\" sizes=\"auto, (max-width: 616px) 100vw, 616px\" \/><\/figure>\n\n\n<p>As you can see we are getting a pretty accurate result. Lets compare the results of the exact search using the VECTOR_DISTANCE function and the approximate search using the VECTOR_SEARCH function with the code below:<\/p>\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: sql; title: ; notranslate\" title=\"\">\nDECLARE @SemanticSearchText Nvarchar(max) = &#039;Im looking for black pants for men which I can wear during my summer vacation.&#039;\n  \n    DECLARE @qv VECTOR(1536) = AI_GENERATE_EMBEDDINGS(@SemanticSearchText USE MODEL OpenAITextEmbedding3Small);\n \n --Exact Search\n\n    SELECT TOP(10)\n\t\tproduct_id as ENN_product_id,\n\t\tproduct_name as ENN_product_name, \n\t\tDescription as ENN_description,\n\t\tprice as ENN_price,\n\t\ts.season as ENN_price,\n\t\tg.gender as ENN_gender,\n\t\tVECTOR_DISTANCE(&#039;cosine&#039;, @qv, embedding) AS distance \n\tFROM dbo.products p\n\t\tinner join dbo.season s on p.season_id = s.season_id\n\t\tinner join dbo.gender g on p.gender_id = g.gender_id\n\t\tORDER BY   distance;\n\n--Approximate Search\n\n\tSELECT\n\t\tproduct_id as ANN_product_id,\n\t\tproduct_name as ANN_product_name, \n\t\tDescription as ANN_Description,\n\t\tprice as ANN_price,\n\t\ts.season as ANN_season,\n\t\tg.gender as ANN_gender\n\t\tFROM\n\t\t\tVECTOR_SEARCH(\n\t\t\t\tTABLE = &#x5B;dbo].&#x5B;products] AS p, \n\t\t\t\tCOLUMN = &#x5B;embedding], \n\t\t\t\tSIMILAR_TO = @qv, \n\t\t\t\tMETRIC = &#039;cosine&#039;, \n\t\t\t\tTOP_N = 10\n\t\t\t) AS vs\n\t\tinner join dbo.season s on p.season_id = s.season_id\n\t\tinner join dbo.gender g on p.gender_id = g.gender_id\n\tORDER BY vs.distance\n\n<\/pre><\/div>\n\n<p>As you can see, we are getting a fairly accurate ANN result. This corresponds to a recall of 0.8, since only the products with IDs 1292 and 2167 are missing from the exact search result set:<\/p>\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"616\" height=\"346\" src=\"http:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/4-3.png\" alt=\"\" class=\"wp-image-40154\" style=\"width:739px;height:auto\" srcset=\"https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/4-3.png 616w, https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/4-3-300x169.png 300w\" sizes=\"auto, (max-width: 616px) 100vw, 616px\" \/><\/figure>\n\n\n\n<p>Of course, in this example we only have 3\u2019000 products in our database, so the savings in resource consumption when using ANN are not very significant. However, when processing much larger datasets, using ANN becomes highly relevant.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-limitations-with-vector-indexes-in-sql-server-2025-preview\">Limitations with Vector indexes \u2013 in SQL-Server 2025 Preview<\/h2>\n\n\n<p>Interesting to know is that tables with a vector index are read only in the preview edition so you can\u2019t manipulate data at the moment:<\/p>\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: sql; title: ; notranslate\" title=\"\">\n\tupdate dbo.products\n\tset price = price * 0.8\n\twhere product_id = 13\n<\/pre><\/div>\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"576\" height=\"259\" src=\"http:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/5-2.png\" alt=\"\" class=\"wp-image-40155\" srcset=\"https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/5-2.png 576w, https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/5-2-300x135.png 300w\" sizes=\"auto, (max-width: 576px) 100vw, 576px\" \/><\/figure>\n\n\n<p>In addition to support a vector index, the table must have a clustered index defined on a single column that is a primary key with the integer data type.\u00a0 In the preview edition, vector indexes do also not support partitioning and are not replicated to subscribers when using replication. According to Microsoft, these limitations apply to the current preview edition of SQL Server 2025. I\u2019m excited to see if these limitations will be removed in the GA release.<\/p>\n<p>Let me know your thoughts in the comment section below and thanks for reading!<\/p>\n<p>Hocine<\/p>\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>When performing search operations on large datasets, performance is a key consideration for ensuring that an application remains efficient and user-friendly. This consideration equally applies when working with embedded data. That\u2019s why in this blog post we\u2019ll explore the different possibilities for vector search and show how to leverage the latest features of SQL Server [&hellip;]<\/p>\n","protected":false},"author":145,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[198,99],"tags":[2810,135,652,51],"type_dbi":[3654,3656,3657,3655,2874],"class_list":["post-40147","post","type-post","status-publish","format-standard","hentry","category-database-management","category-sql-server","tag-ai","tag-cloud","tag-data","tag-sql-server","type-ai","type-data","type-performance","type-semantic-search","type-sql-server"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.2 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>SQL-Server 2025: Vector Indexes &amp; Semantic Search Performance - dbi Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SQL-Server 2025: Vector Indexes &amp; Semantic Search Performance\" \/>\n<meta property=\"og:description\" content=\"When performing search operations on large datasets, performance is a key consideration for ensuring that an application remains efficient and user-friendly. This consideration equally applies when working with embedded data. That\u2019s why in this blog post we\u2019ll explore the different possibilities for vector search and show how to leverage the latest features of SQL Server [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/\" \/>\n<meta property=\"og:site_name\" content=\"dbi Blog\" \/>\n<meta property=\"article:published_time\" content=\"2025-08-20T15:53:40+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-08-20T16:03:11+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/1-2.png\" \/>\n\t<meta property=\"og:image:width\" content=\"383\" \/>\n\t<meta property=\"og:image:height\" content=\"270\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Hocine Mechara\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Hocine Mechara\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/\"},\"author\":{\"name\":\"Hocine Mechara\",\"@id\":\"https:\/\/www.dbi-services.com\/blog\/#\/schema\/person\/29415d02bc1b50884796a01cf649951f\"},\"headline\":\"SQL-Server 2025: Vector Indexes &amp; Semantic Search Performance\",\"datePublished\":\"2025-08-20T15:53:40+00:00\",\"dateModified\":\"2025-08-20T16:03:11+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/\"},\"wordCount\":788,\"commentCount\":0,\"image\":{\"@id\":\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#primaryimage\"},\"thumbnailUrl\":\"http:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/1-2.png\",\"keywords\":[\"ai\",\"Cloud\",\"data\",\"SQL Server\"],\"articleSection\":[\"Database management\",\"SQL Server\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/\",\"url\":\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/\",\"name\":\"SQL-Server 2025: Vector Indexes &amp; Semantic Search Performance - dbi Blog\",\"isPartOf\":{\"@id\":\"https:\/\/www.dbi-services.com\/blog\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#primaryimage\"},\"thumbnailUrl\":\"http:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/1-2.png\",\"datePublished\":\"2025-08-20T15:53:40+00:00\",\"dateModified\":\"2025-08-20T16:03:11+00:00\",\"author\":{\"@id\":\"https:\/\/www.dbi-services.com\/blog\/#\/schema\/person\/29415d02bc1b50884796a01cf649951f\"},\"breadcrumb\":{\"@id\":\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#primaryimage\",\"url\":\"https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/1-2.png\",\"contentUrl\":\"https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/1-2.png\",\"width\":383,\"height\":270},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Accueil\",\"item\":\"https:\/\/www.dbi-services.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"SQL-Server 2025: Vector Indexes &amp; Semantic Search Performance\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.dbi-services.com\/blog\/#website\",\"url\":\"https:\/\/www.dbi-services.com\/blog\/\",\"name\":\"dbi Blog\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.dbi-services.com\/blog\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.dbi-services.com\/blog\/#\/schema\/person\/29415d02bc1b50884796a01cf649951f\",\"name\":\"Hocine Mechara\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/secure.gravatar.com\/avatar\/f771f838feed0619485da1e42ae05d771dcb446e1f4785244582280315fa73c3?s=96&d=mm&r=g\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/f771f838feed0619485da1e42ae05d771dcb446e1f4785244582280315fa73c3?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/f771f838feed0619485da1e42ae05d771dcb446e1f4785244582280315fa73c3?s=96&d=mm&r=g\",\"caption\":\"Hocine Mechara\"},\"url\":\"https:\/\/www.dbi-services.com\/blog\/author\/hocinemechara\/\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"SQL-Server 2025: Vector Indexes &amp; Semantic Search Performance - dbi Blog","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/","og_locale":"en_US","og_type":"article","og_title":"SQL-Server 2025: Vector Indexes &amp; Semantic Search Performance","og_description":"When performing search operations on large datasets, performance is a key consideration for ensuring that an application remains efficient and user-friendly. This consideration equally applies when working with embedded data. That\u2019s why in this blog post we\u2019ll explore the different possibilities for vector search and show how to leverage the latest features of SQL Server [&hellip;]","og_url":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/","og_site_name":"dbi Blog","article_published_time":"2025-08-20T15:53:40+00:00","article_modified_time":"2025-08-20T16:03:11+00:00","og_image":[{"width":383,"height":270,"url":"https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/1-2.png","type":"image\/png"}],"author":"Hocine Mechara","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Hocine Mechara","Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#article","isPartOf":{"@id":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/"},"author":{"name":"Hocine Mechara","@id":"https:\/\/www.dbi-services.com\/blog\/#\/schema\/person\/29415d02bc1b50884796a01cf649951f"},"headline":"SQL-Server 2025: Vector Indexes &amp; Semantic Search Performance","datePublished":"2025-08-20T15:53:40+00:00","dateModified":"2025-08-20T16:03:11+00:00","mainEntityOfPage":{"@id":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/"},"wordCount":788,"commentCount":0,"image":{"@id":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#primaryimage"},"thumbnailUrl":"http:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/1-2.png","keywords":["ai","Cloud","data","SQL Server"],"articleSection":["Database management","SQL Server"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/","url":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/","name":"SQL-Server 2025: Vector Indexes &amp; Semantic Search Performance - dbi Blog","isPartOf":{"@id":"https:\/\/www.dbi-services.com\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#primaryimage"},"image":{"@id":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#primaryimage"},"thumbnailUrl":"http:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/1-2.png","datePublished":"2025-08-20T15:53:40+00:00","dateModified":"2025-08-20T16:03:11+00:00","author":{"@id":"https:\/\/www.dbi-services.com\/blog\/#\/schema\/person\/29415d02bc1b50884796a01cf649951f"},"breadcrumb":{"@id":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#primaryimage","url":"https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/1-2.png","contentUrl":"https:\/\/www.dbi-services.com\/blog\/wp-content\/uploads\/sites\/2\/2025\/08\/1-2.png","width":383,"height":270},{"@type":"BreadcrumbList","@id":"https:\/\/www.dbi-services.com\/blog\/sql-server-2025-vector-indexes-semantic-search-performance\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Accueil","item":"https:\/\/www.dbi-services.com\/blog\/"},{"@type":"ListItem","position":2,"name":"SQL-Server 2025: Vector Indexes &amp; Semantic Search Performance"}]},{"@type":"WebSite","@id":"https:\/\/www.dbi-services.com\/blog\/#website","url":"https:\/\/www.dbi-services.com\/blog\/","name":"dbi Blog","description":"","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.dbi-services.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/www.dbi-services.com\/blog\/#\/schema\/person\/29415d02bc1b50884796a01cf649951f","name":"Hocine Mechara","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/f771f838feed0619485da1e42ae05d771dcb446e1f4785244582280315fa73c3?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/f771f838feed0619485da1e42ae05d771dcb446e1f4785244582280315fa73c3?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/f771f838feed0619485da1e42ae05d771dcb446e1f4785244582280315fa73c3?s=96&d=mm&r=g","caption":"Hocine Mechara"},"url":"https:\/\/www.dbi-services.com\/blog\/author\/hocinemechara\/"}]}},"_links":{"self":[{"href":"https:\/\/www.dbi-services.com\/blog\/wp-json\/wp\/v2\/posts\/40147","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dbi-services.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.dbi-services.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.dbi-services.com\/blog\/wp-json\/wp\/v2\/users\/145"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dbi-services.com\/blog\/wp-json\/wp\/v2\/comments?post=40147"}],"version-history":[{"count":7,"href":"https:\/\/www.dbi-services.com\/blog\/wp-json\/wp\/v2\/posts\/40147\/revisions"}],"predecessor-version":[{"id":40164,"href":"https:\/\/www.dbi-services.com\/blog\/wp-json\/wp\/v2\/posts\/40147\/revisions\/40164"}],"wp:attachment":[{"href":"https:\/\/www.dbi-services.com\/blog\/wp-json\/wp\/v2\/media?parent=40147"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dbi-services.com\/blog\/wp-json\/wp\/v2\/categories?post=40147"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dbi-services.com\/blog\/wp-json\/wp\/v2\/tags?post=40147"},{"taxonomy":"type","embeddable":true,"href":"https:\/\/www.dbi-services.com\/blog\/wp-json\/wp\/v2\/type_dbi?post=40147"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}