{"id":4453,"date":"2025-01-13T14:14:23","date_gmt":"2025-01-13T22:14:23","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/introducing-couchbase-as-vector-store-in-mindsdb\/"},"modified":"2025-01-13T14:14:23","modified_gmt":"2025-01-13T22:14:23","slug":"introducing-couchbase-as-vector-store-in-mindsdb","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/ko\/introducing-couchbase-as-vector-store-in-mindsdb\/","title":{"rendered":"Introducing Couchbase as a Vector Store in MindsDB"},"content":{"rendered":"\n<p>We\u2019re thrilled to announce that <b>Couchbase<\/b> is now supported as a vector store in <b>MindsDB<\/b>, bringing together the best of both worlds: MindsDB\u2019s cutting-edge machine learning capabilities and Couchbase\u2019s high-performance vector storage. With this new integration, users can seamlessly combine data and AI, unlocking powerful new possibilities for their applications.<\/p>\n\n\n\n<p>MindsDB is an open-source tool that integrates various artificial intelligence (AI) models with databases or other data management systems, enabling easy creation and deployment of new AI-based solutions. With its intuitive interface and broad compatibility with popular data sources, MindsDB facilitates the implementation of advanced machine learning models without requiring deep technical knowledge<\/p>\n\n\n\n<p>In the following sections, we&#8217;ll explore some details of this integration.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Setting up MindsDB with Couchbase<\/h2>\n\n\n\n<p>To get started with the MindsDB and Couchbase, you\u2019ll need to follow a few simple steps<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Installing MindsDB<\/h3>\n\n\n\n<p>Run the MindsDB Docker container.\u00a0Execute the following command to create a Docker container for MindsDB:<\/p>\n\n\n\n<p><code>docker run --name mindsdb_container -p 47334:47334 -p 47335:47335 mindsdb\/mindsdb<\/code><\/p>\n\n\n\n<p>Once the container is up and running, you can access the <b>MindsDB editor<\/b> by navigating to <code>https:\/\/127.0.0.1:47334<\/code> in your web browser.<\/p>\n\n\n\n<p><b>Alternative<\/b>: Use MindsDB\u2019s Docker Desktop extension to manage the container directly within Docker Desktop.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Installing Couchbase inside MindsDB<\/h3>\n\n\n\n<p>There are two ways to install the dependencies needed for Couchbase integration:<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Method 1: Install via MindsDB Editor<\/h4>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Open the MindsDB editor.<\/li>\n\n\n<li>Go to <b>Settings<\/b> and then <b>Manage Integrations<\/b>.<\/li>\n\n\n<li>Select the <b>CouchbaseVector<\/b> integration.<\/li>\n\n\n<li>Click <b>Install<\/b>.<\/li>\n\n<\/ol>\n\n\n\n<h4 class=\"wp-block-heading\">Method 2: Install via Command Line<\/h4>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Start the MindsDB Docker container:<br>\n<span><code>docker start mindsdb_container<\/code><\/span><\/li>\n\n\n<li>Start an interactive shell in the container:<br>\n<span><code>docker exec -it mindsdb_container sh<\/code><\/span><\/li>\n\n\n<li>Install the Couchbase dependencies:<br>\n<span><code>pip install .[couchbasevector]<\/code><\/span><\/li>\n\n\n<li>Exit the interactive shell:<br>\n<span><code>exit<\/code><\/span><\/li>\n\n\n<li>Restart the container:<br>\n<span><code>docker restart mindsdb_container<\/code><\/span><\/li>\n\n<\/ol>\n\n\n\n<p>Now you will have Couchbase installed as a dependency in your MindsDB editor!<\/p>\n\n\n\n<p><strong><span>Before performing vector searches in Couchbase, ensure that you have a Full-Text Search (FTS) index configured for your vector embeddings. This is required to perform vector search.<\/span><\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Connecting to Couchbase and performing vector search<\/h3>\n\n\n\n<p>Now that MindsDB and Couchbase are integrated, you can connect Couchbase as a vector store and perform vector searches. Here&#8217;s how:<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">1. Establish a database connection<\/h4>\n\n\n\n<p>Use the following SQL-like syntax to create a connection to your Couchbase instance. The example below connects to the <b>travel-sample<\/b> bucket (you can enable this bucket from the Couchbase UI).<\/p>\n\n\n<p>[crayon nums=&#8221;false&#8221; lang=&#8221;default&#8221; decode=&#8221;true&#8221;]CREATE DATABASE couchbase_vectorsource<br \/>\nWITH<br \/>\nengine=&#8217;couchbasevector&#8217;,<br \/>\nparameters={<br \/>\n  &#8220;connection_string&#8221;: &#8220;couchbase:\/\/localhost&#8221;,<br \/>\n  &#8220;bucket&#8221;: &#8220;travel-sample&#8221;,<br \/>\n  &#8220;user&#8221;: &#8220;admin&#8221;,<br \/>\n  &#8220;password&#8221;: &#8220;password&#8221;,<br \/>\n  &#8220;scope&#8221;: &#8220;inventory&#8221;<br \/>\n};[\/crayon]<\/p>\n\n\n\n<p><a href=\"https:\/\/www.couchbase.com\/wp-content\/uploads\/sites\/5\/2026\/05\/image2-2.gif\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-16760\" src=\"https:\/\/www.couchbase.com\/wp-content\/uploads\/sites\/5\/2026\/05\/image2-2.gif\" alt=\"\" width=\"800\" height=\"464\"><\/a><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">2. Create a table in Couchbase<\/h4>\n\n\n\n<p>You can create a collection (or table) in Couchbase and populate it with data from another MindsDB data source. For instance, to store vector embeddings from a MySQL database:<\/p>\n\n\n<p>[crayon nums=&#8221;false&#8221; lang=&#8221;default&#8221; decode=&#8221;true&#8221;]CREATE TABLE couchbase_vectorsource.test_embeddings (<br \/>\n  SELECT embeddings<br \/>\n  FROM mysql_datasource.test_embeddings<br \/>\n);<br \/>\n[\/crayon]<\/p>\n\n\n\n<p>In this example, <em>mysql_datasource<\/em> refers to another MindsDB data source connected to a MySQL database. The <em>test_embeddings<\/em> table contains the embeddings you wish to store in Couchbase.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">3. Performing\u00a0 vector search<\/h4>\n\n\n\n<p>To perform a vector search, use a query like the one below.<\/p>\n\n\n<p>[crayon nums=&#8221;false&#8221; lang=&#8221;default&#8221; decode=&#8221;true&#8221;]SELECT *<br \/>\nFROM couchbase_vectorsource.test_embeddings<br \/>\nWHERE embeddings = (<br \/>\n  SELECT embeddings<br \/>\n  FROM mysql_datasource.test_embeddings<br \/>\n  LIMIT 1<br \/>\n);<br \/>\n[\/crayon]<\/p>\n\n\n\n<p><a href=\"https:\/\/www.couchbase.com\/wp-content\/uploads\/sites\/5\/2026\/05\/image1-4.gif\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-16761\" src=\"https:\/\/www.couchbase.com\/wp-content\/uploads\/sites\/5\/2026\/05\/image1-4.gif\" alt=\"\" width=\"800\" height=\"464\"><\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Conclusion<\/h3>\n\n\n\n<p>This integration allows you to easily combine Couchbase\u2019s high-performance vector storage with MindsDB\u2019s machine learning capabilities, enabling the development of intelligent, scalable applications with minimal effort. Whether you\u2019re building recommendation systems, semantic search, or other AI-driven solutions, this powerful combination provides the tools needed to succeed.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Next steps<\/h3>\n\n\n\n<p>More information is available in the <a href=\"https:\/\/docs.mindsdb.com\/\">MindsDB documentation<\/a>, including an integration guide to <a href=\"https:\/\/docs.mindsdb.com\/integrations\/vector-db-integrations\/couchbase\">Couchbase<\/a>.<\/p>\n\n\n\n<p>Happy coding!<\/p>\n\n\n\n<p><br><br><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We\u2019re thrilled to announce that Couchbase is now supported as a vector store in MindsDB, bringing together the best of both worlds: MindsDB\u2019s cutting-edge machine learning capabilities and Couchbase\u2019s high-performance vector storage. With this new integration, users can seamlessly combine data and AI, unlocking powerful new possibilities for their applications. MindsDB is an open-source tool [&hellip;]<\/p>\n","protected":false},"author":85563,"featured_media":4450,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"_acf":"","footnotes":""},"categories":[598,178,301,54,727,715],"tags":[943],"ppma_author":[944],"class_list":["post-4453","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-ai","category-connectors","category-cloud","category-couchbase-server","category-generative-ai-genai","category-vector-search","tag-mindsdb"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.6 (Yoast SEO v27.6) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Introducing Couchbase as a Vector Store in MindsDB<\/title>\n<meta name=\"description\" content=\"Combine Couchbase and MindsDB to unlock AI-driven applications with high-performance 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