{"id":16022,"date":"2024-07-16T12:50:20","date_gmt":"2024-07-16T19:50:20","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=16022"},"modified":"2025-06-13T16:36:49","modified_gmt":"2025-06-13T23:36:49","slug":"couchbase-vector-search-in-5-minutes","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/couchbase-vector-search-in-5-minutes\/","title":{"rendered":"Get Started With Couchbase Vector Search In 5 Minutes"},"content":{"rendered":"<h2><span style=\"font-weight: 400;\">What is a Vector<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">A Vector is an object that represents a real-world item as an <em>array of floating numbers<\/em>.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Each item in the real world is represented in Vector format(as an array) and has many dimensions (attributes) associated with the object based on its characteristics.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, if we want to represent Colours in Vector format, we can create an array of attribute values. Example <\/span><span style=\"font-weight: 400;\">[ \u201cR\u201d,\u201dG\u201d,\u201dB\u201d]<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Each colour in an RGB image is represented by three values: the amount of red, green, and blue light present. These values typically range from 0 to 255, indicating the intensity of each colour component.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Pure Red= <\/span><span style=\"font-weight: 400;\">[ \u201c255\u201d,\u201d0\u201d,\u201d0\u201d]<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Where:<\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span style=\"font-weight: 400;\">R represents the intensity of red (in this case, maximum intensity, 255),<\/span><\/li>\n<li><span style=\"font-weight: 400;\">G represents the intensity of green (in this case, 0, so no green),<\/span><\/li>\n<li><span style=\"font-weight: 400;\">B represents the intensity of blue (in this case, 0, so no blue).<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>Similarly, you can represent any colour using this RGB vector format, with values ranging from 0 to 255 for each colour channel.<\/p>\n<p><span style=\"font-weight: 400;\">If we want to find close matches for the colour red, we can find out based on the first attribute value of the colour.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Real world objects can have many other attributes that they have to represent and, therefore, a Vector representing a real world object is represented by a larger array of 512, 1028, 1536 or 2048 attribute values.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">What is Vector Search?<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Vector search is a method of finding items based on their vector representation. In vector search, each item is represented in multidimensional space where each dimension represents the value of the attribute of the item.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">More details can be found at:<\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.couchbase.com\/products\/vector-search\/\">Couchbase Vector Search capability<\/a><\/span><\/li>\n<li><a href=\"https:\/\/www.couchbase.com\/blog\/tag\/vector-search\/\"><span style=\"font-weight: 400;\">Blogs about Vector Search<\/span><\/a> and <a href=\"https:\/\/www.couchbase.com\/blog\/vector-databases\/\">Vector Databases<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Industry-wide Use Cases<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Vector search can be used across industries for various use cases, here are a few of them:<\/span><\/p>\n<ol>\n<li style=\"list-style-type: none;\">\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Content Generation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Anomaly detection<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hybrid Search<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI powered chatbots.<\/span><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<h2><span style=\"font-weight: 400;\">Vector Search vs. Full Text Search?<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Vector search and full-text search are both methods used for searching through collections of data, but they operate in different ways and are suited to different types of data and use cases.<\/span><\/p>\n<p><b>Full text search:<\/b><span style=\"font-weight: 400;\"> is a technique used in information retrieval to search and analyse textual content within documents or databases. Unlike traditional search methods that match exact phrases or keywords, full text search engines analyse the content of documents or records to match search queries based on the meaning and context of the words<\/span><span style=\"font-weight: 400;\">.<\/span><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><\/td>\n<td><strong>Comparison area<\/strong><\/td>\n<td><strong>Full text search<\/strong><\/td>\n<td><strong>Vector Search<\/strong><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">1<\/span><\/td>\n<td><strong>Representation of data<\/strong><\/td>\n<td><span style=\"font-weight: 400;\">Data is represented as documents of text or strings<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Data is represented as vectors in multidimensional space<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">2<\/span><\/td>\n<td><strong>Matching criteria<\/strong><\/td>\n<td><span style=\"font-weight: 400;\">Exact or fuzzy match<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Nearest neighbouring match<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">3<\/span><\/td>\n<td><strong>Search<\/strong><\/td>\n<td><span style=\"font-weight: 400;\">Textual search or comparison<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Contextual search or comparison based on attributes of object.<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">4<\/span><\/td>\n<td><strong>Use case<\/strong><\/td>\n<td><span style=\"font-weight: 400;\">Searching through a document, web page, email content, etc.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Searching through audio, video, image, text, etc.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span style=\"font-weight: 400;\">Why Couchbase for Vector Search?<\/span><\/h2>\n<ol>\n<li style=\"list-style-type: none;\">\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Vector across our products<\/strong>: First in the industry to announce support for all 3 deployments: cloud, on-prem, mobile.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Broad Capabilities<\/strong>: Integrated Cache, Full Text Search, Analytical Search, Time Series, Key-Value, Eventing and other features along with Vector search into single platform.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Ecosystem integration<\/strong>: <a href=\"https:\/\/python.langchain.com\/v0.2\/docs\/integrations\/providers\/couchbase\/\">LangChain<\/a> and <a href=\"https:\/\/docs.llamaindex.ai\/en\/latest\/api_reference\/storage\/vector_store\/couchbase\/\">LlamaIndex<\/a> integration.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Proven Speed and Flexibility<\/strong>: In-memory architecture, flexible json format and powering indexing.<\/span><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">More details can be found in our <a href=\"https:\/\/www.couchbase.com\/blog\/announcing-vector-search\/\">vector search release announcement<\/a>.\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Prerequisites<\/span><\/h2>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Couchbase Capella or Couchbase Server 7.6 EE\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Have already created a database<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Sample data:<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Download file <\/span><a href=\"https:\/\/cbc-remote-execution-examples-prod.s3.amazonaws.com\/color_data_2vectors.zip\"><span style=\"font-weight: 400;\">color_data_2vectors.zip<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">For this example, we will use the <em>rgb.json<\/em> file\u00a0<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Index file: <em>color-index.json<\/em><\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/p>\n<ul>\n<li><em>color-index.json:<\/em>\n<pre class=\"height:300 nums:false scroll:true minimize:true lang:js decode:true\">{\r\n    \"type\": \"fulltext-index\",\r\n    \"name\": \"color-index\",\r\n    \"sourceType\": \"gocbcore\",\r\n    \"sourceName\": \"vector-sample\",\r\n    \"sourceUUID\": \"789365cccdf940ee2814a5dd2752040a\",\r\n    \"planParams\": {\r\n      \"maxPartitionsPerPIndex\": 512,\r\n      \"indexPartitions\": 1\r\n    },\r\n    \"params\": {\r\n      \"doc_config\": {\r\n        \"docid_prefix_delim\": \"\",\r\n        \"docid_regexp\": \"\",\r\n        \"mode\": \"scope.collection.type_field\",\r\n        \"type_field\": \"type\"\r\n      },\r\n      \"mapping\": {\r\n        \"analysis\": {},\r\n        \"default_analyzer\": \"standard\",\r\n        \"default_datetime_parser\": \"dateTimeOptional\",\r\n        \"default_field\": \"_all\",\r\n        \"default_mapping\": {\r\n          \"dynamic\": false,\r\n          \"enabled\": false\r\n        },\r\n        \"default_type\": \"_default\",\r\n        \"docvalues_dynamic\": false,\r\n        \"index_dynamic\": false,\r\n        \"store_dynamic\": false,\r\n        \"type_field\": \"_type\",\r\n        \"types\": {\r\n        \"color.rgb\": {\r\n          \"dynamic\": false,\r\n          \"enabled\": true,\r\n          \"properties\": {\r\n            \"brightness\": {\r\n              \"dynamic\": false,\r\n              \"enabled\": true,\r\n              \"fields\": [\r\n                {\r\n                  \"index\": true,\r\n                  \"name\": \"brightness\",\r\n                  \"store\": true,\r\n                  \"type\": \"number\"\r\n                }\r\n              ]\r\n            },\r\n            \"color\": {\r\n              \"dynamic\": false,\r\n              \"enabled\": true,\r\n              \"fields\": [\r\n                {\r\n                  \"analyzer\": \"en\",\r\n                  \"index\": true,\r\n                  \"name\": \"color\",\r\n                  \"store\": true,\r\n                  \"type\": \"text\"\r\n                }\r\n              ]\r\n            },\r\n            \"colorvect_dot\": {\r\n              \"dynamic\": false,\r\n              \"enabled\": true,\r\n              \"fields\": [\r\n                {\r\n                  \"dims\": 3,\r\n                  \"index\": true,\r\n                  \"name\": \"colorvect_dot\",\r\n                  \"similarity\": \"dot_product\",\r\n                  \"type\": \"vector\"\r\n                }\r\n              ]\r\n            },\r\n            \"colorvect_l2\": {\r\n              \"dynamic\": false,\r\n              \"enabled\": true,\r\n              \"fields\": [\r\n                {\r\n                  \"dims\": 3,\r\n                  \"index\": true,\r\n                  \"name\": \"colorvect_l2\",\r\n                  \"similarity\": \"l2_norm\",\r\n                  \"type\": \"vector\"\r\n                }\r\n              ]\r\n            },\r\n            \"description\": {\r\n              \"dynamic\": false,\r\n              \"enabled\": true,\r\n              \"fields\": [\r\n                {\r\n                  \"analyzer\": \"en\",\r\n                  \"index\": true,\r\n                  \"name\": \"description\",\r\n                  \"store\": true,\r\n                  \"type\": \"text\"\r\n                }\r\n              ]\r\n            },\r\n            \"embedding_vector_dot\": {\r\n              \"dynamic\": false,\r\n              \"enabled\": true,\r\n              \"fields\": [\r\n                {\r\n                  \"dims\": 1536,\r\n                  \"index\": true,\r\n                  \"name\": \"embedding_vector_dot\",\r\n                  \"similarity\": \"dot_product\",\r\n                  \"type\": \"vector\"\r\n                }\r\n              ]\r\n            }\r\n          }\r\n        }\r\n      }\r\n    },\r\n    \"store\": {\r\n      \"indexType\": \"scorch\",\r\n      \"segmentVersion\": 16\r\n    }\r\n  },\r\n  \"sourceParams\": {}\r\n}<\/pre>\n<\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Sample search definition:<br \/>\n<\/span><code>{ \"fields\": [\"*\"], \"query\": { \"match_none\": \"\" }, \"knn\": [ { \"k\": 2, \"field\": \"colorvect_l2\", \"vector\": [ 0, 0, 128 ] } ] }<\/code><\/li>\n<\/ol>\n<h2><span style=\"font-weight: 400;\">Steps<\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">Create Sample Data<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Open Capella UI, Go to <em>Database<\/em>, and start importing data using import from browser using data tools:<\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Use sample rgb.json data file provided in prerequisites.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Choose option <strong>load from browser<\/strong>.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Select file: <em>rgb.json<\/em><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Specify new bucket with name: <em>vector-sample<\/em><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Specify new scope with name: <em>color<\/em><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Specify new collection with name: <em>rgb<\/em><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">In step 3, <em>preview<\/em> your data,<br \/>\n<\/span>Choose how Capella creates identifiers for each of your documents. Select option as field and specify field: <strong>Id<\/strong> as identified as shown in screenshot below.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Click Import.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image2-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-16023\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image2-1.png\" alt=\"\" width=\"571\" height=\"651\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image2-1.png 571w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image2-1-263x300.png 263w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image2-1-300x342.png 300w\" sizes=\"auto, (max-width: 571px) 100vw, 571px\" \/><\/a><\/p>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image7.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large wp-image-16024\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image7-1024x423.png\" alt=\"\" width=\"900\" height=\"372\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image7-1024x423.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image7-300x124.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image7-768x317.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image7-1536x634.png 1536w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image7-1320x545.png 1320w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image7.png 1900w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image6.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large wp-image-16025\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image6-1024x432.png\" alt=\"\" width=\"900\" height=\"380\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image6-1024x432.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image6-300x127.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image6-768x324.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image6-1536x649.png 1536w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image6-1320x558.png 1320w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image6.png 1906w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<h3><span style=\"font-weight: 400;\">Create Vector Search Index\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">In Search Options under <strong>Data Tools<\/strong>:<\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><strong>Create Search Index<br \/>\n<a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image9.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-16026\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image9-1024x333.png\" alt=\"\" width=\"645\" height=\"210\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image9-1024x333.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image9-300x97.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image9-768x250.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image9.png 1234w\" sizes=\"auto, (max-width: 645px) 100vw, 645px\" \/><\/a><br \/>\n<\/strong><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Select <strong>advanced mode<\/strong><\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Click on <strong>Index Definition<\/strong> on the right side of the UI<\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Select option <strong>Import from file<\/strong><\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Choose file <em>color-index.json<\/em> specified in prerequisites<\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Specify Index Name as:\u00a0<em> color-index.json<\/em><\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Choose bucket: <em>vector-sample<\/em><\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Scope will be auto-populated as <em>color<\/em><\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Click on <strong>Create Index<\/strong><\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image5-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large wp-image-16027\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image5-1-1024x423.png\" alt=\"\" width=\"900\" height=\"372\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image5-1-1024x423.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image5-1-300x124.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image5-1-768x318.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image5-1-1320x546.png 1320w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image5-1.png 1323w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<h3><span style=\"font-weight: 400;\">Perform a Vector Search\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Select the <strong>Search<\/strong> option in the <em>color-index<\/em> row (button near far right)<\/span><\/p>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image3-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large wp-image-16028\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image3-1-1024x292.png\" alt=\"\" width=\"900\" height=\"257\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image3-1-1024x292.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image3-1-300x86.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image3-1-768x219.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image3-1-1320x377.png 1320w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image3-1.png 1380w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Paste the search text from the prerequisite step into the Search window.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Click on <strong>Search<\/strong> to get a result (shows in window below the search text).<\/span><\/p>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image8.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large wp-image-16029\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image8-1024x371.png\" alt=\"\" width=\"900\" height=\"326\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image8-1024x371.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image8-300x109.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image8-768x279.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image8-1536x557.png 1536w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image8-1320x479.png 1320w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image8.png 1839w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<h2><span style=\"font-weight: 400;\">Conclusion<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">In this post, we have gone through the basics of what vector search is and how to quickly get started with Vector Search with Couchbase.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">After executing a basic vector search, one can easily combine SQL queries with vector search in Couchbase, helping to consolidate your database stack and avoid writing multiple queries to get a single meaningful result for an application.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Start for free<\/span><\/h3>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span style=\"font-weight: 400;\">Start your <\/span><a href=\"https:\/\/cloud.couchbase.com\/sign-up\"><span style=\"font-weight: 400;\">30-day trial account for Capella<\/span><\/a><span style=\"font-weight: 400;\"> to run your first experiment today!<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">References<\/span><\/h3>\n<ol>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.couchbase.com\/blog\/announcing-vector-search\/\">Vector Search release announcement<\/a>\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.couchbase.com\/products\/vector-search\/\">Couchbase Vector Search capability<\/a><\/span><\/li>\n<li><a href=\"https:\/\/www.couchbase.com\/blog\/tag\/vector-search\/\"><span style=\"font-weight: 400;\">Blogs about Vector Search<\/span><\/a> and <a href=\"https:\/\/www.couchbase.com\/blog\/vector-databases\/\">Vector Databases<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>What is a Vector A Vector is an object that represents a real-world item as an array of floating numbers.\u00a0 Each item in the real world is represented in Vector format(as an array) and has many dimensions (attributes) associated with [&hellip;]<\/p>\n","protected":false},"author":85424,"featured_media":16031,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[10122,1815,2225,1816,9936,9937],"tags":[9963,9995],"ppma_author":[9996],"class_list":["post-16022","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-ai","category-best-practices-and-tutorials","category-cloud","category-couchbase-server","category-search","category-vector-search","tag-langchain","tag-llamaindex"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.8 (Yoast SEO v25.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Get Started With Couchbase Vector Search In 5 Minutes - The Couchbase Blog<\/title>\n<meta name=\"description\" content=\"Vector search and full-text search are both methods used for 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