<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>FAISS on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/faiss/</link><description>Recent content in FAISS on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 20 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/faiss/index.xml" rel="self" type="application/rss+xml"/><item><title>Hands-On Project: Building a Multimodal Search Assistant</title><link>https://ai-blog.noorshomelab.dev/multimodal-ai-guide-2026/hands-on-multimodal-search-assistant/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/multimodal-ai-guide-2026/hands-on-multimodal-search-assistant/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Welcome to an exciting hands-on chapter! In our previous discussions, we&amp;rsquo;ve explored the core concepts of multimodal AI, delving into how different data types—text, images, audio, and video—can be processed and integrated. We&amp;rsquo;ve talked about representation learning, data fusion, and the importance of shared embedding spaces. Now, it&amp;rsquo;s time to put that knowledge into action!&lt;/p&gt;
&lt;p&gt;In this chapter, we&amp;rsquo;ll embark on a practical project: building a simple yet powerful &lt;strong&gt;Multimodal Search Assistant&lt;/strong&gt;. Imagine having a personal knowledge base where you can search for information not just by text, but also by what an image looks like, or even a combination of both. This assistant will allow us to index both text documents and images, and then query them using natural language. We&amp;rsquo;ll leverage state-of-the-art pre-trained models to create a shared understanding across modalities, making our search truly multimodal.&lt;/p&gt;</description></item></channel></rss>