Vector Databases for RAG: An Introduction

Overview

Gain expertise in using vector databases and improve your data retrieval skills in this hands-on course! During the course, you’ll explore the fundamental principles of similarity search and vector databases, learn how they differ from traditional databases, and discover their importance in recommendation systems and Retrieval-Augmented Generation (RAG) applications. You’ll also dive into key concepts such as vector operations and database architecture to develop a strong grasp of Chroma DB's functionality. You’ll gain practical experience using Chroma DB, a leading vector database solution. And through interactive labs, you’ll learn to create collections, manage embeddings, and perform similarity searches with real-world datasets. You’ll then apply what you’ve learned by creating a real-world recommendation system powered by Chroma DB and an embedding model from Hugging Face; an ideal project to demonstrate your understanding of how vector databases improve search and retrieval in AI-driven applications. If you’re keen to gain expertise in using vector databases and similarity searches, both essential components of the RAG pipeline, then enroll today!

Syllabus

Introduction to Vector Databases and Chroma DB

This module explores the transformative role of vector databases in modern data management systems. Participants will learn how vector databases serve as the foundation of recommendation systems and how they differ from traditional databases in storing and managing vector data. Various vector database types and their specific applications will be examined, with a special focus on Chroma DB. Insights into Chroma DB architecture, common coding practices for its operations, and hands-on skills in performing basic vector operations will be provided. Moreover, similarity search will be thoroughly explained, with hands-on labs that perform similarity searches manually and using Chroma DB.

Vector Databases for Recommendation Systems and RAG

This module explores the connections between vector databases, similarity search, recommendation systems, and Retrieval-Augmented Generation (RAG), while also providing instruction on essential database operations using Chroma DB. By analyzing employee data and building a food search recommendation system using hands-on labs, participants will gain a deeper understanding of both recommendation systems and Chroma DB. Moreover, the module highlights the critical role vector databases and similarity searches play within RAG systems.