Redis + AI: Building a Vector Database with Redis

Via LinkedIn Learning

LinkedIn Learning based on 6 ratings

Likes

Overview

Redis + AI: Building a Vector Database with Redis is an in-depth course tailored for developers, data engineers, and AI practitioners interested in combining the speed and versatility of Redis with modern AI-powered vector database capabilities. This course guides you through leveraging Redis as a powerful platform for managing and querying high-dimensional vector data generated by AI models. You will explore the core concepts behind vector embeddings and similarity search, and learn how to store, index, and retrieve vectors efficiently using...

Syllabus

Introduction

Introduction
What you should know

1. Understanding Data

What is structured data and where does it come from?
What is unstructured data and where does it come from?
Using structured data
Using unstructured data: Use case examples
Which is better? Structured vs. unstructured data
Unstructured to structured data
Practical example: Metadata

2. AI-Optimized Databases

What are AI-optimized databases?
What are vector databases?
What are embeddings?
How do vector databases work?
Examples of use cases for vector databases

3. Enter Redis

Quick introduction of RediSearch and how to get it
Redis used as a vector database
Using Redis as a recommendation engine: Architecture overview
Using Redis as a documentation retrieval database: Architecture review

4. Building an Image Similarity Search

Introduction to the project to solve
Architecture review: Using Redis as a vector database
Tech stack overview
Implementation overview: A deep dive into the main aspects of the implementation

5. Building a Semantic Search Example