Vector databases are transforming how modern AI systems retrieve and understand information. In this course, you’ll explore the core principles of vector databases, how they compare to traditional databases, and why they are essential in recommendation systems and Retrieval-Augmented Generation (RAG).
You’ll learn foundational concepts like embeddings, vector operations, and similarity search while gaining a practical understanding of ChromaDB’s architecture and capabilities.
Through guided, hands-on labs, you’ll build and manage collections, load embeddings, and perform similarity searches using real datasets. You’ll gain experience executing core database operations in ChromaDB, including updating, deleting, and maintaining collections for scalable retrieval workflows.
Finally, you’ll apply your skills by building a functional recommendation system using ChromaDB and a Hugging Face embedding model. This real-world project reinforces how vector databases enable accurate, context-aware search in AI applications.
By the end of the course, you’ll understand the internal mechanisms behind RAG and vector databases, and you’ll be ready to build retrieval-powered systems that deliver intelligent, relevant results.
