This course teaches how data vectorization and vector databases enable context-based search over keyword matching, multimodal data search, enhance recommendation systems, and power LLMs.

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Vector databases transform how we search, analyze, and recommend data in today’s AI-driven world. These databases are at the heart of modern applications like semantic search, multimodal search, recommendation systems, and retrieval augmented generation (RAG) for large language models. By using embeddings, which are numerical representations that capture the meaning of data, vector databases allow us to find similar information quickly and accurately, even across vast datasets. This makes them essential for building intelligent systems that understand data beyond simple keyword matching.

In this vector databases course, you’ll learn to generate embeddings for various data types and use vector databases to store and query them. Using the power of embeddings and vector databases, you’ll build semantic search apps, recommendation systems, and multimodal search solutions. 

After completing this course, you’ll have the skills to determine when and how to effectively apply vector databases to different projects.

Vector Databases: From Embeddings to Applications

Get an introduction to the music recommendation system that we are going to develop in this chapter.

Introduction to the Music Recommendation System

Before Getting Started

Getting Started with Vector Databases and Embeddings

Working with Vector Databases

Developing a Music Recommendation System

Wrapping Up

Semantic Search with Transformers

Introduction to the Music Recommendation System