What is Cosine Similarity? A Comprehensive Guide

link管理

链接快照平台

输入网页链接，自动生成快照
标签化管理网页链接

相关文章推荐

安静的香菇 · Accueil − Insee − ...· 2 月前 ·

博学的墨镜 · 老吕弟子班考研MBA管理类联考视频百度云-华 ...· 4 月前 ·

温暖的便当 · IL-10 x insulin - ...· 4 月前 ·

从未表白的煎饼果子 · 神学· 4 月前 ·

温柔的苹果 · XML 命名空间 | 菜鸟教程· 4 月前 ·

</noscript><header class="styles_header__sOX2g "><div class="jsx-3354437287 styles_navbar__yB1B_"><a class="styles_logo__v62C4" title="DataStax Homepage" href="/"><svg width="10rem" height="1rem" viewbox="0 0 161 15" fill="none" xmlns="http://www.w3.org/2000/svg"><g><path d="M80.6844 14.9991V11.7853H94.1512V9.06231H83.6346L80.0166 6.4078V2.65451L83.6346 0H96.5273V3.21375H83.3959V5.84856H93.9125L97.5295 8.50213V12.3455L93.9125 14.9991H80.6844Z" fill="white"/><path d="M36.843 14.9991L30.2354 4.10516L23.6169 14.9991H19.7148L28.8229 0H31.637L40.7451 14.9991H36.843Z" fill="white"/><path d="M47.0761 14.9991V3.21375H40.0088V0H57.5216V3.21375H50.4544V14.9991H47.0761Z" fill="white"/><path d="M107.965 14.9991V3.21375H100.897V0H118.411V3.21375H111.344V14.9991H107.965Z" fill="white"/><path d="M0 14.9991V0H13.8959L17.5138 2.65451V12.3455L13.8959 15H0V14.9991ZM14.1346 11.7853V3.21375H3.37829V11.7853H14.1346Z" fill="white"/><path d="M140.604 14.9991L145.158 7.5L140.604 0H144.506L149.062 7.5L144.506 14.9991H140.604Z" fill="white"/><path d="M156.963 14.9991L152.406 7.5L156.963 0H160.864L156.31 7.5L160.864 14.9991H156.963Z" fill="white"/><path d="M73.9143 14.9991L67.2958 4.10516L60.6872 14.9991H56.7852L65.8932 0H68.7073L77.8154 14.9991H73.9143Z" fill="white"/><path d="M134.803 14.9991L128.185 4.10516L121.577 14.9991H117.675L126.783 0H129.597L138.705 14.9991H134.803Z" fill="white"/></g></svg></a><button class="styles_button__Y3Rmo styles_button--icon__ueDQd styles_button--color-reverse__gOooD styles_toggler__h1aNM" title="Toggle Navbar"><svg fill="none" height="40" viewbox="0 0 48 48" width="40" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M40 17H8V15H40V17Z" fill="currentColor"/><path fill-rule="evenodd" clip-rule="evenodd" d="M40 25H8V23H40V25Z" fill="currentColor"/><path fill-rule="evenodd" clip-rule="evenodd" d="M40 33H8V31H40V33Z" fill="currentColor"/></svg></button><div class="jsx-3354437287 styles_nav__7qrBE"><nav class="jsx-3354437287 styles_main__TkF0P"><button class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_button--uppercase__UQqQH styles_button__rtsAV" id="products_button_test_id"><span>Products</span></button><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_button--uppercase__UQqQH styles_button__rtsAV" id="pricing_button_test_id" href="/pricing"><span>Pricing</span></a><button class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_button--uppercase__UQqQH styles_button__rtsAV" id="stories_button_test_id"><span>Stories</span></button><button class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_button--uppercase__UQqQH styles_button__rtsAV" id="resources_button_test_id"><span>Resources</span></button></nav><nav class="jsx-3354437287 styles_secondary__lS7u4"><a id="docs_button_test_id" href="https://docs.datastax.com/en/home/index.html">Docs</a><a id="contact_us_button_test_id" href="/contact-us">Contact Us</a><a id="sign_in_button_test_id" href="https://astra.datastax.com/">Sign In</a><div class="styles_search__YYoeH"><form action="/search" method="get"><input class="form-control-plaintext" type="text" placeholder="Search DataStax"/></form><button class="styles_button__Y3Rmo styles_button--icon__ueDQd styles_button--color-reverse__gOooD styles_toggler__e4JcK" type="button" title="Search"><svg fill="none" height="16" viewbox="0 0 48 48" width="16" xmlns="http://www.w3.org/2000/svg"><circle cx="19.5" cy="19.5" r="9.5" stroke="currentColor" stroke-width="2"/><path d="M26 26L35.5 35.5" stroke="currentColor" stroke-width="2"/></svg></button></div></nav><nav class="jsx-3354437287 styles_ctas__7eq3j"><a href="https://astra.datastax.com/" class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_button--uppercase__UQqQH"><span>Sign In</span><svg width="6" height="13" viewbox="0 0 6 13" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M2.16196 0.792358L5.45785 6.5021L2.16196 12.2058L0 12.2076L3.29589 6.5021L0 0.792358H2.16196Z" fill="currentColor"/></svg></a><a href="https://astra.datastax.com/signup" class="styles_button__Y3Rmo styles_button--primary__44UCv styles_button--color-reverse__gOooD styles_button--uppercase__UQqQH"><span>Try For Free</span></a></nav></div></div><div class="jsx-2134556869 styles_announcement__Cj8Oc styles_hidden__COyb3"><div class="jsx-2134556869 styles_announcement__content__mrC_t"><p><a href="https://www.datastax.com/blog/simplifying-vector-embedding-generation-with-astra-vectorize"><strong>DataStax Astra Vectorize: Generate Vector Embeddings with 1 Line of Code</strong></a></p></div><button type="button" title="Dismiss Announcement bar" class="jsx-2134556869 styles_dismiss__c_LI4"><svg fill="none" height="24" viewbox="0 0 48 48" width="24" xmlns="http://www.w3.org/2000/svg"><path d="M35.5 12.5L12.5 35.5" stroke="currentColor" stroke-width="2"/><path d="M35.5 35.5L12.5 12.5" stroke="currentColor" stroke-width="2"/></svg></button></div></header><main class="undefined " id="page_container_test_id"><div><div class="styles_breadcrumb__QVgdE"><div class="container"><div class="row"><div class="col"><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-regular__WBzQT styles_button__UMG2O" href="/resources"><span>Back to Resources</span></a></div></div></div></div><section class="styles_hero__7ieyu"><div class="container"><div class="row"><div class="col-lg-6"><span class="styles_eyebrow__tOueC styles_eyebrow--size-100__mWpUx styles_info__C_2Pc"><span>Guide</span><span> • </span><span>Sep 29, 2023</span></span><h1 class="styles_display__cQk8b styles_display--size-400__HV_pT styles_display--weight-600__cx95q">What is Cosine Similarity: A Comprehensive Guide</h1><div class="styles_text__LSXNx styles_text--size-500___TVT0 styles_text--weight-400__XgWIb styles_content__d1Qid"><p><strong>In data analysis and NLP, cosine similarity is invaluable for tasks like text mining and sentiment analysis. But what exactly is it and how does it work?</strong></p></div><a href="https://astra.datastax.com/signup" class="styles_button__Y3Rmo styles_button--secondary__W1Pwy styles_button--color-regular__WBzQT styles_button--uppercase__UQqQH"><span>Sign Up for Astra</span></a><div class="styles_authors__W88JH"><div><img alt="Phil Miesle" loading="lazy" width="48" height="48" decoding="async" data-nimg="1" style="color:transparent" srcset="https://cdn.sanity.io/images/bbnkhnhl/production/d227884465bf6e9abf39c596523bbb0e930c7df9-400x400.jpg?w=48&amp;q=75&amp;fit=clip&amp;auto=format 1x, https://cdn.sanity.io/images/bbnkhnhl/production/d227884465bf6e9abf39c596523bbb0e930c7df9-400x400.jpg?w=96&amp;q=75&amp;fit=clip&amp;auto=format 2x" src="https://cdn.sanity.io/images/bbnkhnhl/production/d227884465bf6e9abf39c596523bbb0e930c7df9-400x400.jpg?w=96&amp;q=75&amp;fit=clip&amp;auto=format"/><div><h6 class="styles_display__cQk8b styles_display--size-200__VVui5 styles_display--weight-600__cx95q">Phil Miesle</h6><h6 class="styles_text__LSXNx styles_text--size-200__t4kFW styles_text--weight-400__XgWIb">AI Developer Advocate </h6></div></div></div></div><div class="col-lg-6"><img alt="" loading="lazy" width="1104" height="640" decoding="async" data-nimg="1" class="styles_hero__image__qmzD3" style="color:transparent" srcset="https://cdn.sanity.io/images/bbnkhnhl/production/d83c32aada00637541dcaa76e55eb2d4e67f92dc-1104x640.jpg?w=1200&amp;q=75&amp;fit=clip&amp;auto=format 1x, https://cdn.sanity.io/images/bbnkhnhl/production/d83c32aada00637541dcaa76e55eb2d4e67f92dc-1104x640.jpg?w=3840&amp;q=75&amp;fit=clip&amp;auto=format 2x" src="https://cdn.sanity.io/images/bbnkhnhl/production/d83c32aada00637541dcaa76e55eb2d4e67f92dc-1104x640.jpg?w=3840&amp;q=75&amp;fit=clip&amp;auto=format"/></div></div><div class="row"><div class="col"><div class="styles_divider__n80m6"/></div></div></div></section><div class="container"><div class="row"><div class="col-lg-8"><section class="styles_heading__2npDn"><div class="row"><div class="col"><div class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_content__g68wo" id="c005d48a74d4"><h2 class="styles_text__LSXNx styles_text--size-700__IJpnW styles_text--weight-600__Kq9jE spacer--top-5 spacer--bottom-3" id="what-is-cosine-similarity">What is Cosine Similarity? </h2><p><strong>Cosine similarity is a mathematical metric used to measure the similarity between two vectors in a multi-dimensional space, particularly in high-dimensional spaces, by calculating the cosine of the angle between them.</strong></p><p>This is our comprehensive guide on cosine similarity, an essential concept in the field of data science, text analysis, machine learning, and much more. If you've ever wondered what cosine similarity is or how it's used in real-world applications, you're in the right place.</p><p>Cosine similarity is a mathematical way to measure how similar two sets of information are. In the simplest terms, it helps us understand the relationship between two elements by looking at the "direction" they are pointing in, rather than just comparing them based on their individual values.</p><p>Imagine you're a book lover, and you've rated three books: "The Lunar Mystery," "Secrets of the Ocean," and "Flight of the Phoenix." You've rated them on a scale of 1 to 5. Your friend has also rated these same books on the same scale:</p><p/><figure class="responsive-image" style="margin:2rem 0;max-width:none"><span style="box-sizing:border-box;display:inline-block;overflow:hidden;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;position:relative;max-width:100%"><span style="box-sizing:border-box;display:block;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;max-width:100%"><img style="display:block;max-width:100%;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0" alt="" aria-hidden="true" src="data:image/svg+xml,%3csvg%20xmlns=%27http://www.w3.org/2000/svg%27%20version=%271.1%27%20width=%27596%27%20height=%27123%27/%3e"/></span><img alt="Similarity Table " src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" decoding="async" data-nimg="intrinsic" style="top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%"/><noscript><img alt="Similarity Table " loading="lazy" decoding="async" data-nimg="intrinsic" style="top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%" srcset="https://cdn.sanity.io/images/bbnkhnhl/production/7533b320535f7fb05507561c637251ecbbb442c2-596x123.png?w=640&amp;q=75&amp;fit=clip&amp;auto=format 1x, https://cdn.sanity.io/images/bbnkhnhl/production/7533b320535f7fb05507561c637251ecbbb442c2-596x123.png?w=1200&amp;q=75&amp;fit=clip&amp;auto=format 2x" src="https://cdn.sanity.io/images/bbnkhnhl/production/7533b320535f7fb05507561c637251ecbbb442c2-596x123.png?w=1200&amp;q=75&amp;fit=clip&amp;auto=format"/></noscript></span></figure><p/><p>Both of your ratings can be represented as lists or, in mathematical terms, as "vectors", represented as [5,3,4] and [4,2,4].</p><p>Do you and your friend have similar ratings? You can look at the lists and come up with a qualitative “yes they’re pretty close”, or you can use cosine similarity to reach a quantitative measure! We will come back to this example, but cosine similarity is a concept that has far-reaching applications in areas like search engines, natural language processing, and recommendation systems.</p><p>Cosine similarity provides a means of understanding how data relates to each other, without getting bogged down by the specific details of what each data point represents. It also allows us to quickly compare information with tens, hundreds, or even thousands of elements.</p><h2 class="styles_text__LSXNx styles_text--size-700__IJpnW styles_text--weight-600__Kq9jE spacer--top-5 spacer--bottom-3" id="why-is-cosine-similarity-important">Why is Cosine Similarity Important?</h2><p>Cosine similarity is widely used in applications like natural language processing (NLP), search algorithms, and recommendation systems. It provides a robust way to understand the semantic similarity between documents, datasets, or images. For example, cosine similarity is often used in vector search engines to find the most relevant records to a given query, making search processes more efficient and precise. (Check out this guide to learn more about <a href="https://datastax.com/guides/what-is-vector-search">vector search</a>.)</p><h2 class="styles_text__LSXNx styles_text--size-700__IJpnW styles_text--weight-600__Kq9jE spacer--top-5 spacer--bottom-3" id="how-does-cosine-similarity-work">How does Cosine Similarity Work?</h2><p>Cosine similarity quantifies the similarity between two vectors by measuring the cosine of the angle between them. This is particularly useful in text analysis, where texts are converted into vectors. Each dimension of the vector represents a word from the document, with its value indicating the frequency or importance of that word.</p><p>When calculating cosine similarity, first, the dot product of the two vectors is found. This product gives a measure of how vectors in the same direction are aligned. Then, the magnitudes (or lengths) of each vector are calculated. The cosine similarity is the dot product divided by the product of the two vectors' magnitudes.</p><p>This method effectively captures the orientation (or direction) of the vectors and not their magnitude, making it a reliable measure of similarity in texts of varying lengths. It's widely used in applications like recommendation systems, document clustering, and information retrieval, where understanding the similarity or dissimilarity between texts is crucial.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="cosine-similarity-example">Cosine Similarity Example</h3><p>Let's revisit our book ratings example for a moment. We had two vectors:</p><p><em><br/><strong>Your ratings: [5,3,4]</strong></em></p><p><strong><em>Your friend's ratings: [4,2,4]</em></strong></p><p><br/>Using cosine similarity, we can quantify how similar these vectors are. The cosine similarity will return a value between -1 and 1; a value closer to 1 indicates greater similarity. In our example, calculating the cosine similarity gives us a value 0.9899, suggesting that you and your friend have very similar tastes in books. If you had another friend with ratings of [1,5,2], the cosine similarity would be 0.7230, suggesting less similar tastes.</p><p>The site <a href="https://www.math3d.org/vectors">math3d.org</a> can provide a helpful way to visualize two and three-dimensional vectors. With our simple example vectors, we can see that the angle between [5,3,4] and [4,2,4] is smaller than the angle between [5,3,4] and [1,5,2]:</p><p/><figure class="responsive-image" style="margin:2rem 0;max-width:none"><span style="box-sizing:border-box;display:inline-block;overflow:hidden;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;position:relative;max-width:100%"><span style="box-sizing:border-box;display:block;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;max-width:100%"><img style="display:block;max-width:100%;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0" alt="" aria-hidden="true" src="data:image/svg+xml,%3csvg%20xmlns=%27http://www.w3.org/2000/svg%27%20version=%271.1%27%20width=%27676%27%20height=%27582%27/%3e"/></span><img alt="Angular Similarity Comparison Graph" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" decoding="async" data-nimg="intrinsic" style="top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%"/><noscript><img alt="Angular Similarity Comparison Graph" loading="lazy" decoding="async" data-nimg="intrinsic" style="top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%" srcset="https://cdn.sanity.io/images/bbnkhnhl/production/e836deb936e753918c22d2d613d320a55093a2e0-676x582.png?w=750&amp;q=75&amp;fit=clip&amp;auto=format 1x, https://cdn.sanity.io/images/bbnkhnhl/production/e836deb936e753918c22d2d613d320a55093a2e0-676x582.png?w=1920&amp;q=75&amp;fit=clip&amp;auto=format 2x" src="https://cdn.sanity.io/images/bbnkhnhl/production/e836deb936e753918c22d2d613d320a55093a2e0-676x582.png?w=1920&amp;q=75&amp;fit=clip&amp;auto=format"/></noscript></span></figure><p><em>Source: <a href="https://www.math3d.org/7tCJuIQal">https://www.math3d.org/7tCJuIQal</a><br/></em></p><p>If you are wondering “If smaller angles mean two vectors are more similar, why are we not just using the angles - why all this complicated math?”, we will be answering that later on!</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="the-significance-of-cosine-similarity-in-data-analysis-and-nlp">The Significance of Cosine Similarity in Data Analysis and NLP</h3><p>Cosine similarity is invaluable in fields like data analysis and natural language processing. In NLP, it is frequently used for tasks such as text mining, sentiment analysis, and document clustering. The metric helps in comparing two pieces of text to understand their semantic similarity, which is crucial for making accurate recommendations or categorizations.</p><p>A real-world example of a customer making use of cosine similarity is <a href="https://www.dataworkz.com/">Dataworkz</a>. They are a California-based company that aims to simplify AI-driven decision-making for business users through its no-code, cloud-based platform. The service unifies data gathering, transformation, and the application of machine learning algorithms into a single user-friendly interface. You can read more about what they are doing <a href="https://www.datastax.com/blog/how-dataworkz-democratizes-access-to-ai-and-data-analytics">here</a>.</p><h2 class="styles_text__LSXNx styles_text--size-700__IJpnW styles_text--weight-600__Kq9jE spacer--top-5 spacer--bottom-3" id="how-cosine-similarity-differs-from-other-similarity-metrics">How Cosine Similarity Differs from Other Similarity Metrics</h2><p>There are various ways to measure similarity between sets of data, with Euclidean distance being another commonly used metric. While Euclidean distance focuses on the straight-line distance between two points in space, cosine similarity focuses on the angle between two vectors. This makes cosine similarity more robust in capturing the pattern similarities between two sets of data, even if their magnitudes differ.</p><p>For example, if two documents have the same words but in different frequencies, Euclidean distance might consider them quite different due to the differences in magnitude (frequency). Cosine similarity, however, would capture their similarity more effectively because it is less sensitive to the frequency of the words and more focused on their presence or absence in the documents.</p><p>A “close cousin” to cosine similarity is dot product similarity. It is typically used when the vectors are already normalized (their magnitudes are 1), thereby avoiding the computational step of dividing by the product of their magnitudes (which will always be 1!). Several <a href="https://www.datastax.com/guides/what-is-a-vector-embedding">vector embedding</a> models output normalized vectors, making dot product similarity calculations faster.</p><h2 class="styles_text__LSXNx styles_text--size-700__IJpnW styles_text--weight-600__Kq9jE spacer--top-5 spacer--bottom-3" id="practical-tips-for-using-cosine-similarity">Practical Tips for Using Cosine Similarity</h2><p>To effectively utilize cosine similarity in various applications, certain practical tips can enhance accuracy and efficiency. The below tips help navigate common challenges and ensure that cosine similarity provides meaningful insights, especially in text analysis and comparison tasks.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="1-preprocess-data">1. Preprocess Data</h3><p>Thorough data preprocessing is crucial. This involves removing stop words which are common words that add little semantic value. Additionally, applying stemming or lemmatization helps in reducing words to their base form, thereby standardizing the dataset for better comparison.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="2-term-weighting">2. Term Weighting</h3><p>Implementing TF-IDF (Term Frequency-Inverse Document Frequency) is beneficial. This technique assigns weights to each word in a document, emphasizing words that are rare across the dataset but frequent in individual documents, thereby enhancing the differentiation power of the vectors.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="3-consider-dataset-size-and-diversity">3. Consider Dataset Size and Diversity</h3><p>The size and diversity of your dataset are critical. Larger datasets, encompassing a wide range of topics or styles, typically provide more robust and accurate similarity measures, offering a comprehensive basis for comparison.</p><p><br/></p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="4-be-mindful-of-computational-complexity">4. Be Mindful of Computational Complexity</h3><p>For large datasets, the computational complexity can be significant. It's important to optimize your algorithm and computational resources to handle the data efficiently without sacrificing accuracy.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="5-understand-the-context">5. Understand the Context</h3><p>It’s essential to align the use of cosine similarity with the context of your application. Since cosine similarity measures the orientation rather than the magnitude of vectors, it's ideal for some scenarios (like text similarity) but may not be suitable for others where magnitude is important.</p></div></div></div></section><section class=" styles_banner_section___ABu3"><div class=""><div class="row"><div class="col"><div class="styles_banner__9aW4Q styles_spacing__AGxIx styles_banner--background-astra__3oyLw"><div class="row"><div class="col styles_col_spacing__HDdTa"><h2 class="styles_display__cQk8b styles_display--size-300__vVmUP styles_display--weight-600__cx95q spacer--bottom-3">Build Production-ready Generative AI Apps at Scale with Astra DB</h2><p class="styles_text__LSXNx styles_text--size-400__zklxt styles_text--weight-400__XgWIb spacer--bottom-3">Scale the development of real-time generative AI projects by harnessing the power of our industry-leading vector database.</p><div class="d-block d-lg-inline spacer--top-3 spacer--right-3"><a href="https://astra.datastax.com/signup" class="styles_button__Y3Rmo styles_button--secondary__W1Pwy styles_button--color-reverse__gOooD styles_button--uppercase__UQqQH"><span>Get Started for Free</span></a></div><div class="d-block d-lg-inline spacer--top-3 spacer--right-3"><a href="https://www.datastax.com/products/vector-search" class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_button--uppercase__UQqQH"><span>Learn More</span><svg width="6" height="13" viewbox="0 0 6 13" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M2.16196 0.792358L5.45785 6.5021L2.16196 12.2058L0 12.2076L3.29589 6.5021L0 0.792358H2.16196Z" fill="currentColor"/></svg></a></div></div></div></div></div></div></div></section><section class="styles_heading__2npDn"><div class="row"><div class="col"><div class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_content__g68wo" id="fcec55c29cddf15999c59c968a3cb92c"><h2 class="styles_text__LSXNx styles_text--size-700__IJpnW styles_text--weight-600__Kq9jE spacer--top-5 spacer--bottom-3" id="advantages-of-cosine-similarity">Advantages of Cosine Similarity</h2><p>Cosine similarity is a widely used metric that has several advantages in various applications, such as text analysis, recommendation systems, and more. Below are some key benefits that make it a go-to choice for measuring similarity between vectors.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="scale-invariant">Scale-invariant</h3><p>Cosine similarity is scale-invariant, meaning that it is not affected by the magnitudes of the vectors. This is especially useful in scenarios where you want to focus solely on the directionality of the vectors, rather than their length. Whether the values in your vector are in the tens or the millions, the cosine similarity will remain the same, making it versatile across different scales.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="dimensionality-reduction">Dimensionality Reduction</h3><p>Another advantage of using cosine similarity is its compatibility with techniques like Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). Because it measures similarity in terms of angle rather than distance, you can reduce the dimensions of your vectors without significantly affecting the cosine similarity measure.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="simplicity-and-efficiency">Simplicity and Efficiency</h3><p>The formula for calculating cosine similarity is straightforward, requiring just the dot product of the vectors and their magnitudes. This simplicity leads to efficient computations, making it suitable for real-time applications and large datasets.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="angle-measurement">Angle Measurement</h3><p>Unlike other distance-based similarity measures, cosine similarity considers the angle between vectors, providing a more intuitive sense of similarity. Smaller angles indicate higher similarity, and the measure ranges between -1 and 1, making interpretation easier.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="widely-used-in-text-analysis">Widely Used in Text Analysis</h3><p>Cosine similarity is particularly popular in the field of text analysis. When documents are converted into embedding vectors, cosine similarity effectively captures the "angle" between different documents, highlighting how closely the contents are related.</p><p>By considering these advantages, it becomes clear why cosine similarity is a popular choice in various machine learning and data science applications.</p><h2 class="styles_text__LSXNx styles_text--size-700__IJpnW styles_text--weight-600__Kq9jE spacer--top-5 spacer--bottom-3" id="potential-challenges-and-limitations-of-cosine-similarity">Potential Challenges and Limitations of Cosine Similarity</h2><p>While cosine similarity is a valuable tool in text analysis and other applications, it comes with specific challenges and limitations that can impact its effectiveness. Understanding these challenges is crucial for accurately interpreting results and applying cosine similarity most effectively. Here are some key challenges and limitations to consider:</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="handling-high-dimensional-data">Handling High-Dimensional Data</h3><p>Cosine similarity can become less effective in high-dimensional spaces, often referred to as the "curse of dimensionality". In such spaces, distinguishing between different vectors becomes challenging due to the increased distance between points.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="sensitivity-to-document-length">Sensitivity to Document Length</h3><p>While cosine similarity normalizes for document length, it can still be sensitive to variations in length. This sensitivity might affect the accuracy when comparing longer documents with shorter ones.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="interpretation-of-results">Interpretation of Results</h3><p>Interpreting the cosine similarity score requires caution. A high similarity score doesn’t always equate to high relevance or quality content, and vice versa. The context of the data and the application’s specific needs must be considered.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="dependence-on-vector-representation">Dependence on Vector Representation</h3><p>The effectiveness of cosine similarity heavily relies on the quality of the vector representation of the documents. Poorly constructed vectors can lead to inaccurate similarity measures.</p><h3 class="styles_text__LSXNx styles_text--size-600__1sjZA styles_text--weight-600__Kq9jE spacer--top-2 spacer--bottom-2" id="overlooking-semantic-meaning">Overlooking Semantic Meaning</h3><p>Cosine similarity focuses on the frequency of terms but can overlook the deeper semantic meaning behind them. This can lead to misleading results, especially in documents where the context and semantic meaning are crucial.</p><h2 class="styles_text__LSXNx styles_text--size-700__IJpnW styles_text--weight-600__Kq9jE spacer--top-5 spacer--bottom-3" id="unraveling-the-power-of-cosine-similarity">Unraveling the Power of Cosine Similarity</h2><p>As we wrap up, let's take a moment to summarize what we've discussed in this extensive guide on cosine similarity. We delved deep into the core principles of this fascinating metric, showing you its mathematical foundations. And don't forget about its numerous advantages, including its scale-invariant nature and its compatibility with dimensionality reduction techniques, which make it an essential tool in the fields of machine learning and data science.</p><p>Now, if you're interested in putting your newfound knowledge into practical use, DataStax's <a href="https://www.datastax.com/products/datastax-astra">Astra DB</a> offers an excellent platform for executing vector searches with built-in cosine similarity calculations. Astra DB's <a href="https://www.datastax.com/products/vector-search">Vector Search</a> feature handles the heavy lifting, allowing you to focus more on deriving insights from your data.</p><p>The <a href="https://docs.datastax.com/en/astra-serverless/docs/vector-search/overview.html">Vector Search Overview</a> documentation contains many quick-starts and tutorials that can help you build real-world applications that leverage cosine similarity search, including not only chatbots but also image searching!</p><p>To start your journey with Astra DB, you can register for a <a href="https://www.datastax.com/lp/astra-registration?utm_source=cosine_guide&amp;utm_medium=guide&amp;utm_campaign=guidepillar">free account here</a>.</p></div><div class="row spacer--top-4"><div class="col text-center"><a href="/guides/rss.xml" class="styles_button__Y3Rmo styles_button--primary__44UCv styles_button--color-regular__WBzQT styles_button--uppercase__UQqQH" rel="noreferrer noopener" target="_blank"><span>Subscribe to the RSS Feed</span></a></div></div></div></div></section><section class="styles_margin__7d75K styles_banner_section___ABu3"><div class=""><div class="row"><div class="col"><div class="styles_banner__9aW4Q styles_spacing__AGxIx styles_banner--background-astra__3oyLw"><div class="row"><div class="col styles_col_spacing__HDdTa"><h2 class="styles_display__cQk8b styles_display--size-300__vVmUP styles_display--weight-600__cx95q spacer--bottom-3">Build Production-ready Generative AI Apps at Scale with Astra DB</h2><p class="styles_text__LSXNx styles_text--size-400__zklxt styles_text--weight-400__XgWIb spacer--bottom-3">Scale the development of real-time generative AI projects by harnessing the power of our industry-leading vector database.</p><div class="d-block d-lg-inline spacer--top-3 spacer--right-3"><a href="https://astra.datastax.com/signup" class="styles_button__Y3Rmo styles_button--secondary__W1Pwy styles_button--color-reverse__gOooD styles_button--uppercase__UQqQH"><span>Get Started for Free</span></a></div><div class="d-block d-lg-inline spacer--top-3 spacer--right-3"><a href="https://www.datastax.com/products/vector-search" class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_button--uppercase__UQqQH"><span>Learn More</span><svg width="6" height="13" viewbox="0 0 6 13" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M2.16196 0.792358L5.45785 6.5021L2.16196 12.2058L0 12.2076L3.29589 6.5021L0 0.792358H2.16196Z" fill="currentColor"/></svg></a></div></div></div></div></div></div></div></section><section class="styles_faq__QWPDz"><div class="row"><div class="col spacer--bottom-5"><h4 class="styles_display__cQk8b styles_display--size-400__HV_pT styles_display--weight-600__cx95q" id="cosine-similarity-faqs">Cosine Similarity FAQs</h4></div></div><div class="row"><div class="col"><article><header><h3 class="styles_display__cQk8b styles_display--size-200__VVui5 styles_display--weight-600__cx95q">What is cosine similarity?</h3><svg fill="none" height="24" viewbox="0 0 24 24" width="24" xmlns="http://www.w3.org/2000/svg"><rect x="5" y="11" width="14" height="2" fill="currentColor"/><rect x="13" y="5" width="14" height="2" transform="rotate(90 13 5)" fill="currentColor"/></svg></header><div class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb" style="height:0px"><div class="inner-spacer--top-3"><p>Cosine similarity is a metric used to determine the cosine of the angle between two non-zero vectors, helping to understand the similarity between two sets of data based on orientation rather than magnitude.</p></div></div></article><article><header><h3 class="styles_display__cQk8b styles_display--size-200__VVui5 styles_display--weight-600__cx95q">How is cosine similarity calculated?</h3><svg fill="none" height="24" viewbox="0 0 24 24" width="24" xmlns="http://www.w3.org/2000/svg"><rect x="5" y="11" width="14" height="2" fill="currentColor"/><rect x="13" y="5" width="14" height="2" transform="rotate(90 13 5)" fill="currentColor"/></svg></header><div class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb" style="height:0px"><div class="inner-spacer--top-3"><p>It is computed as the dot product of the vectors divided by the product of their magnitudes, with a value range of -1 to 1, where 1 indicates greater similarity.</p></div></div></article><article><header><h3 class="styles_display__cQk8b styles_display--size-200__VVui5 styles_display--weight-600__cx95q">How does cosine similarity differ from other similarity metrics?</h3><svg fill="none" height="24" viewbox="0 0 24 24" width="24" xmlns="http://www.w3.org/2000/svg"><rect x="5" y="11" width="14" height="2" fill="currentColor"/><rect x="13" y="5" width="14" height="2" transform="rotate(90 13 5)" fill="currentColor"/></svg></header><div class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb" style="height:0px"><div class="inner-spacer--top-3"><p>Unlike Euclidean distance which focuses on magnitude, cosine similarity emphasizes the orientation of vectors, making it more robust in capturing pattern similarities between data sets.</p></div></div></article><article><header><h3 class="styles_display__cQk8b styles_display--size-200__VVui5 styles_display--weight-600__cx95q">Why is cosine similarity significant in natural language processing (NLP)?</h3><svg fill="none" height="24" viewbox="0 0 24 24" width="24" xmlns="http://www.w3.org/2000/svg"><rect x="5" y="11" width="14" height="2" fill="currentColor"/><rect x="13" y="5" width="14" height="2" transform="rotate(90 13 5)" fill="currentColor"/></svg></header><div class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb" style="height:0px"><div class="inner-spacer--top-3"><p>It helps in comparing text to understand semantic similarity, crucial for text mining, sentiment analysis, and document clustering in NLP.</p></div></div></article><article><header><h3 class="styles_display__cQk8b styles_display--size-200__VVui5 styles_display--weight-600__cx95q">What are the advantages of using cosine similarity?</h3><svg fill="none" height="24" viewbox="0 0 24 24" width="24" xmlns="http://www.w3.org/2000/svg"><rect x="5" y="11" width="14" height="2" fill="currentColor"/><rect x="13" y="5" width="14" height="2" transform="rotate(90 13 5)" fill="currentColor"/></svg></header><div class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb" style="height:0px"><div class="inner-spacer--top-3"><p>It offers a robust way to measure similarity with broad applications, especially in NLP and data analysis, and is less sensitive to the magnitude of vectors compared to other metrics.</p></div></div></article></div></div></section></div><div class="col-lg-4 styles_page__gap__HFkQm"><div class="styles_page__sidebar__Wx31R"><div class="styles_sidebar__NSmjz"><div class="styles_sidebar_content__dLVPi"><div><div class="styles_share__nImQR styles_sidebar_social__8W7XM"><h5 class="styles_eyebrow__tOueC styles_eyebrow--size-200__CQpqa">Share</h5><div><a href="http://www.facebook.com/sharer.php?u=https://www.datastax.com/guides/what-is-cosine-similarity&amp;p[title]=What is Cosine Similarity? A Comprehensive Guide" target="_blank" rel="noreferrer"><span role="img" aria-label="Share on Facebook"><svg width="32" height="32" viewbox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M32.0005 0H2.32856e-05V31.6509H17.0952V19.4347H12.9128V14.6205H17.0728V11.1056C17.0728 7.01316 19.5935 4.7865 23.2819 4.7865C24.5349 4.79018 25.7868 4.86062 27.0321 4.99751V9.2676H24.4833C22.4791 9.2676 22.0917 10.206 22.0917 11.5887V14.6427H26.8917L26.2686 19.4347H22.0917V31.6509H32.0005V0Z" fill="currentColor"/></svg></span></a><a href="http://twitter.com/share?text=What is Cosine Similarity? A Comprehensive Guide&amp;url=https://www.datastax.com/guides/what-is-cosine-similarity" target="_blank" rel="noreferrer"><span role="img" aria-label="Share on X"><svg width="32" height="32" viewbox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M32 0H0V32H32V0ZM26.283 5L18.093 14.5495H18.0925L27 27.5525H20.449L14.4507 18.7962L6.94088 27.5525H5L13.589 17.5385L5 5H11.551L17.2308 13.2917L24.3422 5H26.283ZM14.5644 16.4004L15.4347 17.6489V17.6494L21.3621 26.1536H24.3431L17.0795 15.7318L16.2092 14.4833L10.6213 6.46555H7.6402L14.5644 16.4004Z" fill="currentColor"/></svg></span></a><a href="https://www.linkedin.com/shareArticle?mini=true&amp;url=https://www.datastax.com/guides/what-is-cosine-similarity&amp;title=What is Cosine Similarity? A Comprehensive Guide" target="_blank" rel="noreferrer"><span role="img" aria-label="Share on LinkedIn"><svg width="32" height="32" viewbox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 3.23069e-05V31.6509C10.6667 31.6509 21.3333 31.6509 32 31.6509V0C21.3333 0.000442157 10.6667 0.000151395 0 3.23069e-05ZM9.48148 26.9619H4.74074V11.8398H9.48148V26.9619ZM7.11111 9.78836C5.57037 9.78836 4.38519 8.55749 4.38519 7.09218C4.38519 5.56825 5.62963 4.39599 7.11111 4.39599C8.65185 4.39599 9.83704 5.62686 9.83704 7.09218C9.8963 8.55749 8.65185 9.78836 7.11111 9.78836ZM27.2593 26.9619H22.5185V19.6353C22.5185 17.8769 22.4593 15.6496 20.0296 15.6496C17.5407 15.6496 17.1852 17.5838 17.1852 19.5181V26.9619H12.4444V11.8398H17.0074V13.8912H17.0667C17.7185 12.719 19.2593 11.4295 21.5704 11.4295C26.3704 11.4295 27.2593 14.536 27.2593 18.6389V26.9619Z" fill="currentColor"/></svg></span></a><a href="https://news.ycombinator.com/submitlink?u=https://www.datastax.com/guides/what-is-cosine-similarity" target="_blank" rel="noreferrer"><span role="img" aria-label="Share on Hacker News"><svg width="32" height="32" viewbox="0 0 22 22" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill="currentColor" d="M.75 0v22h22V0h-22Zm12.405 10.742v7.453h-2.81v-7.453L5.979 3.465l3.245-.017s2.577 4.684 2.594 4.702h.05c.016-.018 2.66-4.685 2.66-4.685h2.993l-4.366 7.277Z"/></svg></span></a></div></div></div><div class="styles_sidebar_jump__4c4z7"><span class="styles_eyebrow__tOueC styles_eyebrow--size-200__CQpqa">JUMP TO SECTION</span></div><div><div><h2 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#what-is-cosine-similarity">What is Cosine Similarity? </a></h2><h2 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#why-is-cosine-similarity-important">Why is Cosine Similarity Important?</a></h2><h2 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#how-does-cosine-similarity-work">How does Cosine Similarity Work?</a></h2><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#cosine-similarity-example">Cosine Similarity Example</a></h3><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#the-significance-of-cosine-similarity-in-data-analysis-and-nlp">The Significance of Cosine Similarity in Data Analysis and NLP</a></h3><h2 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#how-cosine-similarity-differs-from-other-similarity-metrics">How Cosine Similarity Differs from Other Similarity Metrics</a></h2><h2 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#practical-tips-for-using-cosine-similarity">Practical Tips for Using Cosine Similarity</a></h2><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#1-preprocess-data">1. Preprocess Data</a></h3><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#2-term-weighting">2. Term Weighting</a></h3><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#3-consider-dataset-size-and-diversity">3. Consider Dataset Size and Diversity</a></h3><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#4-be-mindful-of-computational-complexity">4. Be Mindful of Computational Complexity</a></h3><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#5-understand-the-context">5. Understand the Context</a></h3></div></div><div><div><h2 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#advantages-of-cosine-similarity">Advantages of Cosine Similarity</a></h2><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#scale-invariant">Scale-invariant</a></h3><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#dimensionality-reduction">Dimensionality Reduction</a></h3><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#simplicity-and-efficiency">Simplicity and Efficiency</a></h3><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#angle-measurement">Angle Measurement</a></h3><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#widely-used-in-text-analysis">Widely Used in Text Analysis</a></h3><h2 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#potential-challenges-and-limitations-of-cosine-similarity">Potential Challenges and Limitations of Cosine Similarity</a></h2><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#handling-high-dimensional-data">Handling High-Dimensional Data</a></h3><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#sensitivity-to-document-length">Sensitivity to Document Length</a></h3><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#interpretation-of-results">Interpretation of Results</a></h3><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#dependence-on-vector-representation">Dependence on Vector Representation</a></h3><h3 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#overlooking-semantic-meaning">Overlooking Semantic Meaning</a></h3><h2 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#unraveling-the-power-of-cosine-similarity">Unraveling the Power of Cosine Similarity</a></h2></div></div><div><h2 class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-400__XgWIb styles_heading__pkklR"><a href="#cosine-similarity-faqs">Cosine Similarity FAQs</a></h2></div></div></div></div></div></div></div><section class="styles_related__lP0jo"><div class="container"><div class="row"><div class="col styles_heading__16USR"><h5 class="styles_display__cQk8b styles_display--size-300__vVmUP styles_display--weight-600__cx95q">More Guides</h5><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-regular__WBzQT" href="/guides"><span>View All</span><svg width="6" height="13" viewbox="0 0 6 13" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M2.16196 0.792358L5.45785 6.5021L2.16196 12.2058L0 12.2076L3.29589 6.5021L0 0.792358H2.16196Z" fill="currentColor"/></svg></a></div></div><div class="row"><div class="col-md-6 spacer--bottom-5"><a class="styles_guide__wi1f9" href="/guides/what-is-hybrid-cloud"><img alt="What is Hybrid Cloud? | Hybrid Cloud Database" loading="lazy" width="0" height="0" decoding="async" data-nimg="1" style="color:transparent" src="https://www.datastax.com/api/og?title=What%20is%20Hybrid%20Cloud%3F%20%7C%20Hybrid%20Cloud%20Database&amp;type=guide"/><span class="styles_eyebrow__tOueC styles_eyebrow--size-100__mWpUx">Guide</span><h6 class="styles_display__cQk8b styles_display--size-100__DvYrC styles_display--weight-600__cx95q">What is Hybrid Cloud? | Hybrid Cloud Database</h6></a></div><div class="col-md-6 spacer--bottom-5"><a class="styles_guide__wi1f9" href="/guides/what-is-stargate"><img alt="What is Stargate? All About Cassandra Stargate" loading="lazy" width="551" height="400" decoding="async" data-nimg="1" style="color:transparent" srcset="https://cdn.sanity.io/images/bbnkhnhl/production/ff62dbcd4717c37878d51f918c00a2a131efc926-551x400.jpg?w=640&amp;q=75&amp;fit=clip&amp;auto=format 1x, https://cdn.sanity.io/images/bbnkhnhl/production/ff62dbcd4717c37878d51f918c00a2a131efc926-551x400.jpg?w=1200&amp;q=75&amp;fit=clip&amp;auto=format 2x" src="https://cdn.sanity.io/images/bbnkhnhl/production/ff62dbcd4717c37878d51f918c00a2a131efc926-551x400.jpg?w=1200&amp;q=75&amp;fit=clip&amp;auto=format"/><span class="styles_eyebrow__tOueC styles_eyebrow--size-100__mWpUx">Guide</span><h6 class="styles_display__cQk8b styles_display--size-100__DvYrC styles_display--weight-600__cx95q">What is Stargate? All About Cassandra Stargate</h6></a></div><div class="col-md-6 spacer--bottom-5"><a class="styles_guide__wi1f9" href="/guides/what-is-a-cloud-native-application"><img alt="What is a Cloud-Native Application? Definition, Benefits &amp; More" loading="lazy" width="312" height="232" decoding="async" data-nimg="1" style="color:transparent" srcset="https://cdn.sanity.io/images/bbnkhnhl/production/99ac3548a15a73c950dc3fc8817b56ef5ee80e07-312x232.svg?w=384&amp;q=75&amp;fit=clip&amp;auto=format 1x, https://cdn.sanity.io/images/bbnkhnhl/production/99ac3548a15a73c950dc3fc8817b56ef5ee80e07-312x232.svg?w=640&amp;q=75&amp;fit=clip&amp;auto=format 2x" src="https://cdn.sanity.io/images/bbnkhnhl/production/99ac3548a15a73c950dc3fc8817b56ef5ee80e07-312x232.svg?w=640&amp;q=75&amp;fit=clip&amp;auto=format"/><span class="styles_eyebrow__tOueC styles_eyebrow--size-100__mWpUx">Guide</span><h6 class="styles_display__cQk8b styles_display--size-100__DvYrC styles_display--weight-600__cx95q">What is a Cloud-Native Application? Definition, Benefits &amp; More</h6></a></div><div class="col-md-6 spacer--bottom-5"><a class="styles_guide__wi1f9" href="/guides/what-is-a-serverless-database"><img alt="What is a Serverless Database? All About Serverless Databases" loading="lazy" width="0" height="0" decoding="async" data-nimg="1" style="color:transparent" src="https://www.datastax.com/api/og?title=What%20is%20a%20Serverless%20Database%3F%20All%20About%20Serverless%20Databases&amp;type=guide"/><span class="styles_eyebrow__tOueC styles_eyebrow--size-100__mWpUx">Guide</span><h6 class="styles_display__cQk8b styles_display--size-100__DvYrC styles_display--weight-600__cx95q">What is a Serverless Database? All About Serverless Databases</h6></a></div></div></div></section><section class=" styles_banner_section___ABu3"><div class="container"><div class="row"><div class="col"><div class="styles_banner__9aW4Q styles_bg_spacing__3yWSI styles_banner--background-astra__3oyLw" style="background-image:url(https://cdn.sanity.io/images/bbnkhnhl/production/f5246408eb8d1cca5cf8466e0cee5d32e3150107-758x560.png)"><div class="row"><div class="col-lg-6 offset-lg-5"><h2 class="styles_display__cQk8b styles_display--size-400__HV_pT styles_display--weight-600__cx95q spacer--bottom-3">One-stop Data API for Production GenAI</h2><p class="styles_text__LSXNx styles_text--size-400__zklxt styles_text--weight-400__XgWIb spacer--bottom-3">Astra DB gives JavaScript developers a complete data API and out-of-the-box integrations that make it easier to build production RAG apps with high relevancy and low latency.</p><div class="d-block d-lg-inline spacer--top-3 spacer--right-3"><a href="https://astra.datastax.com/signup" class="styles_button__Y3Rmo styles_button--secondary__W1Pwy styles_button--color-reverse__gOooD styles_button--uppercase__UQqQH"><span>Get started for free</span></a></div><div class="d-block d-lg-inline spacer--top-3 spacer--right-3"><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_button--uppercase__UQqQH" href="/products/astra/demo"><span>Schedule a demo</span><svg width="6" height="13" viewbox="0 0 6 13" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M2.16196 0.792358L5.45785 6.5021L2.16196 12.2058L0 12.2076L3.29589 6.5021L0 0.792358H2.16196Z" fill="currentColor"/></svg></a></div></div></div></div></div></div></div></section></div></main><footer class="styles_footer__tD1mE"><div class="be-ix-link-block"/><div class="styles_container__JnDP_"><div class="row"><div class="col-xl-9 col-md-12"><div class="row styles_nav__row__eEqbY"><div class="col-xl-3 col-md-4 col-6 styles_nav__col___Gx9r" data-item-number="0"><span class="styles_eyebrow__tOueC styles_eyebrow--size-200__CQpqa styles_nav__title__R1_IA">Company</span><nav class="styles_nav__s5eJW"><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/company"><span>About Us</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/our-people"><span>Leadership</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/company/board-of-directors"><span>Board of Directors</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/contact-us"><span>Contact Us</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/partners/directory"><span>Partners</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/company/careers"><span>Careers</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/resources/news"><span>Newsroom</span></a></nav></div><div class="col-xl-3 col-md-4 col-6 styles_nav__col___Gx9r" data-item-number="1"><span class="styles_eyebrow__tOueC styles_eyebrow--size-200__CQpqa styles_nav__title__R1_IA">Resources</span><nav class="styles_nav__s5eJW"><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/blog"><span>Blog</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/events"><span>Events</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/resources?type=ebook"><span>eBooks</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/resources?type=whitepapers"><span>Whitepapers</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/resources?type=webinar"><span>Webinars</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/legal"><span>Legal</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/products/datastax-enterprise/security-assurance"><span>Security</span></a><a href="https://awesome-astra.github.io/docs/" class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" rel="noreferrer noopener" target="_blank"><span>Awesome Astra</span></a></nav></div><div class="col-xl-3 col-md-4 col-6 styles_nav__col___Gx9r" data-item-number="2"><span class="styles_eyebrow__tOueC styles_eyebrow--size-200__CQpqa styles_nav__title__R1_IA">Cloud Partners</span><nav class="styles_nav__s5eJW"><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/platform/amazon-web-services"><span>Amazon Web Services</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/platform/google-cloud"><span>Google Cloud</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/platform/microsoft-azure"><span>Microsoft Azure</span></a><a class="styles_button__Y3Rmo styles_button--link___jH5I styles_button--color-reverse__gOooD styles_link__kHd8K" href="/platform/nvidia"><span>NVIDIA</span></a></nav></div><div class="col-xl-3 col-md-4 col-125 ms-xl-auto"><div class="styles_newsletter__zt0jX"><span class="styles_text__LSXNx styles_text--size-300__Jtynv styles_text--weight-600__Kq9jE styles_title__K72uG">Get the latest developer updates</span><span class="styles_text__LSXNx styles_text--size-200__t4kFW styles_text--weight-400__XgWIb styles_content__WhNSE">Subscribe for weekly dev stories and news you can use</span><form id="mktoForm_1908"/></div></div></div><div class="row"><div class="col"><nav class="styles_subnav__NcsCu"><span>© 2024 DataStax</span><span><a href="/legal/datastax-website-privacy-policy">Privacy Policy</a></span><span><a href="/legal/datastax-website-terms-use">Terms of Use</a></span><span><a href="/legal/datastax-trademark-notice">Trademark Notice</a></span><span><a href="/brand-resources">Brand Resources</a></span><span><button id="ot-sdk-btn" class="ot-sdk-show-settings">Do Not Sell My Info</button></span></nav></div></div><div class="row"><div class="col"><p class="styles_text__LSXNx styles_text--size-100__NOnqB styles_text--weight-400__XgWIb styles_copy__manCQ">DataStax, is a registered trademark of DataStax, Inc.. Apache, Apache Cassandra, Cassandra, Apache Pulsar, and Pulsar are either registered trademarks or trademarks of the Apache Software Foundation.</p></div></div></div><div class="col-xl-3 col-md-12 d-flex flex-column"><div class="styles_extra__5FFQk"><nav class="styles_social__N71rf"><a href="https://www.facebook.com/datastax" class="styles_button__Y3Rmo styles_button--icon__ueDQd styles_button--color-regular__WBzQT styles_icon_link__SQDYN" target="_blank" rel="noreferrer noopener" title="https://www.facebook.com/datastax"><img alt="DataStax on Facebook" loading="lazy" width="32" height="32" decoding="async" data-nimg="1" style="color:transparent" srcset="https://cdn.sanity.io/images/bbnkhnhl/production/8ce5879a6fdfda3267ac087906a03d2ce3182094-32x32.svg?w=32&amp;q=75&amp;fit=clip&amp;auto=format 1x, https://cdn.sanity.io/images/bbnkhnhl/production/8ce5879a6fdfda3267ac087906a03d2ce3182094-32x32.svg?w=64&amp;q=75&amp;fit=clip&amp;auto=format 2x" src="https://cdn.sanity.io/images/bbnkhnhl/production/8ce5879a6fdfda3267ac087906a03d2ce3182094-32x32.svg?w=64&amp;q=75&amp;fit=clip&amp;auto=format"/></a><a href="https://twitter.com/datastax" class="styles_button__Y3Rmo styles_button--icon__ueDQd styles_button--color-regular__WBzQT styles_icon_link__SQDYN" target="_blank" rel="noreferrer noopener" title="https://twitter.com/datastax"><img alt="DataStax on X" loading="lazy" width="32" height="33" decoding="async" data-nimg="1" style="color:transparent" srcset="https://cdn.sanity.io/images/bbnkhnhl/production/f39a8e0412d732fcfbf8e166b9fc2b5c0ab62ff8-32x33.svg?w=32&amp;q=75&amp;fit=clip&amp;auto=format 1x, https://cdn.sanity.io/images/bbnkhnhl/production/f39a8e0412d732fcfbf8e166b9fc2b5c0ab62ff8-32x33.svg?w=64&amp;q=75&amp;fit=clip&amp;auto=format 2x" src="https://cdn.sanity.io/images/bbnkhnhl/production/f39a8e0412d732fcfbf8e166b9fc2b5c0ab62ff8-32x33.svg?w=64&amp;q=75&amp;fit=clip&amp;auto=format"/></a><a href="https://www.linkedin.com/company/datastax/" class="styles_button__Y3Rmo styles_button--icon__ueDQd styles_button--color-regular__WBzQT styles_icon_link__SQDYN" target="_blank" rel="noreferrer noopener" title="https://www.linkedin.com/company/datastax/"><img alt="DataStax on LinkedIn" loading="lazy" width="32" height="32" decoding="async" data-nimg="1" style="color:transparent" srcset="https://cdn.sanity.io/images/bbnkhnhl/production/d96e639a2bf940cd885a5c56679cd287d8f7b903-32x32.svg?w=32&amp;q=75&amp;fit=clip&amp;auto=format 1x, https://cdn.sanity.io/images/bbnkhnhl/production/d96e639a2bf940cd885a5c56679cd287d8f7b903-32x32.svg?w=64&amp;q=75&amp;fit=clip&amp;auto=format 2x" src="https://cdn.sanity.io/images/bbnkhnhl/production/d96e639a2bf940cd885a5c56679cd287d8f7b903-32x32.svg?w=64&amp;q=75&amp;fit=clip&amp;auto=format"/></a><a href="https://github.com/datastax/" class="styles_button__Y3Rmo styles_button--icon__ueDQd styles_button--color-regular__WBzQT styles_icon_link__SQDYN" target="_blank" rel="noreferrer noopener" title="https://github.com/datastax/"><img alt="DataStax on GitHub" loading="lazy" width="32" height="32" decoding="async" data-nimg="1" style="color:transparent" srcset="https://cdn.sanity.io/images/bbnkhnhl/production/ad8620a9e1b21c1c982e43642cb4de21d3d19eb9-32x32.svg?w=32&amp;q=75&amp;fit=clip&amp;auto=format 1x, https://cdn.sanity.io/images/bbnkhnhl/production/ad8620a9e1b21c1c982e43642cb4de21d3d19eb9-32x32.svg?w=64&amp;q=75&amp;fit=clip&amp;auto=format 2x" src="https://cdn.sanity.io/images/bbnkhnhl/production/ad8620a9e1b21c1c982e43642cb4de21d3d19eb9-32x32.svg?w=64&amp;q=75&amp;fit=clip&amp;auto=format"/></a><a href="https://datastax.medium.com/" class="styles_button__Y3Rmo styles_button--icon__ueDQd styles_button--color-regular__WBzQT styles_icon_link__SQDYN" target="_blank" rel="noreferrer noopener" title="https://datastax.medium.com/"><img alt="DataStax on Medium" loading="lazy" width="32" height="32" decoding="async" data-nimg="1" style="color:transparent" srcset="https://cdn.sanity.io/images/bbnkhnhl/production/dd43439c38fe31a6213e46a6b9aae6ff4db76794-32x32.svg?w=32&amp;q=75&amp;fit=clip&amp;auto=format 1x, https://cdn.sanity.io/images/bbnkhnhl/production/dd43439c38fe31a6213e46a6b9aae6ff4db76794-32x32.svg?w=64&amp;q=75&amp;fit=clip&amp;auto=format 2x" src="https://cdn.sanity.io/images/bbnkhnhl/production/dd43439c38fe31a6213e46a6b9aae6ff4db76794-32x32.svg?w=64&amp;q=75&amp;fit=clip&amp;auto=format"/></a><a href="https://www.twitch.tv/datastaxdevs" class="styles_button__Y3Rmo styles_button--icon__ueDQd styles_button--color-regular__WBzQT styles_icon_link__SQDYN" target="_blank" rel="noreferrer noopener" title="https://www.twitch.tv/datastaxdevs"><img alt="DataStax on Twitch" loading="lazy" width="32" height="32" decoding="async" data-nimg="1" style="color:transparent" srcset="https://cdn.sanity.io/images/bbnkhnhl/production/e453f185aeb071f0923eceed1818a0d48f06e963-32x32.svg?w=32&amp;q=75&amp;fit=clip&amp;auto=format 1x, https://cdn.sanity.io/images/bbnkhnhl/production/e453f185aeb071f0923eceed1818a0d48f06e963-32x32.svg?w=64&amp;q=75&amp;fit=clip&amp;auto=format 2x" src="https://cdn.sanity.io/images/bbnkhnhl/production/e453f185aeb071f0923eceed1818a0d48f06e963-32x32.svg?w=64&amp;q=75&amp;fit=clip&amp;auto=format"/></a></nav><div class="styles_localization__YauVF styles_lang__eRJEZ"><button class="styles_toggler__13a1o" type="button"><svg fill="none" height="24" viewbox="0 0 48 48" width="24" xmlns="http://www.w3.org/2000/svg"><circle cx="24" cy="24" r="15" stroke="currentColor" stroke-width="2"/><path d="M31 24C31 28.2967 30.1271 32.1372 28.7624 34.8665C27.3679 37.6556 25.6263 39 24 39C22.3737 39 20.6321 37.6556 19.2376 34.8665C17.8729 32.1372 17 28.2967 17 24C17 19.7033 17.8729 15.8628 19.2376 13.1335C20.6321 10.3444 22.3737 9 24 9C25.6263 9 27.3679 10.3444 28.7624 13.1335C30.1271 15.8628 31 19.7033 31 24Z" stroke="currentColor" stroke-width="2"/><path d="M10 20H38" stroke="currentColor" stroke-width="2"/><path d="M10 28H38" stroke="currentColor" stroke-width="2"/></svg><span>United States</span></button></div></div></div></div></div></footer></body>