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</div> </header> <div class="nav-overlay"></div> <div class="algolia-hits" id="hits-block"> <h6 class="algolia-post-title">All Posts</h6> <div id="hits"></div> <button id="see-all-button" class="see-all-button cta-btn bg-black text-white">See All Results</button> </div> <div class="hero-wrapper"> <div class="hero-posts"> <div id="post-15353" class="with-featured-image hero-post post-15353 post type-post status-publish format-standard has-post-thumbnail hentry category-application-design category-best-practices-and-tutorials tag-ai tag-artificial-intelligence tag-embeddings tag-generative-ai tag-vector-search">  <div class="site-header-bg-wrap"> <div class="header-opacity"> <div class="header-gradient"></div> <div class="site-header-bg background-effect" style="background-image: url(https://www.couchbase.com/blog/wp-content/uploads/sites/1/2022/11/image_2022-11-23_113349252.png); opacity: 0.5;"></div> </div> </div> <div class="container hero-container">  <div class="hero-text"> <h1 class="entry-title">What are Vector Embeddings?</h1> <div class="hero-date"> <ul> <li>  <a class="author-profile-avatar" href="https://www.couchbase.com/blog/author/matthew-groves/" title="Posts by Matthew Groves"> <img alt='' src='https://secure.gravatar.com/avatar/ca7f815e5cfadc140b5a78814865a920?s=65&d=mm&r=g' srcset='https://secure.gravatar.com/avatar/ca7f815e5cfadc140b5a78814865a920?s=130&d=mm&r=g 2x' class='avatar avatar-65 photo' height='65' width='65' decoding='async'/> </a> </li> <li> <div class="auth_detail_wrp">  <a class="auth_name" href="https://www.couchbase.com/blog/author/matthew-groves/"> Matthew Groves </a> </div> <div class="date_blog"> <span class="hero-date-span">February 20, 2024</span> </div> </li> </ul> </div> </div> </div> </div> </div> </div> <div id="page" class="hfeed site container"> <div id="content" class="site-content"> <div id="primary" class="content-area"> <main id="main" class="site-main" role="main"> <article id="post-15353" class="post full-post post-15353 type-post status-publish format-standard has-post-thumbnail hentry category-application-design category-best-practices-and-tutorials tag-ai tag-artificial-intelligence tag-embeddings tag-generative-ai tag-vector-search"> <div class="entry-content"> <p><span style="font-weight: 400">Vector embeddings are a critical component in machine learning that convert “high-dimensional” information, such as text or images, into a structured vector space. This process enables the ability to process and identify related data more effectively by representing it as numerical vectors. In this post, you’ll learn how to create vector embeddings, their types, and their deployment in various use cases.</span></p> <h2>Vector Embeddings Explained</h2> <p><span style="font-weight: 400">Vector embeddings are like translating information we understand into something a computer understands. Imagine you鈥檙e trying to explain the concept of “Valentine鈥檚 Day” to a computer. Since computers don鈥檛 understand concepts like holidays, romance, and the cultural context the way we do, we have to translate them into something they DO understand: numbers. That鈥檚 what vector embeddings do. They represent words, pictures, or any kind of data in a list of numbers that represent what those words or images are all about.</span></p> <p><span style="font-weight: 400">For example, with words, if “cat” and “kitten” are similar, when processed through a (large) language model, their number lists (i.e., vectors) will be pretty close together. It鈥檚 not just about words, though. You can do the same thing with photos or other types of media. So, if you have a bunch of pictures of pets, vector embeddings help a computer see which ones are similar, even if it doesn鈥檛 “know” what a cat is.</span></p> <p><span style="font-weight: 400">Let鈥檚 say we鈥檙e turning the words “Valentine鈥檚 Day” into a vector. The string “Valentine鈥檚 Day” would be given to some model, typically an </span><a href="https://www.couchbase.com/blog/large-language-models-explained/"><span style="font-weight: 400">LLM (large language model)</span></a><span style="font-weight: 400">, which would produce an array of numbers to be stored alongside the words.</span></p> <div id="urvanov-syntax-highlighter-67419fe962407249525700" class="urvanov-syntax-highlighter-syntax crayon-theme-classic urvanov-syntax-highlighter-font-monaco urvanov-syntax-highlighter-os-mac print-yes notranslate" data-settings=" minimize scroll-mouseover" style=" margin-top: 12px; margin-bottom: 12px; font-size: 12px !important; line-height: 15px !important;"> <div class="crayon-toolbar" data-settings=" mouseover overlay hide delay" style="font-size: 12px !important;height: 18px !important; line-height: 18px !important;"><span class="crayon-title"></span> <div class="crayon-tools" style="font-size: 12px !important;height: 18px !important; line-height: 18px !important;"><div class="crayon-button urvanov-syntax-highlighter-nums-button" title="Toggle Line Numbers"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-plain-button" title="Toggle Plain Code"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-wrap-button" title="Toggle Line Wrap"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-expand-button" title="Expand Code"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-copy-button" title="Copy"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-popup-button" title="Open Code In New Window"><div class="urvanov-syntax-highlighter-button-icon"></div></div><span class="crayon-language">JavaScript</span></div></div> <div class="crayon-info" style="min-height: 16.8px !important; line-height: 16.8px !important;"></div> <div class="urvanov-syntax-highlighter-plain-wrap"><textarea wrap="soft" class="urvanov-syntax-highlighter-plain print-no" data-settings="dblclick" readonly style="-moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4; font-size: 12px !important; line-height: 15px !important;"> { 聽 "word": "Valentine's Day", 聽 "vector": [0.12, 0.75, -0.33, 0.85, 0.21, ...etc...] }</textarea></div> <div class="urvanov-syntax-highlighter-main" style=""> <table class="crayon-table"> <tr class="urvanov-syntax-highlighter-row"> <td class="crayon-nums " data-settings="hide"> <div class="urvanov-syntax-highlighter-nums-content" style="font-size: 12px !important; line-height: 15px !important;"><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe962407249525700-1">1</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe962407249525700-2">2</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe962407249525700-3">3</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe962407249525700-4">4</div></div> </td> <td class="urvanov-syntax-highlighter-code"><div class="crayon-pre" style="font-size: 12px !important; line-height: 15px !important; -moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4;"><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe962407249525700-1"><span class="crayon-sy">{</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe962407249525700-2">聽<span class="crayon-h"> </span><span class="crayon-s">"word"</span><span class="crayon-o">:</span><span class="crayon-h"> </span><span class="crayon-s">"Valentine's Day"</span><span class="crayon-sy">,</span></div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe962407249525700-3">聽<span class="crayon-h"> </span><span class="crayon-s">"vector"</span><span class="crayon-o">:</span><span class="crayon-h"> </span><span class="crayon-sy">[</span><span class="crayon-cn">0.12</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-cn">0.75</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-o">-</span><span class="crayon-cn">0.33</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-cn">0.85</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-cn">0.21</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-sy">.</span><span class="crayon-sy">.</span><span class="crayon-sy">.</span><span class="crayon-v">etc</span><span class="crayon-sy">.</span><span class="crayon-sy">.</span><span class="crayon-sy">.</span><span class="crayon-sy">]</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe962407249525700-4"><span class="crayon-sy">}</span></div></div></td> </tr> </table> </div> </div>  <p><span style="font-weight: 400">Vectors are very long and complex. For instance, </span><a href="https://platform.openai.com/docs/guides/embeddings/what-are-embeddings"><span style="font-weight: 400">OpenAI鈥檚 vector size</span></a><span style="font-weight: 400"> is typically 1536, which means each embedding is an array of 1536 floating point numbers.</span></p> <p> </p> <p><a href="https://www.couchbase.com/blog/wp-content/uploads/2024/02/image1-1.png"><img fetchpriority="high" decoding="async" class="size-full wp-image-15355 alignnone" src="https://www.couchbase.com/blog/wp-content/uploads/2024/02/image1-1.png" alt="Produce vectors from embedding" width="941" height="365" srcset="https://www.couchbase.com/blog/wp-content/uploads/sites/1/2024/02/image1-1.png 941w, https://www.couchbase.com/blog/wp-content/uploads/sites/1/2024/02/image1-1-300x116.png 300w, https://www.couchbase.com/blog/wp-content/uploads/sites/1/2024/02/image1-1-768x298.png 768w" sizes="(max-width: 941px) 100vw, 941px" /></a></p> <p> </p> <p><span style="font-weight: 400">By itself, this data doesn鈥檛 really mean much: it鈥檚 all about finding other embeddings that are </span><i><span style="font-weight: 400">close</span></i><span style="font-weight: 400">.</span></p> <p> </p> <p><span style="font-weight: 400"><a href="https://www.couchbase.com/blog/wp-content/uploads/2024/02/image2-1.png"><img decoding="async" class="size-large wp-image-15354 alignnone" src="https://www.couchbase.com/blog/wp-content/uploads/2024/02/image2-1-1024x408.png" alt="Produce vectors from embedding" width="900" height="359" srcset="https://www.couchbase.com/blog/wp-content/uploads/sites/1/2024/02/image2-1-1024x408.png 1024w, https://www.couchbase.com/blog/wp-content/uploads/sites/1/2024/02/image2-1-300x120.png 300w, https://www.couchbase.com/blog/wp-content/uploads/sites/1/2024/02/image2-1-768x306.png 768w, https://www.couchbase.com/blog/wp-content/uploads/sites/1/2024/02/image2-1.png 1028w" sizes="(max-width: 900px) 100vw, 900px" /></a></span></p> <p><span style="font-weight: 400">In this diagram, a nearest neighbor algorithm can find data with vectors </span><i><span style="font-weight: 400">close</span></i><span style="font-weight: 400"> to the vectorized query. These results are returned in a list (ordered by their proximity).</span></p> <h2>Types of Vector Embeddings</h2> <p><span style="font-weight: 400">There are several types of embeddings, each with its unique way of understanding and representing data. Here鈥檚 a rundown of the main types you might come across:</span></p> <p><b>Word Embeddings</b><span style="font-weight: 400">: Word embeddings translate single words into vectors, capturing the essence of their meaning. Popular models like Word2Vec, GloVe, and FastText are used to create these embeddings. These can help to show the relationship between words, like understanding that “king” and “queen” are related in the same way as “man” and “woman.”</span></p> <p><span style="font-weight: 400">Here鈥檚 an example of Word2Vec in action:</span></p> <div id="urvanov-syntax-highlighter-67419fe96240e161559876" class="urvanov-syntax-highlighter-syntax crayon-theme-classic urvanov-syntax-highlighter-font-monaco urvanov-syntax-highlighter-os-mac print-yes notranslate" data-settings=" minimize scroll-mouseover" style=" margin-top: 12px; margin-bottom: 12px; font-size: 12px !important; line-height: 15px !important;"> <div class="crayon-toolbar" data-settings=" mouseover overlay hide delay" style="font-size: 12px !important;height: 18px !important; line-height: 18px !important;"><span class="crayon-title"></span> <div class="crayon-tools" style="font-size: 12px !important;height: 18px !important; line-height: 18px !important;"><div class="crayon-button urvanov-syntax-highlighter-nums-button" title="Toggle Line Numbers"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-plain-button" title="Toggle Plain Code"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-wrap-button" title="Toggle Line Wrap"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-expand-button" title="Expand Code"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-copy-button" title="Copy"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-popup-button" title="Open Code In New Window"><div class="urvanov-syntax-highlighter-button-icon"></div></div><span class="crayon-language">Python</span></div></div> <div class="crayon-info" style="min-height: 16.8px !important; line-height: 16.8px !important;"></div> <div class="urvanov-syntax-highlighter-plain-wrap"><textarea wrap="soft" class="urvanov-syntax-highlighter-plain print-no" data-settings="dblclick" readonly style="-moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4; font-size: 12px !important; line-height: 15px !important;"> from gensim.models import Word2Vec sentences = [ 聽聽 "Couchbase is a distributed NoSQL database.", 聽聽 "Couchbase Capella provides flexibility and scalability.", 聽聽 "Couchbase supports SQL++ for querying JSON documents.", 聽聽 "Couchbase Mobile extends the database to the edge.", 聽聽 "Couchbase has a built-in Full Text Search Engine" ] # Preprocess the sentences: tokenize and lower case processed_sentences = [sentence.lower().split() for sentence in sentences] # Train the Word2Vec model model = Word2Vec(sentences=processed_sentences, vector_size=100, window=5, min_count=1, workers=4) # Get the vector for a word word_vector = model.wv['couchbase'] # Print the vector print(word_vector) This Python code would output something like: [-0.00053675, 0.000236998, 0.00510486, 0.00900848, ..., 0.000901757, 0.00639282]</textarea></div> <div class="urvanov-syntax-highlighter-main" style=""> <table class="crayon-table"> <tr class="urvanov-syntax-highlighter-row"> <td class="crayon-nums " data-settings="hide"> <div class="urvanov-syntax-highlighter-nums-content" style="font-size: 12px !important; line-height: 15px !important;"><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-1">1</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-2">2</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-3">3</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-4">4</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-5">5</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-6">6</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-7">7</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-8">8</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-9">9</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-10">10</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-11">11</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-12">12</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-13">13</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-14">14</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-15">15</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-16">16</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-17">17</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-18">18</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-19">19</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-20">20</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-21">21</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-22">22</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-23">23</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-24">24</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe96240e161559876-25">25</div></div> </td> <td class="urvanov-syntax-highlighter-code"><div class="crayon-pre" style="font-size: 12px !important; line-height: 15px !important; -moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4;"><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-1"><span class="crayon-st">from</span><span class="crayon-h"> </span><span class="crayon-v">gensim</span><span class="crayon-sy">.</span><span class="crayon-e">models </span><span class="crayon-r">import</span><span class="crayon-h"> </span><span class="crayon-e">Word2Vec</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-2"> </div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-3"><span class="crayon-v">sentences</span><span class="crayon-h"> </span><span class="crayon-o">=</span><span class="crayon-h"> </span><span class="crayon-sy">[</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-4">聽<span class="crayon-h"> </span>聽<span class="crayon-h"> </span><span class="crayon-s">"Couchbase is a distributed NoSQL database."</span><span class="crayon-sy">,</span></div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-5">聽<span class="crayon-h"> </span>聽<span class="crayon-h"> </span><span class="crayon-s">"Couchbase Capella provides flexibility and scalability."</span><span class="crayon-sy">,</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-6">聽<span class="crayon-h"> </span>聽<span class="crayon-h"> </span><span class="crayon-s">"Couchbase supports SQL++ for querying JSON documents."</span><span class="crayon-sy">,</span></div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-7">聽<span class="crayon-h"> </span>聽<span class="crayon-h"> </span><span class="crayon-s">"Couchbase Mobile extends the database to the edge."</span><span class="crayon-sy">,</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-8">聽<span class="crayon-h"> </span>聽<span class="crayon-h"> </span><span class="crayon-s">"Couchbase has a built-in Full Text Search Engine"</span></div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-9"><span class="crayon-sy">]</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-10"> </div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-11"><span class="crayon-c"># Preprocess the sentences: tokenize and lower case</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-12"><span class="crayon-v">processed_sentences</span><span class="crayon-h"> </span><span class="crayon-o">=</span><span class="crayon-h"> </span><span class="crayon-sy">[</span><span class="crayon-v">sentence</span><span class="crayon-sy">.</span><span class="crayon-e">lower</span><span class="crayon-sy">(</span><span class="crayon-sy">)</span><span class="crayon-sy">.</span><span class="crayon-e">split</span><span class="crayon-sy">(</span><span class="crayon-sy">)</span><span class="crayon-h"> </span><span class="crayon-st">for</span><span class="crayon-h"> </span><span class="crayon-e">sentence </span><span class="crayon-st">in</span><span class="crayon-h"> </span><span class="crayon-v">sentences</span><span class="crayon-sy">]</span></div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-13"> </div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-14"><span class="crayon-c"># Train the Word2Vec model</span></div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-15"><span class="crayon-v">model</span><span class="crayon-h"> </span><span class="crayon-o">=</span><span class="crayon-h"> </span><span class="crayon-e">Word2Vec</span><span class="crayon-sy">(</span><span class="crayon-v">sentences</span><span class="crayon-o">=</span><span class="crayon-v">processed_sentences</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-v">vector_size</span><span class="crayon-o">=</span><span class="crayon-cn">100</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-v">window</span><span class="crayon-o">=</span><span class="crayon-cn">5</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-v">min_count</span><span class="crayon-o">=</span><span class="crayon-cn">1</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-v">workers</span><span class="crayon-o">=</span><span class="crayon-cn">4</span><span class="crayon-sy">)</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-16"> </div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-17"><span class="crayon-c"># Get the vector for a word</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-18"><span class="crayon-v">word_vector</span><span class="crayon-h"> </span><span class="crayon-o">=</span><span class="crayon-h"> </span><span class="crayon-v">model</span><span class="crayon-sy">.</span><span class="crayon-v">wv</span><span class="crayon-sy">[</span><span class="crayon-s">'couchbase'</span><span class="crayon-sy">]</span></div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-19"> </div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-20"><span class="crayon-c"># Print the vector</span></div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-21"><span class="crayon-k ">print</span><span class="crayon-sy">(</span><span class="crayon-v">word_vector</span><span class="crayon-sy">)</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-22"> </div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-23"><span class="crayon-e">This </span><span class="crayon-e">Python </span><span class="crayon-k ">code</span><span class="crayon-h"> </span><span class="crayon-e">would </span><span class="crayon-e">output </span><span class="crayon-e">something </span><span class="crayon-v">like</span><span class="crayon-o">:</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-24"> </div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe96240e161559876-25"><span class="crayon-sy">[</span><span class="crayon-o">-</span><span class="crayon-cn">0.00053675</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-cn">0.000236998</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-cn">0.00510486</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-cn">0.00900848</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-sy">.</span><span class="crayon-sy">.</span><span class="crayon-sy">.</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-cn">0.000901757</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-cn">0.00639282</span><span class="crayon-sy">]</span></div></div></td> </tr> </table> </div> </div>  <p><b>Sentence and Document Embeddings</b><span style="font-weight: 400">: Moving beyond single words, sentence and document embeddings represent larger pieces of text. These embeddings can capture the context of an entire sentence or document, not just individual words. Models like BERT and Doc2Vec are good examples. They鈥檙e used in tasks that require understanding the overall message, sentiment, or topic of texts.</span></p> <p><b>Image Embeddings</b><span style="font-weight: 400">: These convert images into vectors, capturing visual features like shapes, colors, and textures. Image embeddings are created using deep learning models (like </span><a href="https://en.wikipedia.org/wiki/Convolutional_neural_network"><span style="font-weight: 400">CNNs: Convolutional Neural Networks</span></a><span style="font-weight: 400">). They enable tasks such as image recognition, classification, and similarity searches. For example, an image embedding might help a computer recognize whether a given picture is a hot dog or not.</span></p> <p><b>Graph Embeddings</b><span style="font-weight: 400">: Graph embeddings are used to represent relationships and structures, such as social networks, org charts, or biological pathways. They turn the nodes and edges of a graph into vectors, capturing how items are connected. This is useful for recommendations, clustering, and detecting communities (clusters) within networks.</span></p> <p><b>Audio Embeddings</b><span style="font-weight: 400">: Similar to image embeddings, audio embeddings translate sound into vectors, capturing features like pitch, tone, and rhythm. These are used in voice recognition, music analysis, and sound classification tasks.</span></p> <p><b>Video Embeddings</b><span style="font-weight: 400">: Video embeddings capture both the visual and temporal dynamics of videos. They鈥檙e used for activities like video search, classification, and understanding scenes or activities within the footage.</span></p> <h2>How to Create Vector Embeddings</h2> <p><span style="font-weight: 400">Generally speaking, there are four steps:</span></p> <ol> <li style="list-style-type: none"> <ol> <li style="font-weight: 400"><b>Choose Your Vector Embedding Model</b><span style="font-weight: 400">: Decide on the type of model based on your needs. Word2Vec, GloVe, and FastText are popular for word embeddings, while BERT and GPT-4 are used for sentence and document embeddings, etc.</span></li> <li style="font-weight: 400"><b>Prepare Your Data</b><span style="font-weight: 400">: Clean and preprocess your data. For text, this can include tokenization, removing “stopwords,” and possibly lemmatization (reducing words to their base form). For images, this might include resizing, normalizing pixel values, etc.</span></li> <li style="font-weight: 400"><b>Train or Use Pre-trained Models</b><span style="font-weight: 400">: You can train your model on your dataset or use a pre-trained model. Training from scratch requires a significant amount of data, time, and computational resources. Pre-trained models are a quick way to get started and can be fine-tuned (or augmented) with your specific dataset.</span></li> <li style="font-weight: 400"><b>Generate Embeddings</b><span style="font-weight: 400">: Once your model is ready, feed your data through it (via SDK, REST, etc.) to generate embeddings. Each item will be transformed into a vector that represents its semantic meaning. Typically, the embeddings are stored in a database, sometimes right alongside the original data.</span></li> </ol> </li> </ol> <h2>Applications of Vector Embeddings</h2> <p><span style="font-weight: 400">So, what鈥檚 the big deal with vector? What problems can I attack with it? Here are several use cases that are enabled by using vector embeddings to find semantically similar items (i.e., “vector search”):</span></p> <h3>Natural Language Processing (NLP)</h3> <ul> <li style="list-style-type: none"> <ul> <li style="font-weight: 400"><b>Semantic Search</b><span style="font-weight: 400">: Improving search relevance and user experience by better utilizing the meaning behind search terms, above and beyond traditional text-based searching.</span></li> <li style="font-weight: 400"><b>Sentiment Analysis</b><span style="font-weight: 400">: Analyzing customer feedback, social media posts, and reviews to gauge sentiment (positive, negative, or neutral).</span></li> <li style="font-weight: 400"><b>Language Translation</b><span style="font-weight: 400">: Understanding the semantics of the source language and generating appropriate text in the target language.</span></li> </ul> </li> </ul> <h3>Recommendation Systems</h3> <ul> <li style="list-style-type: none"> <ul> <li style="font-weight: 400"><a href="https://www.couchbase.com/blog/machine-learning-predictions-couchbase-lite-predictive-query/"><b>E-commerce</b></a><span style="font-weight: 400">: Personalizing product recommendations based on browsing and purchase history.</span></li> <li style="font-weight: 400"><b>Content Platforms</b><span style="font-weight: 400">: Recommending content to users based on their interests and past interactions.</span></li> </ul> </li> </ul> <h3>Computer Vision</h3> <ul> <li style="list-style-type: none"> <ul> <li style="font-weight: 400"><b>Image Recognition and Classification</b><span style="font-weight: 400">: Identifying objects, people, or scenes in images for applications like surveillance, tagging photos, identifying parts, etc.</span></li> <li style="font-weight: 400"><b>Visual Search</b><span style="font-weight: 400">: Enabling users to search with images instead of text queries.</span></li> </ul> </li> </ul> <h3>Healthcare</h3> <ul> <li style="list-style-type: none"> <ul> <li style="font-weight: 400"><b>Drug Discovery</b><span style="font-weight: 400">: Helping to identify interactions.</span></li> <li style="font-weight: 400"><b>Medical Image Analysis</b><span style="font-weight: 400">: Diagnosing diseases by analyzing medical images such as X-rays, MRIs, and CT scans.</span></li> </ul> </li> </ul> <h3>Finance</h3> <ul> <li style="list-style-type: none"> <ul> <li style="font-weight: 400"><a href="https://www.couchbase.com/blog/couchbase-ai-ml-fraud-detection/"><b>Fraud Detection</b></a><span style="font-weight: 400">: Analyzing transaction patterns to identify and prevent fraudulent activities.</span></li> <li style="font-weight: 400"><b>Credit Scoring</b><span style="font-weight: 400">: Analyzing financial history and behavior.</span></li> </ul> </li> </ul> <h3>Retrieval-Augmented Generation (RAG)</h3> <p><span style="font-weight: 400"><a href="https://www.couchbase.com/blog/what-are-vector-embeddings/">Retrieval-Augmented Generation</a> is an approach that combines the strengths of pre-trained </span><a href="https://www.couchbase.com/blog/what-is-generative-ai/"><span style="font-weight: 400">generative language models</span></a><span style="font-weight: 400"> (like GPT-4) with information retrieval capabilities (like vector search) to enhance the generation of responses.</span></p> <p><span style="font-weight: 400">RAG can augment a query to an LLM like GPT-4 with up-to-date and relevant domain information. There are two steps:</span></p> <ol> <li style="list-style-type: none"> <ol> <li style="font-weight: 400"><span style="font-weight: 400">Query for relevant documents.<br /> </span>Vector search is particularly good at identifying relevant data, but any querying can work, including analytical queries that <a href="https://www.couchbase.com/blog/couchbase-capella-columnar/">Couchbase columnar</a> makes possible.</li> <li style="font-weight: 400"><span style="font-weight: 400">Pass the results of the query as context to the generative model, along with the query itself.</span></li> </ol> </li> </ol> <ol> <li style="list-style-type: none"></li> </ol> <p><span style="font-weight: 400">This approach allows the model to produce more informative, accurate, and contextually relevant answers.</span></p> <p><span style="font-weight: 400">Use cases for RAG include:</span></p> <ul> <li style="list-style-type: none"> <ul> <li style="font-weight: 400"><b>Question Answering</b><span style="font-weight: 400">: Unlike closed-domain systems that rely on a fixed dataset, RAG can access up-to-date information from its knowledge source.</span></li> <li style="font-weight: 400"><b>Content Creation</b><span style="font-weight: 400">: RAG can augment content with relevant facts and figures, ensuring better accuracy.</span></li> <li style="font-weight: 400"><b>Chatbots/Assistants</b><span style="font-weight: 400">: Bots like </span><a href="https://www.couchbase.com/ai-cloud-services/"><span style="font-weight: 400">Couchbase Capella iQ</span></a><span style="font-weight: 400"> can provide more detailed and informative responses across a wide range of topics.</span></li> <li style="font-weight: 400"><b>Educational Tools</b><span style="font-weight: 400">: RAG can provide detailed explanations or supplemental information on a wide array of subjects tailored to the user鈥檚 queries.</span></li> <li style="font-weight: 400"><b>Recommendation Systems</b><span style="font-weight: 400">: RAG can generate personalized explanations or reasons behind recommendations by retrieving relevant information that matches the user鈥檚 interests or query context.</span></li> </ul> </li> </ul> <h2>Vector Embeddings and Couchbase</h2> <p><span style="font-weight: 400">Couchbase is a multi-purpose database that excels in managing JSON data. This flexibility applies to vector embeddings, as Couchbase鈥檚 schemaless nature allows for the efficient storage and retrieval of complex, multi-dimensional vector data alongside traditional JSON documents (as shown earlier in this blog post)</span></p> <div id="urvanov-syntax-highlighter-67419fe962411935304295" class="urvanov-syntax-highlighter-syntax crayon-theme-classic urvanov-syntax-highlighter-font-monaco urvanov-syntax-highlighter-os-mac print-yes notranslate" data-settings=" minimize scroll-mouseover" style=" margin-top: 12px; margin-bottom: 12px; font-size: 12px !important; line-height: 15px !important;"> <div class="crayon-toolbar" data-settings=" mouseover overlay hide delay" style="font-size: 12px !important;height: 18px !important; line-height: 18px !important;"><span class="crayon-title"></span> <div class="crayon-tools" style="font-size: 12px !important;height: 18px !important; line-height: 18px !important;"><div class="crayon-button urvanov-syntax-highlighter-nums-button" title="Toggle Line Numbers"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-plain-button" title="Toggle Plain Code"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-wrap-button" title="Toggle Line Wrap"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-expand-button" title="Expand Code"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-copy-button" title="Copy"><div class="urvanov-syntax-highlighter-button-icon"></div></div><div class="crayon-button urvanov-syntax-highlighter-popup-button" title="Open Code In New Window"><div class="urvanov-syntax-highlighter-button-icon"></div></div><span class="crayon-language">JavaScript</span></div></div> <div class="crayon-info" style="min-height: 16.8px !important; line-height: 16.8px !important;"></div> <div class="urvanov-syntax-highlighter-plain-wrap"><textarea wrap="soft" class="urvanov-syntax-highlighter-plain print-no" data-settings="dblclick" readonly style="-moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4; font-size: 12px !important; line-height: 15px !important;"> { 聽 "word": "Valentine's Day", 聽 "vector": [0.12, 0.75, -0.33, 0.85, 0.21, ...etc...] }</textarea></div> <div class="urvanov-syntax-highlighter-main" style=""> <table class="crayon-table"> <tr class="urvanov-syntax-highlighter-row"> <td class="crayon-nums " data-settings="hide"> <div class="urvanov-syntax-highlighter-nums-content" style="font-size: 12px !important; line-height: 15px !important;"><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe962411935304295-1">1</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe962411935304295-2">2</div><div class="crayon-num" data-line="urvanov-syntax-highlighter-67419fe962411935304295-3">3</div><div class="crayon-num crayon-striped-num" data-line="urvanov-syntax-highlighter-67419fe962411935304295-4">4</div></div> </td> <td class="urvanov-syntax-highlighter-code"><div class="crayon-pre" style="font-size: 12px !important; line-height: 15px !important; -moz-tab-size:4; -o-tab-size:4; -webkit-tab-size:4; tab-size:4;"><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe962411935304295-1"><span class="crayon-sy">{</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe962411935304295-2">聽<span class="crayon-h"> </span><span class="crayon-s">"word"</span><span class="crayon-o">:</span><span class="crayon-h"> </span><span class="crayon-s">"Valentine's Day"</span><span class="crayon-sy">,</span></div><div class="crayon-line" id="urvanov-syntax-highlighter-67419fe962411935304295-3">聽<span class="crayon-h"> </span><span class="crayon-s">"vector"</span><span class="crayon-o">:</span><span class="crayon-h"> </span><span class="crayon-sy">[</span><span class="crayon-cn">0.12</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-cn">0.75</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-o">-</span><span class="crayon-cn">0.33</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-cn">0.85</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-cn">0.21</span><span class="crayon-sy">,</span><span class="crayon-h"> </span><span class="crayon-sy">.</span><span class="crayon-sy">.</span><span class="crayon-sy">.</span><span class="crayon-v">etc</span><span class="crayon-sy">.</span><span class="crayon-sy">.</span><span class="crayon-sy">.</span><span class="crayon-sy">]</span></div><div class="crayon-line crayon-striped-line" id="urvanov-syntax-highlighter-67419fe962411935304295-4"><span class="crayon-sy">}</span></div></div></td> </tr> </table> </div> </div>  <p><span style="font-weight: 400">Couchbase鈥檚 strength lies in its ability to handle a wide range of data types and use cases within a single platform, contrasting with specialized, single-purpose </span><a href="https://www.couchbase.com/blog/vector-databases/"><span style="font-weight: 400">vector databases</span></a><span style="font-weight: 400"> (like Pinecone) focused solely on vector search and similarity. Benefits of Couchbase鈥檚 approach include:</span></p> <p style="padding-left: 40px"><b>Hybrid Query</b><span style="font-weight: 400">: With Couchbase, you can combine SQL++, key/value, geospatial, and full-text search into a single query to reduce post-query processing and more quickly build a rich set of application features.</span></p> <p style="padding-left: 40px"><b>Versatility</b><span style="font-weight: 400">: Couchbase supports key-value, document, and full-text search, as well as real-time analytics and eventing, all within the same platform. This versatility allows developers to use vector embeddings for advanced search and recommendation features without needing a separate system.</span></p> <p style="padding-left: 40px"><b>Scalability and Performance</b><span style="font-weight: 400">: Designed for high performance and scalability, Couchbase ensures that applications using vector embeddings can scale out efficiently to meet growing data and traffic demands.</span></p> <p style="padding-left: 40px"><b>Unified Development Experience</b><span style="font-weight: 400">: Consolidating data use cases into Couchbase simplifies the development process. Teams can focus on building features rather than managing multiple databases, integrations, and data pipelines.</span></p> <h2>Next Steps</h2> <p><span style="font-weight: 400">Give </span><a href="https://www.couchbase.com/products/capella/"><span style="font-weight: 400">Couchbase Capella</span></a><span style="font-weight: 400"> a try, and see how a multi-purpose database can help you build powerful, adaptive applications. You can also <a href="https://www.couchbase.com/downloads/?family=couchbase-server">download</a> the on-prem server version of Couchbase Server 7.6, complete with vector search integration.</span></p> <p><span style="font-weight: 400">You can get up and running in minutes with a free trial (no credit card needed). </span><a href="https://www.couchbase.com/ai-cloud-services/"><span style="font-weight: 400">Capella iQ鈥檚 generative AI</span></a><span style="font-weight: 400"> is built right in and can help you start writing your first queries.</span></p> <h2>Vector Embedding FAQs</h2> <p><b>What is the difference between text vectorization and embedding?</b></p> <p><span style="font-weight: 400">Text vectorization is a way to count the occurrences of words in a document. Embedding represents the semantic meaning of words and their context.</span></p> <p><b>What is the difference between indexing and embedding?</b></p> <p><span style="font-weight: 400">Embedding is the process of generating the vectors. Indexing is the process that enables the retrieval of the vectors and their neighbors.</span></p> <p><b>What content types can be embedded?</b></p> <p><span style="font-weight: 400">Words, text, images, documents, audio, video, graphs, networks, etc.</span></p> <p><b>How do vector embeddings support generative AI?</b></p> <p><span style="font-weight: 400">Vector embeddings can be used to find context to augment the generation of responses. See the above section on RAG.</span></p> <p><b>What are embeddings in machine learning?</b></p> <p><span style="font-weight: 400">A mathematical representation of data used to represent the data compactly and to find similarities between data.</span></p>  <div class="pp-multiple-authors-boxes-wrapper pp-multiple-authors-wrapper pp-multiple-authors-layout-simple_list multiple-authors-target-the-content box-post-id-14469 box-instance-id-1 ppma_boxes_14469" data-post_id="14469" data-instance_id="1" data-additional_class="pp-multiple-authors-layout-simple_list.multiple-authors-target-the-content" data-original_class="pp-multiple-authors-boxes-wrapper pp-multiple-authors-wrapper box-post-id-14469 box-instance-id-1"> <span class="ppma-layout-prefix"></span> <div class="ppma-author-category-wrap"> <span class="ppma-category-group ppma-category-group-1 category-index-0"> <ul class="pp-multiple-authors-boxes-ul author-ul-0"> <li class="pp-multiple-authors-boxes-li author_index_0 author_matthew-groves has-avatar"> <div class="pp-author-boxes-avatar"> <div class="avatar-image"> <img alt='Matthew Groves' src='https://secure.gravatar.com/avatar/ca7f815e5cfadc140b5a78814865a920?s=35&d=mm&r=g' srcset='https://secure.gravatar.com/avatar/ca7f815e5cfadc140b5a78814865a920?s=70&d=mm&r=g 2x' class='avatar avatar-35 photo' height='35' width='35' /> </div> </div> <div class="pp-author-boxes-avatar-details"> <div class="pp-author-boxes-name multiple-authors-name"><a href="https://www.couchbase.com/blog/author/matthew-groves/" rel="author" title="Matthew Groves" class="author url fn">Matthew Groves</a></div> </div> </li> </ul> </span> </div> <span class="ppma-layout-suffix"></span> </div>  <style> .pp-multiple-authors-boxes-wrapper.pp-multiple-authors-layout-simple_list.multiple-authors-target-the-content .pp-author-boxes-avatar img { width: 35px !important; height: 35px !important; } 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