BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250606T003417Z UID:F710C1CA-0505-43C6-8D60-B124995D3586 DTSTART;TZID=America/New_York:20250605T190000 DTEND;TZID=America/New_York:20250605T203000 DESCRIPTION:About the panelist:\n\nDevang Parekh is a Software Engineer at FIYGE Research in Toronto\, with expertise in Backend Development and Appl ied Machine Learning. He has driven innovative projects at FIYGE\, leverag ing his technical skills to build robust solutions. Previously\, Devang me ntored students as a CS Club Mentor at the University of Waterloo\, enhanc ing a passion for technology. As a proud Waterloonian\, he is dedicated to mentoring aspiring engineers and advancing the tech community through kno wledge-sharing and collaboration.\n\n[event details]\n\nThis event will in troduce participants to Retrieval-Augmented Generation (RAG) and Vector Da tabases\, emphasizing their roles in enhancing AI-driven applications such as agentic applications\, text generation\, and search\, as well as enabl ing autonomous systems. The session will blend theoretical explanations wi th live demonstrations\, ensuring attendees gain both a conceptual underst anding and practical exposure.\n\nBy the end of this session\, participant s will:\n\n- Understand the fundamentals of RAG and its importance in AI a pplications.\n- Learn about vector databases and how they enhance search a nd retrieval efficiency.\n- See real-world applications of RAG and vector search in NLP and AI systems.\n- Explore visualization tools such as Tenso rFlow Embedding Projector to understand vectorization.\n- Gain hands-on in sights into implementing RAG with vector databases like Chroma or Pinecone .\n\nSession Breakdown:\n\n- Introduction to RAG\n- What is Retrieval-Augm ented Generation (RAG)?\n- How does it improve AI models?\n- Use cases in Targeted Text Generation\, Agentic Applications\, Deep Search.\n\n- Fundam entals of Vector Databases\n- What are vector databases?\n- How do they di ffer from traditional databases?\n- Brief on Langchain\n- Key vector datab ase technologies (ChromaDB).\n\n- How Vectorization Works\n- Explanation o f embeddings and vectorization.\n- Demonstration using TensorFlow Embeddin g Projector.\n- Understanding cosine similarity and nearest neighbor searc h.\n\n- Implementing RAG with Vector Databases\n- Overview of integration: LLMs + Vector Databases.\n- Simple implementation walkthrough (Python\, O penAI API + ChromaDB).\n- Performance considerations and best practices.\n \n- Q&A and Closing Remarks\n\nCo-sponsored by: Angelina Ziesemer\n\nVirtu al: https://events.vtools.ieee.org/m/477104 LOCATION:Virtual: https://events.vtools.ieee.org/m/477104 ORGANIZER:adecarvalhoalvarezz@conestocac.on.ca SEQUENCE:183 SUMMARY:Exploring Retrieval-Augmented Generation (RAG) and Vector Databases Enhancing AI with Advanced Information Retrieval URL;VALUE=URI:https://events.vtools.ieee.org/m/477104 X-ALT-DESC:Description: <br /><p>About the panelist:&nbsp\;</p>\n<p><span d ata-olk-copy-source="MessageBody">Devang Parekh is a Software Engineer at FIYGE Research in Toronto\, with expertise in Backend Development and Appl ied Machine Learning. He has driven innovative projects at FIYGE\, leverag ing his technical skills to build robust solutions. Previously\, Devang me ntored students as a CS Club Mentor at the University of Waterloo\, enhanc ing a passion for technology. As a proud Waterloonian\, he is dedicated to mentoring aspiring engineers and advancing the tech community through kno wledge-sharing and collaboration.</span></p>\n<p><span data-olk-copy-sourc e="MessageBody"><img src="https://events.vtools.ieee.org/vtools_ui/media/d isplay/01fd47da-f01c-4c86-b19b-f87d5a405632" alt="event details" width="57 8" height="447"></span></p>\n<p>This event will introduce participants to Retrieval-Augmented Generation (RAG) and Vector Databases\, emphasizing th eir roles in enhancing AI-driven applications such as agentic applications \, text generation\, and search\, as well as enabling autonomous systems. The session will blend theoretical explanations with live demonstrations\, ensuring attendees gain both a conceptual understanding and practical exp osure.</p>\n<p>By the end of this session\, participants will:</p>\n<ul>\n <li>Understand the fundamentals of RAG and its importance in AI applicatio ns.</li>\n<li>Learn about vector databases and how they enhance search and retrieval efficiency.</li>\n<li>See real-world applications of RAG and ve ctor search in NLP and AI systems.</li>\n<li>Explore visualization tools s uch as TensorFlow Embedding Projector to understand vectorization.</li>\n< li>Gain hands-on insights into implementing RAG with vector databases like Chroma or Pinecone.</li>\n</ul>\n<p>Session Breakdown:</p>\n<ol>\n<li>Int roduction to RAG\n<ol>\n<li>What is Retrieval-Augmented Generation (RAG)?< /li>\n<li>How does it improve AI models?</li>\n<li>Use cases in Targeted T ext Generation\, Agentic Applications\, Deep Search.</li>\n</ol>\n</li>\n< li>Fundamentals of Vector Databases&nbsp\;&nbsp\;\n<ol>\n<li>What are vect or databases?</li>\n<li>How do they differ from traditional databases?</li >\n<li>Brief on Langchain</li>\n<li>Key vector database technologies (Chro maDB).</li>\n</ol>\n</li>\n<li>How Vectorization Works\n<ol>\n<li>Explanat ion of embeddings and vectorization.</li>\n<li>Demonstration using TensorF low Embedding Projector.</li>\n<li>Understanding cosine similarity and nea rest neighbor search.</li>\n</ol>\n</li>\n<li>Implementing RAG with Vector Databases\n<ol>\n<li>Overview of integration: LLMs + Vector Databases.</l i>\n<li>Simple implementation walkthrough (Python\, OpenAI API + ChromaDB) .</li>\n<li>Performance considerations and best practices.</li>\n</ol>\n</ li>\n<li>Q&amp\;A and Closing Remarks</li>\n</ol>\n<p>&nbsp\;</p> END:VEVENT END:VCALENDAR