{"id":16759,"date":"2025-01-13T14:14:23","date_gmt":"2025-01-13T22:14:23","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=16759"},"modified":"2025-06-13T16:36:23","modified_gmt":"2025-06-13T23:36:23","slug":"introducing-couchbase-as-vector-store-in-mindsdb","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/pt\/introducing-couchbase-as-vector-store-in-mindsdb\/","title":{"rendered":"Apresentando o Couchbase como um armazenamento de vetores no MindsDB"},"content":{"rendered":"

We\u2019re thrilled to announce that Couchbase<\/b> is now supported as a vector store in 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

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

In the following sections, we’ll explore some details of this integration.<\/p>\n

Setting up MindsDB with Couchbase<\/h2>\n

To get started with the MindsDB and Couchbase, you\u2019ll need to follow a few simple steps<\/p>\n

Installing MindsDB<\/h3>\n

Run the MindsDB Docker container.\u00a0Execute the following command to create a Docker container for MindsDB:<\/p>\n

docker run --name mindsdb_container -p 47334:47334 -p 47335:47335 mindsdb\/mindsdb<\/code><\/p>\n

Once the container is up and running, you can access the MindsDB editor<\/b> by navigating to https:\/\/127.0.0.1:47334<\/code> in your web browser.<\/p>\n

Alternative<\/b>: Use MindsDB\u2019s Docker Desktop extension to manage the container directly within Docker Desktop.<\/p>\n

Installing Couchbase inside MindsDB<\/h3>\n

There are two ways to install the dependencies needed for Couchbase integration:<\/p>\n

Method 1: Install via MindsDB Editor<\/h4>\n
    \n
  1. \n
      \n
    1. Open the MindsDB editor.<\/li>\n
    2. Go to Settings<\/b> and then Manage Integrations<\/b>.<\/li>\n
    3. Select the CouchbaseVector<\/b> integration.<\/li>\n
    4. Click Install<\/b>.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

      Method 2: Install via Command Line<\/h4>\n
        \n
      1. \n
          \n
        1. Start the MindsDB Docker container:
          \ndocker start mindsdb_container<\/code><\/span><\/li>\n
        2. Start an interactive shell in the container:
          \ndocker exec -it mindsdb_container sh<\/code><\/span><\/li>\n
        3. Install the Couchbase dependencies:
          \npip install .[couchbasevector]<\/code><\/span><\/li>\n
        4. Exit the interactive shell:
          \nexit<\/code><\/span><\/li>\n
        5. Restart the container:
          \ndocker restart mindsdb_container<\/code><\/span><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

          Now you will have Couchbase installed as a dependency in your MindsDB editor!<\/p>\n

          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

          Connecting to Couchbase and performing vector search<\/h3>\n

          Now that MindsDB and Couchbase are integrated, you can connect Couchbase as a vector store and perform vector searches. Here’s how:<\/p>\n

          1. Establish a database connection<\/h4>\n

          Use the following SQL-like syntax to create a connection to your Couchbase instance. The example below connects to the travel-sample<\/b> bucket (you can enable this bucket from the Couchbase UI).<\/p>\n

          CREATE DATABASE couchbase_vectorsource\r\nWITH\r\nengine='couchbasevector',\r\nparameters={\r\n  \"connection_string\": \"couchbase:\/\/localhost\",\r\n  \"bucket\": \"travel-sample\",\r\n  \"user\": \"admin\",\r\n  \"password\": \"password\",\r\n  \"scope\": \"inventory\"\r\n};<\/pre>\n
          \"\"<\/a><\/p>\n
          2. Create a table in Couchbase<\/h4>\n
          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
          CREATE TABLE couchbase_vectorsource.test_embeddings (\r\n  SELECT embeddings\r\n  FROM mysql_datasource.test_embeddings\r\n);\r\n<\/pre>\n
          In this example, mysql_datasource<\/em> refers to another MindsDB data source connected to a MySQL database. The test_embeddings<\/em> table contains the embeddings you wish to store in Couchbase.<\/p>\n
          3. Performing\u00a0 vector search<\/h4>\n
          To perform a vector search, use a query like the one below.<\/p>\n
          SELECT *\r\nFROM couchbase_vectorsource.test_embeddings\r\nWHERE embeddings = (\r\n  SELECT embeddings\r\n  FROM mysql_datasource.test_embeddings\r\n  LIMIT 1\r\n);\r\n<\/pre>\n
          \"\"<\/a><\/p>\n
          Conclusion<\/h3>\n
          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
          Next steps<\/h3>\n
          More information is available in the MindsDB documentation<\/a>, including an integration guide to Couchbase<\/a>.<\/p>\n
          Happy coding!<\/p>\n


          <\/p>\n","protected":false},"excerpt":{"rendered":"
          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 […]<\/p>\n","protected":false},"author":85563,"featured_media":16764,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[10122,2242,2225,1816,9973,9937],"tags":[10078],"ppma_author":[10077],"class_list":["post-16759","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":"\nIntroducing 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 vector storage and seamless integration.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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