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RAG: Vector Databases with ChromaDB

PremiumIntermediateCourse

Learn vector databases, similarity search, and ChromaDB operations to build recommendation systems, and improve AI retrieval performance in GenAI applications using embeddings.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • ChromaDB, Generative AI, Hugging Face, RAG, Similarity Search, Vector Databases

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 9 Hours

Platform

  • SkillsNetwork

Last Update

  • March 19, 2026
About this Course
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). 

After completing this course, you will be able to: 

  • Describe the core principles of vector databases and how they compare to traditional data systems
  • Perform key vector database tasks in ChromaDB, including managing collections and embeddings
  • Use similarity search methods to analyze and retrieve data across vector spaces
  • Create a functional recommendation system utilizing vector databases and embedding models
  • Interpret how vector storage, indexing, and retrieval mechanisms work internally
  • Develop a foundational understanding of how vector databases integrate into RAG pipelines

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.

The following skills are required to be successful with this course: 
 
  • Python programming
  • Familiarity with Databases
  • Generative AI Application Development Fundamentals


Course Syllabus


Welcome to the Course
  • Course Introduction
  • Course Overview
  • RAG and Agentic AI Professional Certificate Overview
  • Reading: Helpful Tips for Course Completion

Module 1: Introduction to Vector Databases and Chroma DB
  • Module Summary and Learning Objectives
  • Introduction to Vector Databases and Similarity Search
    • Vector Database Concepts
    • Reading: Vector Databases Versus Traditional Databases
    • Vector Database Types
    • Applications of Vector Databases
    • Reading: Similarity Search
    • Lab: Similarity Search by Hand
    • [Optional] Interactive Lesson Recap Podcast (AI-Powered)
    • Practice Quiz: Introduction to Vector Databases and Similarity Search
  • Exploring Chroma DB
    • Chroma DB Key Concepts and Architecture
    • Reading: Chroma DB Filtering
    • Reading: Similarity Search and HNSW in Chroma DB
    • Lab: Similarity Search on Text Using Chroma DB and Python
    • [Optional] Interactive Lesson Recap Podcast (AI-Powered)
    • Practice Quiz: Exploring Chroma DB
  • Module Summary and Evaluation
    • Reading: Summary and Highlights: Introduction to Vector Databases and Chroma DB
    • Reading: Cheat Sheet: Introduction to Vector Databases and Chroma DB
    • Graded Quiz: Introduction to Vector Databases and Chroma DB
    • [Optional] Discussion Prompt: Meet and Greet

Module 2: Vector Databases for Recommendation Systems and RAG
  • Module Summary and Learning Objectives
  • Chroma DB Database Operations
    • Essential Database Operations in Chroma DB
    • Lab: Similarity Search on Employee Records using Python and Chroma DB
    • [Optional] Interactive Lesson Recap Podcast (AI-Powered)
    • Practice Quiz: Chroma DB Database Operations
  • Develop a Recommendation System and Connect Learned Concepts
    • How Vector Databases Power RAG
    • Lab: Practice Project: Food Recommendation System Using Chroma DB
    • [Optional] Interactive Lesson Recap Podcast (AI-Powered)
    • Practice Quiz: Develop a Recommendation System and Connect Learned Concepts
  • Module Summary and Evaluation
    • Summary and Highlights: Vector Databases for Recommendation Systems and RAG
    • Reading: Vector Databases for Recommendation Systems and RAG
    • Graded Assignment: Graded Quiz: Vector Databases for Recommendation Systems and RAG

Course Wrap Up
  • Course Wrap-Up
  • Congratulations and Next Steps
  • Team and Acknowledgments