Exploring Retrieval-Augmented Generation (RAG) and Vector Databases Enhancing AI with Advanced Information Retrieval

#rag #AI #vectordatabases #computer #STEM

About the panelist: 

Devang Parekh is a Software Engineer at FIYGE Research in Toronto, with expertise in Backend Development and Applied Machine Learning. He has driven innovative projects at FIYGE, leveraging his technical skills to build robust solutions. Previously, Devang mentored students as a CS Club Mentor at the University of Waterloo, enhancing a passion for technology. As a proud Waterloonian, he is dedicated to mentoring aspiring engineers and advancing the tech community through knowledge-sharing and collaboration.

This event will introduce participants to Retrieval-Augmented Generation (RAG) and Vector Databases, emphasizing their 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 exposure.

By the end of this session, participants will:

• Understand the fundamentals of RAG and its importance in AI applications.
• Learn about vector databases and how they enhance search and retrieval efficiency.
• See real-world applications of RAG and vector search in NLP and AI systems.
• Explore visualization tools such as TensorFlow Embedding Projector to understand vectorization.
• Gain hands-on insights into implementing RAG with vector databases like Chroma or Pinecone.

Session Breakdown:

1. Introduction to RAG
   1. What is Retrieval-Augmented Generation (RAG)?
   2. How does it improve AI models?
   3. Use cases in Targeted Text Generation, Agentic Applications, Deep Search.
2. Fundamentals of Vector Databases  
   1. What are vector databases?
   2. How do they differ from traditional databases?
   3. Brief on Langchain
   4. Key vector database technologies (ChromaDB).
3. How Vectorization Works
   1. Explanation of embeddings and vectorization.
   2. Demonstration using TensorFlow Embedding Projector.
   3. Understanding cosine similarity and nearest neighbor search.
4. Implementing RAG with Vector Databases
   1. Overview of integration: LLMs + Vector Databases.
   2. Simple implementation walkthrough (Python, OpenAI API + ChromaDB).
   3. Performance considerations and best practices.
5. Q&A and Closing Remarks