<?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/1a8af684822a43e9b4b2518c406addc6&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/1a8af684822a43e9b4b2518c406addc6-a62aeb08bd78ba86.gif</thumbnail_url><duration>272.056</duration><title>DocuMind AI: End-to-End GenAI Document Chatbot Using LLMs &amp;amp; Vector Search</title><description>In this video, I walk you through my project, Document AI, which uses FastAPI for the backend. We handle file uploads and convert documents into text using a document loader. The chat functionality allows users to query relevant information from these documents using a query engine and LLM. The front end features a user-friendly landing page with options for document uploads and voice queries. I encourage viewers to explore the application and provide feedback.</description></oembed>