<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/da198d42181a49499ee7a808308f3a31&quot; frameborder=&quot;0&quot; width=&quot;1774&quot; height=&quot;1330&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1330</height><width>1774</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1330</thumbnail_height><thumbnail_width>1774</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/da198d42181a49499ee7a808308f3a31-8481722f1a0dd2af.gif</thumbnail_url><duration>802.6084</duration><title>Local RAG System Demo</title><description>Hey there, I&apos;m Henry, and in this video, I&apos;ll be showcasing our CISC-452 Neuron Genetic Computing final project. We&apos;ve developed a local reg system to test retrieval augmented generation capabilities using LLAMA 3.2 with the FAISS or FACE vector store. I&apos;ll walk you through the code demo and setup process, explaining how we manage conversation memory, ingest PDF files, and perform similarity metrics. No action requested, just enjoy the demo!</description></oembed>