Working with Vector Databases: Foundations and Practice

As generative AI and large language models reshape technology, traditional relational databases are no longer enough to handle unstructured data. Vector databases have emerged as the essential infrastructure for managing high-dimensional data and powering semantic search. In this text-based course, you will transition from database basics to confidently structuring, querying, and managing vector data. You will gain a clear, conceptual understanding of how embeddings work and how to leverage vector databases for real-world applications. What you'll learn: - Understand the core concepts of vector embeddings and high-dimensional spaces. - Compare popular open-source vector databases and their unique architectures. - Apply similarity search metrics such as cosine similarity and Euclidean distance. - Configure vector indexes using algorithms like HNSW and IVF for optimized retrieval. - Integrate vector databases into retrieval-augmented generation (RAG) workflows. - Practice querying and managing vector data through written scenarios and conceptual exercises. The course begins with foundational definitions of vector search before guiding you through indexing strategies, database selection, and integration patterns. You will consolidate your learning with structured, text-based practice scenarios designed to test your understanding. This course is designed for software developers, data enthusiasts, and AI beginners who want to understand modern data storage without needing prior experience in vector math. Start reading today to unlock the power of semantic search and modern AI data retrieval.