ChromaDB Foundations: Building RAG and Vector Search Applications

As generative AI applications grow, standard keyword search is no longer enough to find relevant information. Vector databases allow AI models to search by meaning rather than exact words, making them essential for building intelligent retrieval systems. In this text-based course, you will learn the core concepts of vector databases, similarity search, and Retrieval-Augmented Generation (RAG). You will understand how to represent text as embeddings, store them in ChromaDB, and retrieve contextually relevant data to power your generative AI workflows. What you'll learn: Understand the core concepts of vector embeddings and semantic similarity; Configure and manage a local ChromaDB instance to store and query high-dimensional data; Apply modern text chunking strategies to optimize information retrieval for AI models; Implement metadata filtering and query techniques to refine search results; Build a foundational Retrieval-Augmented Generation (RAG) pipeline from scratch. You will start by exploring essential terminology, similarity metrics, and how vector databases differ from traditional relational databases. From there, you will read through step-by-step implementations, building your understanding through structured code explanations and written conceptual exercises. This course is designed for software developers, data enthusiasts, and AI beginners who want to understand modern search architectures without needing prior experience in vector databases. Start reading today to unlock the power of semantic search and RAG in your applications.