

{"id":6259,"date":"2023-11-16T14:32:16","date_gmt":"2023-11-16T14:32:16","guid":{"rendered":"https:\/\/www.prefixbox.com\/blog\/?p=6259"},"modified":"2024-08-27T09:16:55","modified_gmt":"2024-08-27T09:16:55","slug":"unveiling-how-vector-search-works","status":"publish","type":"post","link":"https:\/\/www.prefixbox.com\/blog\/unveiling-how-vector-search-works\/","title":{"rendered":"Unveiling How Vector Search Works"},"content":{"rendered":"\n<div class=\"wp-block-getwid-section alignfull post-header has-inner-blocks-gap-small getwid-section-content-custom-width\" style=\"margin-top:;margin-bottom:50px\"><div class=\"wp-block-getwid-section__wrapper getwid-padding-left-medium getwid-padding-right-medium\" style=\"padding-top:30px;padding-bottom:20px\"><div class=\"wp-block-getwid-section__inner-wrapper\" style=\"max-width:1140px\"><div class=\"wp-block-getwid-section__background-holder\"><div class=\"wp-block-getwid-section__background\"><\/div><div class=\"wp-block-getwid-section__foreground\" style=\"opacity:0.2;mix-blend-mode:normal\"><\/div><\/div><div class=\"wp-block-getwid-section__content\"><div class=\"wp-block-getwid-section__inner-content\">\n<h1 class=\"has-text-align-center has-text-color\" id=\"gf81f2a1c12e1\" style=\"color:#2d3064\">Unveiling How Vector Search Works<\/h1>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:30px\" aria-hidden=\"true\"><\/div>\n\n\n\n<div class=\"wp-block-columns has-2-columns are-vertically-aligned-center\">\n<div class=\"wp-block-column is-vertically-aligned-top is-vertically-aligned-center\">\n<p class=\"has-text-color\" style=\"color:#2d3064\">Vector Search is a game-changer for retailers striving to enhance their online user experience, boost conversion rates, and increase their online revenue.&nbsp;<\/p>\n\n\n\n<p class=\"has-text-color\" style=\"color:#2d3064\">In this article we are going to delve deeper into how this transformative technology actually works.<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:10px\" aria-hidden=\"true\"><\/div>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-vertically-aligned-center\" style=\"flex-basis:500px\">\n<div class=\"wp-block-image\"><figure class=\"alignleft size-large\"><img loading=\"lazy\" width=\"1024\" height=\"839\" src=\"https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/Group-2234-1024x839.png\" alt=\"how Vector search works illustration\" class=\"wp-image-6274\" srcset=\"https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/Group-2234-1024x839.png 1024w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/Group-2234-300x246.png 300w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/Group-2234-768x629.png 768w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/Group-2234-1536x1258.png 1536w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/Group-2234-2048x1678.png 2048w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/Group-2234-938x769.png 938w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/Group-2234-1130x926.png 1130w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n<\/div>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:20px\" aria-hidden=\"true\"><\/div>\n\n\n\n<div class=\"wp-block-getwid-table-of-contents is-style-default table-of-contents-toc\"><ul class=\"wp-block-getwid-table-of-contents__list\"><li><a href=\"#g1c4d82ccd76a\">Mathematical representation of information<\/a><ul><li><a href=\"#gffc42fde3fa2\">Vectorization methods<\/a><\/li><li><a href=\"#gffc42fde3fa2\">Indexing<\/a><\/li><li><a href=\"#gffc42fde3fa2\">Search Query Vector<\/a><\/li><li><a href=\"#gffc42fde3fa2\">Similarity Metrics<\/a><\/li><\/ul><\/li><li><a href=\"#gffc42fde3fa2\">Utilizing the joint power of keyword and vector search<\/a><\/li><li><a href=\"#gffc42fde3fa2\">Summary<\/a><\/li><\/ul><\/div>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:20px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p>We&#8217;ve recently covered the<a href=\"https:\/\/app\/document\/2268731\/topics\" target=\"_blank\"> basics and the main benefits of vector search<\/a>, an emerging technology that&#8217;s redefining how we search and shop online.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.youtube.com\/@Prefixbox\/\" target=\"_blank\" rel=\"noopener\">Check our mini video series on the topic too<\/a>! Now let&#8217;s see the technical aspects of vector search.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 id=\"g1c4d82ccd76a\">Mathematical representation of information<\/h2>\n\n\n\n<div class=\"wp-block-getwid-section getwid-margin-top-none\" style=\"margin-bottom:-20px\"><div class=\"wp-block-getwid-section__wrapper getwid-padding-top-none getwid-padding-bottom-none getwid-padding-left-none getwid-padding-right-none getwid-margin-left-none getwid-margin-right-none\"><div class=\"wp-block-getwid-section__inner-wrapper\"><div class=\"wp-block-getwid-section__background-holder\"><div class=\"wp-block-getwid-section__background\"><\/div><div class=\"wp-block-getwid-section__foreground\"><\/div><\/div><div class=\"wp-block-getwid-section__content\"><div class=\"wp-block-getwid-section__inner-content\">\n<hr class=\"wp-block-separator has-text-color has-background blog-content-separator\" style=\"background-color:#3de3cf;color:#3de3cf\"\/>\n<\/div><\/div><\/div><\/div><\/div>\n\n\n\n<p>The key difference between vector and keyword search lies in the way data is represented and compared. <\/p>\n\n\n\n<p>Traditional search methods treat data as a collection of words, focusing on the presence or absence of specific terms. Vector search, on the other hand, uses <a href=\"https:\/\/cloud.google.com\/blog\/topics\/developers-practitioners\/meet-ais-multitool-vector-embeddings\" target=\"_blank\" rel=\"noopener\"><strong>vector<\/strong> <strong>embeddings<\/strong><\/a>&nbsp;to represent data points.<\/p>\n\n\n\n<p class=\"paragraph-highlight has-background\" style=\"background-color:#eaf2ff\">Vectors are essentially mathematical representations of objects, whether they be words, products, or images.<\/p>\n\n\n\n<p>Each product in an E-commerce catalog is translated into a vector in a high-dimensional space. These dimensions can represent various product attributes. For example, movies might be represented as vectors where each dimension represents a movie feature (e.g., genre, director, actors, etc.).<\/p>\n\n\n\n<p>By operating in high-dimensional spaces, the search algorithm can capture intricate relationships and similarities between products. For example, products with similar attributes will have vectors that are closer in this multi-dimensional space.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h3 id=\"gffc42fde3fa2\">Vectorization methods<\/h3>\n\n\n\n<div class=\"wp-block-getwid-section getwid-margin-top-none\" style=\"margin-bottom:-20px\"><div class=\"wp-block-getwid-section__wrapper getwid-padding-top-none getwid-padding-bottom-none getwid-padding-left-none getwid-padding-right-none getwid-margin-left-none getwid-margin-right-none\"><div class=\"wp-block-getwid-section__inner-wrapper\"><div class=\"wp-block-getwid-section__background-holder\"><div class=\"wp-block-getwid-section__background\"><\/div><div class=\"wp-block-getwid-section__foreground\"><\/div><\/div><div class=\"wp-block-getwid-section__content\"><div class=\"wp-block-getwid-section__inner-content\">\n<hr class=\"wp-block-separator has-text-color has-background blog-content-separator\" style=\"background-color:#3de3cf;color:#3de3cf\"\/>\n<\/div><\/div><\/div><\/div><\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" width=\"1024\" height=\"1024\" src=\"https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/E-commerce-site-search-relevancy-illustration-1024x1024.jpg\" alt=\"E-commerce site search relevancy\" class=\"wp-image-6279\" srcset=\"https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/E-commerce-site-search-relevancy-illustration-1024x1024.jpg 1024w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/E-commerce-site-search-relevancy-illustration-300x300.jpg 300w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/E-commerce-site-search-relevancy-illustration-150x150.jpg 150w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/E-commerce-site-search-relevancy-illustration-768x768.jpg 768w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/E-commerce-site-search-relevancy-illustration-1536x1536.jpg 1536w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/E-commerce-site-search-relevancy-illustration-938x938.jpg 938w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/E-commerce-site-search-relevancy-illustration-1130x1130.jpg 1130w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/E-commerce-site-search-relevancy-illustration.jpg 2000w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>To create vector representations, you can use various techniques, such as <a href=\"https:\/\/serokell.io\/blog\/word2vec\" target=\"_blank\" rel=\"noopener\">Word2Vec<\/a>, <a href=\"https:\/\/medium.com\/wisio\/a-gentle-introduction-to-doc2vec-db3e8c0cce5e\" target=\"_blank\" rel=\"noopener\">Doc2Vec<\/a> or deep learning models like <a href=\"https:\/\/www.techtarget.com\/searchenterpriseai\/definition\/convolutional-neural-network\" target=\"_blank\" rel=\"noopener\">convolutional neural networks (CNNs)<\/a> or <a href=\"https:\/\/machinelearningmastery.com\/an-introduction-to-recurrent-neural-networks-and-the-math-that-powers-them\/\" target=\"_blank\" rel=\"noopener\">recurrent neural networks (RNNs)<\/a>. The choice of machine learning method depends on the data and the problem you are trying to solve.<\/p>\n\n\n\n<p>In the E-commerce setup, methods like word embeddings (for textual product descriptions), product embeddings (based on their attributes), session data embeddings (that adds up to a user profile), and hybrid models that combine multiple vectorization methods might be the most beneficial, providing the best search accuracy.<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:20px\" aria-hidden=\"true\"><\/div>\n\n\n\n<h3 id=\"gffc42fde3fa2\">Indexing<\/h3>\n\n\n\n<div class=\"wp-block-getwid-section getwid-margin-top-none\" style=\"margin-bottom:-20px\"><div class=\"wp-block-getwid-section__wrapper getwid-padding-top-none getwid-padding-bottom-none getwid-padding-left-none getwid-padding-right-none getwid-margin-left-none getwid-margin-right-none\"><div class=\"wp-block-getwid-section__inner-wrapper\"><div class=\"wp-block-getwid-section__background-holder\"><div class=\"wp-block-getwid-section__background\"><\/div><div class=\"wp-block-getwid-section__foreground\"><\/div><\/div><div class=\"wp-block-getwid-section__content\"><div class=\"wp-block-getwid-section__inner-content\">\n<hr class=\"wp-block-separator has-text-color has-background blog-content-separator\" style=\"background-color:#3de3cf;color:#3de3cf\"\/>\n<\/div><\/div><\/div><\/div><\/div>\n\n\n\n<p>Once you have vector representations for all your items, you need to <a href=\"https:\/\/www.prefixbox.com\/blog\/search-index\/\">index them for efficient retrieval<\/a>.<\/p>\n\n\n\n<p>This is often done using data structures like search trees or more advanced techniques like <a href=\"https:\/\/www.youtube.com\/watch?v=e_SBq3s20M8\" target=\"_blank\" rel=\"noopener\">locality-sensitive hashing (LSH)<\/a> or <a href=\"https:\/\/medium.com\/@brijesh_soni\/the-potential-of-approximate-nearest-neighbors-ann-in-high-dimensional-spaces-579567e4f1a7\" target=\"_blank\" rel=\"noopener\">approximate nearest neighbor (ANN)<\/a> indexes. These data structures enable fast nearest-neighbor searches.<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:20px\" aria-hidden=\"true\"><\/div>\n\n\n\n<h3 id=\"gffc42fde3fa2\">Search Query Vector<\/h3>\n\n\n\n<div class=\"wp-block-getwid-section getwid-margin-top-none\" style=\"margin-bottom:-20px\"><div class=\"wp-block-getwid-section__wrapper getwid-padding-top-none getwid-padding-bottom-none getwid-padding-left-none getwid-padding-right-none getwid-margin-left-none getwid-margin-right-none\"><div class=\"wp-block-getwid-section__inner-wrapper\"><div class=\"wp-block-getwid-section__background-holder\"><div class=\"wp-block-getwid-section__background\"><\/div><div class=\"wp-block-getwid-section__foreground\"><\/div><\/div><div class=\"wp-block-getwid-section__content\"><div class=\"wp-block-getwid-section__inner-content\">\n<hr class=\"wp-block-separator has-text-color has-background blog-content-separator\" style=\"background-color:#3de3cf;color:#3de3cf\"\/>\n<\/div><\/div><\/div><\/div><\/div>\n\n\n\n<p>When a user or system submits a query, it is also vectorized in the same manner as the items. The query vector represents the features or characteristics of the query, which then will be used as a base of similarity to find the relevant products in stock.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" width=\"1024\" height=\"684\" src=\"https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/vector_search_galaxy_detail-1-1024x684.png\" alt=\"Vector Search vector embeddings illustration with a bonsai tree\" class=\"wp-image-6280\" srcset=\"https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/vector_search_galaxy_detail-1-1024x684.png 1024w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/vector_search_galaxy_detail-1-300x200.png 300w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/vector_search_galaxy_detail-1-768x513.png 768w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/vector_search_galaxy_detail-1-1536x1026.png 1536w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/vector_search_galaxy_detail-1-938x626.png 938w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/vector_search_galaxy_detail-1-1130x755.png 1130w, https:\/\/www.prefixbox.com\/blog\/wp-content\/uploads\/2023\/11\/vector_search_galaxy_detail-1.png 1912w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:20px\" aria-hidden=\"true\"><\/div>\n\n\n\n<h3 id=\"gffc42fde3fa2\">Similarity Metrics<\/h3>\n\n\n\n<div class=\"wp-block-getwid-section getwid-margin-top-none\" style=\"margin-bottom:-20px\"><div class=\"wp-block-getwid-section__wrapper getwid-padding-top-none getwid-padding-bottom-none getwid-padding-left-none getwid-padding-right-none getwid-margin-left-none getwid-margin-right-none\"><div class=\"wp-block-getwid-section__inner-wrapper\"><div class=\"wp-block-getwid-section__background-holder\"><div class=\"wp-block-getwid-section__background\"><\/div><div class=\"wp-block-getwid-section__foreground\"><\/div><\/div><div class=\"wp-block-getwid-section__content\"><div class=\"wp-block-getwid-section__inner-content\">\n<hr class=\"wp-block-separator has-text-color has-background blog-content-separator\" style=\"background-color:#3de3cf;color:#3de3cf\"\/>\n<\/div><\/div><\/div><\/div><\/div>\n\n\n\n<p>To find similar items in vector databases, you need a way to measure the similarity between the search query vector and the vectors of items in the dataset. Common similarity metrics include <a href=\"https:\/\/www.geeksforgeeks.org\/cosine-similarity\/\" target=\"_blank\" rel=\"noopener\"><strong>cosine similarity<\/strong><\/a><strong>, Euclidean distance, L2 distance <\/strong>and <a href=\"https:\/\/www.statisticshowto.com\/jaccard-index\/\" target=\"_blank\" rel=\"noopener\"><strong>Jaccard similarity<\/strong>,<\/a> among others.<\/p>\n\n\n\n<p>Closeness in space means more vector similarity, whereas more distance means fewer common characteristics. Businesses using a vector search engine could perform nearest-neighbor searches to determine distance metrics to the closest query-related vectors in a space.&nbsp;<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:20px\" aria-hidden=\"true\"><\/div>\n\n\n\n<h2 id=\"gffc42fde3fa2\">Utilizing the joint power of keyword and vector search<\/h2>\n\n\n\n<div class=\"wp-block-getwid-section getwid-margin-top-none\" style=\"margin-bottom:-20px\"><div class=\"wp-block-getwid-section__wrapper getwid-padding-top-none getwid-padding-bottom-none getwid-padding-left-none getwid-padding-right-none getwid-margin-left-none getwid-margin-right-none\"><div class=\"wp-block-getwid-section__inner-wrapper\"><div class=\"wp-block-getwid-section__background-holder\"><div class=\"wp-block-getwid-section__background\"><\/div><div class=\"wp-block-getwid-section__foreground\"><\/div><\/div><div class=\"wp-block-getwid-section__content\"><div class=\"wp-block-getwid-section__inner-content\">\n<hr class=\"wp-block-separator has-text-color has-background blog-content-separator\" style=\"background-color:#3de3cf;color:#3de3cf\"\/>\n<\/div><\/div><\/div><\/div><\/div>\n\n\n\n<p class=\"paragraph-highlight has-background\" style=\"background-color:#eaf2ff\">When vector search and keyword search results are blended, it\u2019s called a <a href=\"https:\/\/www.prefixbox.com\/en-us\/solutions\/search-suite\/hybrid-search-engine\">hybrid search solution.<\/a><\/p>\n\n\n\n<p>A hybrid search engine that combines keyword-based and vector-based approaches offers a more robust and versatile solution, providing highly relevant search results across a wide range of scenarios.<\/p>\n\n\n\n<p>But how exactly are keyword and vector search results blended?<\/p>\n\n\n\n<p>There are multiple different ways, but here\u2019s how we do it at Prefixbox:<\/p>\n\n\n\n<p>In a vector-based search result, you can still have traditional keyword-based search scores, like<\/p>\n\n\n\n<ul><li>the <strong>matching score<\/strong>, which shows how relevant the result is for a specific search query, and<\/li><li>the <strong>popularity score<\/strong>, which shows how popular a specific search result is.<\/li><\/ul>\n\n\n\n<p>In addition to these scores, the vector-based search results have <strong>similarity scores, <\/strong>which are based on the mathematical representation of the keyword, and its distance from the result in the vector space.<\/p>\n\n\n\n<p class=\"paragraph-highlight has-background\" style=\"background-color:#eaf2ff\"><strong>Blending <\/strong>is the method of merging the result lists. This can be achieved by scaling and normalizing these scores by applying weights in order to get the best ranking.&nbsp;<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:20px\" aria-hidden=\"true\"><\/div>\n\n\n\n<h2 id=\"gffc42fde3fa2\">Summary<\/h2>\n\n\n\n<div class=\"wp-block-getwid-section getwid-margin-top-none\" style=\"margin-bottom:-20px\"><div class=\"wp-block-getwid-section__wrapper getwid-padding-top-none getwid-padding-bottom-none getwid-padding-left-none getwid-padding-right-none getwid-margin-left-none getwid-margin-right-none\"><div class=\"wp-block-getwid-section__inner-wrapper\"><div class=\"wp-block-getwid-section__background-holder\"><div class=\"wp-block-getwid-section__background\"><\/div><div class=\"wp-block-getwid-section__foreground\"><\/div><\/div><div class=\"wp-block-getwid-section__content\"><div class=\"wp-block-getwid-section__inner-content\">\n<hr class=\"wp-block-separator has-text-color has-background blog-content-separator\" style=\"background-color:#3de3cf;color:#3de3cf\"\/>\n<\/div><\/div><\/div><\/div><\/div>\n\n\n\n<p>Vector search is a technique used to find similar items in a large dataset based on their vector representations. Applications span from content recommendation to image retrieval and E-commerce product recommendations.<\/p>\n\n\n\n<p>It&#8217;s the critical element that empowers recommendation systems to deliver more insightful suggestions and fine-tunes search engines for superior performance.&nbsp;<\/p>\n\n\n\n<style type=\"text\/css\">\n  @import url(https:\/\/assets.mlcdn.com\/fonts.css?version=1685515);\n<\/style>\n<style type=\"text\/css\">\n  .ml-form-embedSubmitLoad{display:inline-block;width:20px;height:20px}.g-recaptcha{transform:scale(1);-webkit-transform:scale(1);transform-origin:0 0;-webkit-transform-origin:0 0}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.ml-form-embedSubmitLoad:after{content:\" \";display:block;width:11px;height:11px;margin:1px;border-radius:50%;border:4px solid #fff;border-color:#fff #fff #fff transparent;animation:ml-form-embedSubmitLoad 1.2s linear infinite}@keyframes 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