Advanced RAG Applications with Vector Databases

This is the repository for the LinkedIn Learning course Advanced RAG Applications with Vector Databases. The full course is available from LinkedIn Learning.

Course Description

Retrieval-augmented generation (RAG) is everywhere these days, and vector databases are what give them their power. But RAG isn’t as simple as some companies claim, so it can be easy to get overwhelmed. In this course, discover state-of-the-art RAG methods, including how to optimize text-based RAG via chunking, embedding, and metadata usage, and how to conduct basic image search with a vector database. You’ll also get a chance to practice multimodal RAG by embedding and storing data and querying images with text. Along the way, instructor Yujian Tang provides practical, hands-on demonstrations and exercise challenges to test out your new skills.

Installing

1. To use these exercise files, you must have the following installed:
   • Python 3.10 or 3.11
   • requirements.txt
2. Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.

Instructor

Yujian Tang

AI Builder

Check out my other courses on LinkedIn Learning.