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IBM

Vector Databases for RAG: An Introduction

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!

状态:Database Management
状态:Generative AI Agents
中级课程小时

精选评论

AA

5.0评论日期:Dec 3, 2025

Labs were outstanding. Made all my concepts clear.

AR

5.0评论日期:Jan 8, 2026

Following these labs which are written in Skills Network Labs is a bit cumbersome. Jupyter Notebooks were much easier to follow in previous courses.

AA

5.0评论日期:Oct 9, 2025

Very detailed and useful course in tersm of understanding fundamentals of Vector DBs. The last Lab project is very useful.

VN

5.0评论日期:Sep 6, 2025

I got clear understanding of Vector DB in this course

NA

5.0评论日期:Oct 2, 2025

Superb Content. Star of the show is the new AI Powered Podcast capability. The live interaction in it is something that really blew my mind.

RS

5.0评论日期:Mar 28, 2026

Brief yet complete in-depth understanding of vector databases along with a clear application taught and shown with RAG.

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Azzam Radman
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