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‎index.html

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@@ -52,7 +52,8 @@ <h1 class="sitename">LOTUS</h1>
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<nav id="navmenu" class="navmenu">
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<ul>
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<li><a href="#hero" class="active">Home</a></li>
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<li><a href="#about">About</a></li>
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<li><a href="#about">Use Cases</a></li>
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<li><a href="#code-example">Code Example</a></li>
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<li><a href="#team">Team</a></li>
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</ul>
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<i class="mobile-nav-toggle d-xl-none bi bi-list"></i>
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<!-- Section Title -->
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<div class="container section-title" data-aos="fade-up">
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<h2>About</h2>
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<h2>Use Cases</h2>
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<p>LOTUS introduces semantic operators: a concise, declarative model for AI-driven data transformations.</p>
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</div><!-- End Section Title -->
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<div class="col-xl-7" data-aos="fade-up" data-aos-delay="200">
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<div class="col-md-6 icon-box position-relative">
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<i class="bi bi-code-square"></i>
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<h4><a href="" class="stretched-link">Semantic Operators</a></h4>
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<p>LOTUS introduces semantic operators, a declarative and general-purpose programming interface that extends the relational model with composable AI-based operations for bulk semantic queries.</p>
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<i class="bi bi-check-circle"></i>
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<h4><a href="" class="stretched-link">Fact-Checking</a></h4>
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<p>LOTUS is able to achieve 28x speedup and 10% higher accuracy on FEVER than existing fact-checking pipelines through semantic search and filter.</p>
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<h4><a href="" class="stretched-link">Ease of Use</a></h4>
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<p>LOTUS enables achieving state-of-the-art performance with just a few lines of code. Its intuitive interface allows users to implement complex AI-driven data transformations effortlessly.</p>
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<i class="bi bi-tags"></i>
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<h4><a href="" class="stretched-link">Multi-Label Classification</a></h4>
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<p>LOTUS explores the semantic join space and achieves 400x faster joins on while maintaining statistical accuracy guarantees on BioDEX.</p>
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<h4><a href="" class="stretched-link">High Performance</a></h4>
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<h4><a href="" class="stretched-link">Search and Ranking</a></h4>
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<p>
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LOTUS outperforms state-of-the-art AI pipelines, offering up to 28x faster execution with comparable accuracy. It achieves up to 400x acceleration in semantic operations while maintaining statistical accuracy guarantees.
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LOTUS uses semantic indexes in conjunction with sorting based algorithms to achieve 20% faster ranking compared to standard ranking on BEIR.
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</p>
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</div><!-- Icon-Box -->
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<h4><a href="" class="stretched-link">Expressive Power</a></h4>
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<p>The semantic operator model is highly expressive, capturing state-of-the-art AI pipelines in a few operator calls and enabling new pipelines that achieve up to 49.4% higher quality.</p>
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<h4><a href="" class="stretched-link">ArXiv Paper Analysis</a></h4>
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<p>LOTUS can semantically group ArXiv papers and allow users to declaratively set accuracy thresholds to control the latency and quality of the results.</p>
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<div class="container">
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<div class="content">
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<h3>Quickly Build Semantic Pipelines</h3>
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<p>
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This example shows how LOTUS operators can be easily composed to summarize impactful papers related to function calling with large language models.
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First, we use <code>sem_search</code> to find the top 100 papers related to function calling.
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<div class="code-block">
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<pre><code class="language-python">
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papers_df.sem_search("abstract", "function calling", K=100)

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