Vector Databases and Embeddings with ChromaDB

As artificial intelligence and large language models reshape software, traditional relational databases are no longer enough to handle semantic data. Modern applications require specialized storage to manage complex high-dimensional vectors efficiently. This text-based course guides you through the foundational concepts of vector databases and semantic search, showing you how to implement these technologies in your own projects. You will transition from a beginner to a confident practitioner capable of managing embeddings and building intelligent search features. Through clear explanations and practical code examples, you will learn how to structure data for retrieval-augmented generation (RAG) and semantic similarity. What you'll learn: - Understand the core concepts of vector embeddings and high-dimensional space - Configure and initialize ChromaDB for local and persistent storage - Generate and manage vector embeddings using modern embedding models - Query vector databases using semantic similarity and metadata filtering - Apply vector storage solutions to retrieval-augmented generation (RAG) workflows - Practice your knowledge with structured review questions and coding exercises This course starts with essential terminology and mathematical intuition before moving into hands-on configuration, database operations, and practical query patterns. You will read through clear architectural explanations and analyze real-world implementation code. This course is designed for software developers, data enthusiasts, and AI beginners who want to understand vector search from the ground up. No prior experience with vector databases is required, though a basic understanding of Python is helpful. Begin reading today to master the storage engine behind modern AI applications.