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<!DOCTYPE html><html lang="en-US"><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width,height=device-height,initial-scale=1.0"><meta name="apple-mobile-web-app-capable" content="yes"><meta http-equiv="X-UA-Compatible" content="ie=edge"><meta property="og:type" content="website"><meta name="twitter:card" content="summary"><style>@media screen{body[data-bespoke-view=""] .bespoke-marp-parent>.bespoke-marp-osc>button,body[data-bespoke-view=next] .bespoke-marp-parent>.bespoke-marp-osc>button,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-info-container button,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-note-container button{-webkit-tap-highlight-color:transparent;-webkit-appearance:none;appearance:none;background-color:transparent;border:0;color:inherit;cursor:pointer;font-size:inherit;opacity:.8;outline:none;padding:0;transition:opacity .2s linear}body[data-bespoke-view=""] .bespoke-marp-parent>.bespoke-marp-osc>button:disabled,body[data-bespoke-view=next] .bespoke-marp-parent>.bespoke-marp-osc>button:disabled,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-info-container button:disabled,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-note-container button:disabled{cursor:not-allowed;opacity:.15!important}body[data-bespoke-view=""] .bespoke-marp-parent>.bespoke-marp-osc>button:hover,body[data-bespoke-view=next] .bespoke-marp-parent>.bespoke-marp-osc>button:hover,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-info-container button:hover,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-note-container button:hover{opacity:1}body[data-bespoke-view=""] .bespoke-marp-parent>.bespoke-marp-osc>button:hover:active,body[data-bespoke-view=next] .bespoke-marp-parent>.bespoke-marp-osc>button:hover:active,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-info-container button:hover:active,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-note-container button:hover:active{opacity:.6}body[data-bespoke-view=""] .bespoke-marp-parent>.bespoke-marp-osc>button:hover:not(:disabled),body[data-bespoke-view=next] .bespoke-marp-parent>.bespoke-marp-osc>button:hover:not(:disabled),body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-info-container button:hover:not(:disabled),body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-note-container button:hover:not(:disabled){transition:none}body[data-bespoke-view=""] .bespoke-marp-parent>.bespoke-marp-osc>button[data-bespoke-marp-osc=prev],body[data-bespoke-view=next] 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url("data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAxMDAgMTAwIj48cGF0aCBmaWxsPSJub25lIiBzdHJva2U9IiNmZmYiIHN0cm9rZS1saW5lY2FwPSJyb3VuZCIgc3Ryb2tlLWxpbmVqb2luPSJyb3VuZCIgc3Ryb2tlLXdpZHRoPSI1IiBkPSJNODcuOCA0Ny41Qzg5IDUwIDg3LjcgNTIgODUgNTJIMzVhOC43IDguNyAwIDAgMS03LjItNC41bC0xNS42LTMxQzExIDE0IDEyLjIgMTIgMTUgMTJoNTBhOC44IDguOCAwIDAgMSA3LjIgNC41ek02MCA1MnYzNm0tMTAgMGgyME00NSA0MmgyMCIvPjwvc3ZnPg==") no-repeat 50%;background-size:contain;overflow:hidden;text-indent:100%;white-space:nowrap}body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-note-container button.bespoke-marp-presenter-note-bigger{background:transparent url("data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAxMDAgMTAwIj48cGF0aCBzdHJva2U9IiNmZmYiIHN0cm9rZS1saW5lY2FwPSJyb3VuZCIgc3Ryb2tlLWxpbmVqb2luPSJyb3VuZCIgc3Ryb2tlLXdpZHRoPSI1IiBkPSJNMTIgNTBoODBNNTIgOTBWMTAiLz48L3N2Zz4=") no-repeat 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/*!
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*
* @theme gaia
* @author Yuki Hattori
*
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class="bespoke-marp-osc"><button data-bespoke-marp-osc="prev" tabindex="-1" title="Previous slide">Previous slide</button><span data-bespoke-marp-osc="page"></span><button data-bespoke-marp-osc="next" tabindex="-1" title="Next slide">Next slide</button><button data-bespoke-marp-osc="fullscreen" tabindex="-1" title="Toggle fullscreen (f)">Toggle fullscreen</button><button data-bespoke-marp-osc="presenter" tabindex="-1" title="Open presenter view (p)">Open presenter view</button></div><div id=":$p"><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section data-class="lead" data-theme="gaia" data-style=":root {
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<h2 id="whoami">$ whoami</h2>
<ul>
<li data-marpit-fragment="1">
<p>Kumar Shivendu</p>
</li>
<li data-marpit-fragment="2">
<p>Software Engineer @ Qdrant</p>
</li>
<li data-marpit-fragment="3">
<p>I <img class="emoji" draggable="false" alt="❤️" src="https://cdn.jsdelivr.net/gh/jdecked/[email protected]/assets/svg/2764.svg" data-marp-twemoji=""/> information retrieval, performance, and data mining.</p>
</li>
<li data-marpit-fragment="4">
<p>First talk!</p>
</li>
<li data-marpit-fragment="5">
<p>Qdrant: Future of search and beyond</p>
</li>
</ul>
</section>
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<h2 id="traditional-ways-to-build-search">Traditional ways to build search</h2>
<ul>
<li data-marpit-fragment="1">
<p>Approaches:</p>
<ul>
<li data-marpit-fragment="2">Keyword match, Regex, Boolean operators</li>
<li data-marpit-fragment="3">Extracting metadata using NLP and CV</li>
<li data-marpit-fragment="4">Knowledge graphs, Vocabs</li>
</ul>
</li>
<li data-marpit-fragment="5">
<p>Challenges:</p>
<ul>
<li data-marpit-fragment="6">Effort to maintain the knowledge</li>
<li data-marpit-fragment="7">Growth of unstructured data</li>
<li data-marpit-fragment="8">Multimodal search remains hard: Text, Image, Audio, Video</li>
</ul>
</li>
</ul>
</section>
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<h2 id="vectors">Vectors</h2>
<ul>
<li data-marpit-fragment="1">Points in an N-dimensional space</li>
<li data-marpit-fragment="2">Anything -> Vector</li>
<li data-marpit-fragment="3">Generated from:
<ul>
<li data-marpit-fragment="4">ML models</li>
<li data-marpit-fragment="5">Metric learning
<ul>
<li data-marpit-fragment="6">CLIP</li>
</ul>
</li>
</ul>
</li>
</ul>
</section>
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<h2 id="vector-search">Vector search</h2>
<ul>
<li data-marpit-fragment="1">Nearest points</li>
<li data-marpit-fragment="2">Example: Google Lens</li>
<li data-marpit-fragment="3">But this is expensive and not easy to scale</li>
<li data-marpit-fragment="4">Solution: Indexing and approximation</li>
</ul>
</section>
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<h2 id="what-is-qdrant">What is <strong>Qdrant</strong></h2>
<ul>
<li data-marpit-fragment="1">
<p>Vector Search Engine (aka Vector DB)</p>
</li>
<li data-marpit-fragment="2">
<p>15k+ stars on Github</p>
</li>
<li data-marpit-fragment="3">
<p>Written in Rust <img class="emoji" draggable="false" alt="🦀" src="https://cdn.jsdelivr.net/gh/jdecked/[email protected]/assets/svg/1f980.svg" data-marp-twemoji=""/></p>
</li>
<li data-marpit-fragment="4">
<p>SDKs for Python, JS, Go, Java, etc</p>
</li>
<li data-marpit-fragment="5">
<p>Twitter, Canva, Meesho, Flipkart</p>
</li>
</ul>
</section>
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<h2 id="the-hnsw-index">The HNSW Index</h2>
<ul>
<li data-marpit-fragment="1">Skip Lists + Graphs</li>
<li data-marpit-fragment="2">Approximate and Tunable</li>
<li data-marpit-fragment="3">Filter during search</li>
<li data-marpit-fragment="4">Quantization</li>
</ul>
</section>
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<h2 id="running-search">Running search:</h2>
<pre is="marp-pre" data-auto-scaling="downscale-only"><code class="language-js"><span class="hljs-variable constant_">POST</span> /collections/rentals/points/search
{
<span class="hljs-string">"query"</span>: [<span class="hljs-number">0.2</span>, <span class="hljs-number">0.3</span>, <span class="hljs-number">0.4</span>, <span class="hljs-number">0.5</span>], <span class="hljs-comment">// vector generated from image/text/video</span>
<span class="hljs-string">"filter"</span>: { <span class="hljs-string">"must"</span>: [{<span class="hljs-string">"key"</span>: <span class="hljs-string">"locality"</span>, <span class="hljs-string">"match"</span>: {<span class="hljs-string">"value"</span>: <span class="hljs-string">"Indiranagar"</span>}}] },
<span class="hljs-string">"limit"</span>: <span class="hljs-number">10</span>
}
</code></pre>
<ul>
<li data-marpit-fragment="1">
<pre is="marp-pre" data-auto-scaling="downscale-only"><code class="language-js">[
{<span class="hljs-string">"id"</span>: <span class="hljs-number">4</span>, <span class="hljs-string">"score"</span>: <span class="hljs-number">0.56</span>, <span class="hljs-string">"payload"</span>: {...}},
{<span class="hljs-string">"id"</span>: <span class="hljs-number">2</span>, <span class="hljs-string">"score"</span>: <span class="hljs-number">0.40</span>, <span class="hljs-string">"payload"</span>: {...}},
{<span class="hljs-string">"id"</span>: <span class="hljs-number">5</span>, <span class="hljs-string">"score"</span>: <span class="hljs-number">0.23</span>, <span class="hljs-string">"payload"</span>: {...}},
]
</code></pre>
</li>
</ul>
</section>
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<h2 id="beyond-search-recommendations">Beyond search: Recommendations</h2>
<ul>
<li data-marpit-fragment="1">Realtime addition of points is possible.</li>
<li data-marpit-fragment="2"><code>average_vector</code> and <code>best_score</code></li>
<li data-marpit-fragment="3">
<pre is="marp-pre" data-auto-scaling="downscale-only"><code class="language-js"><span class="hljs-variable constant_">POST</span> /collections/rentals/points/recommend
{
<span class="hljs-string">"positive"</span>: [<span class="hljs-number">100</span>, <span class="hljs-number">231</span>], <span class="hljs-comment">// vector ids</span>
<span class="hljs-string">"negative"</span>: [<span class="hljs-number">718</span>, [<span class="hljs-number">0.2</span>, <span class="hljs-number">0.3</span>, <span class="hljs-number">0.4</span>, <span class="hljs-number">0.5</span>]], <span class="hljs-comment">// vector id and vector</span>
<span class="hljs-string">"filter"</span>: { <span class="hljs-string">"must"</span>: [{<span class="hljs-string">"key"</span>: <span class="hljs-string">"locality"</span>, <span class="hljs-string">"match"</span>: {<span class="hljs-string">"value"</span>: <span class="hljs-string">"Indiranagar"</span>}}] },
<span class="hljs-string">"strategy"</span>: <span class="hljs-string">"best_score"</span>,
}
</code></pre>
</li>
<li data-marpit-fragment="4">DailyMotion (Qdrant), Spotify (Annoy)</li>
</ul>
</section>
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<h2 id="how-to-find-this-thing-on-the-internet">How to find this thing on the internet?</h2>
<ul>
<li data-marpit-fragment="1">No reverse image search</li>
<li data-marpit-fragment="2">No known name</li>
</ul>
</section>
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;--marpit-advanced-background-split:50%;" data-marpit-advanced-background="content" data-marpit-advanced-background-split="right">
<h2 id="strategy-one">Strategy One</h2>
<ul>
<li data-marpit-fragment="1">Describe the thing
<ul>
<li data-marpit-fragment="2">"Combination of human, dragon and chicken"</li>
<li data-marpit-fragment="3">"Mythology creature of human and dragon"</li>
</ul>
</li>
</ul>
</section>
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<h2 id="strategy-two">Strategy Two</h2>
<ul>
<li data-marpit-fragment="1">Search for similar images
<ul>
<li data-marpit-fragment="2">Similarity bubble</li>
</ul>
</li>
</ul>
</section>
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<h2 id="beyond-search-discovery-api">Beyond search: Discovery API</h2>
<ul>
<li data-marpit-fragment="1">
<p>Unique iterative search by Qdrant</p>
</li>
<li data-marpit-fragment="2">
<p>Combine multi-modal vectors in single query</p>
</li>
<li data-marpit-fragment="3">
<pre is="marp-pre" data-auto-scaling="downscale-only"><code class="language-js"><span class="hljs-variable constant_">POST</span> collections/my-collection/points/discover
{
<span class="hljs-string">"target"</span>: [<span class="hljs-number">0.63</span>, <span class="hljs-number">0.10</span>, <span class="hljs-number">0.91</span>, <span class="hljs-number">0.55</span>],
<span class="hljs-string">"context"</span>: [
{
<span class="hljs-string">"positive"</span>: <span class="hljs-number">7125</span>, <span class="hljs-comment">// <-- ID of the example</span>
<span class="hljs-string">"negative"</span>: <span class="hljs-number">122</span> <span class="hljs-comment">// <-- This can also be a vector</span>
}
],
}
</code></pre>
</li>
</ul>
</section>
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<h1 id="summary">Summary</h1>
<ul>
<li data-marpit-fragment="1">
<p>Anything => vector</p>
</li>
<li data-marpit-fragment="2">
<p>Vectors >> similarity search</p>
</li>
<li data-marpit-fragment="3">
<p>Thousands of use-cases with Qdrant</p>
</li>
</ul>
<ul>
<li data-marpit-fragment="4">Find me at
<ul>
<li data-marpit-fragment="5">kshivendu.dev/bio</li>
<li data-marpit-fragment="6">kshivendu.dev/twitter</li>
</ul>
</li>
</ul>
</section>
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## Topics to cover
* Evolution of search
* Traditional approaches and drawbacks
* Vectors search and how it harnesses ML models
* Qdrant
* Building HNSW index and vector search
* Beyond similarity search:
* Recommendations
* Discovery
* Sparse vectors</p></div><div class="bespoke-marp-note" data-index="3" tabindex="0"><p>Replace search space image</p></div><div class="bespoke-marp-note" data-index="4" tabindex="0"><p>Image showing vector search</p></div><div class="bespoke-marp-note" data-index="13" tabindex="0"><p>---
## How discovery uses that?
##### Remember Metric Learning?
---</p><p>
---</p><p>## How multi-modal embeddings look like?
---
---
---</p><p>## Sparse vectors
* VS text search
* BM25 & TF-IDF
* Transformer's attention weights
* SPLADE
---</p><p>* Navigating search (read vector) space is powerful!</p><p>* Thank you!</p></div><script>/*!! License: https://unpkg.com/@marp-team/[email protected]/lib/bespoke.js.LICENSE.txt */
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