Vector Databases: Foundations of Semantic Search and AI

As artificial intelligence and large language models reshape the technology landscape, standard relational databases are no longer enough to handle unstructured data. Understanding how to store, index, and query high-dimensional vector data is now an essential skill for modern software and data professionals. In this text-based course, you will transition from a traditional database mindset to mastering semantic search. You will understand how vector databases power recommendation engines, question-answering systems, and intelligent search tools, giving you the practical knowledge to design and implement vector-based architectures. What you'll learn: - Understand the core concepts of vector embeddings, high-dimensional spaces, and distance metrics like cosine similarity. - Compare popular vector database architectures and indexing methods, such as HNSW and IVF. - Configure vector databases to store, query, and manage unstructured text and image data. - Apply metadata filtering and hybrid search techniques to improve query accuracy. - Implement Retrieval-Augmented Generation (RAG) patterns to connect LLMs with external vector knowledge bases. The journey begins with foundational definitions of embeddings and similarity metrics before moving into indexing strategies and practical query operations. You will read clear explanations and analyze structured code examples to see how these databases integrate with modern AI pipelines. This course is designed for software developers, data analysts, and tech enthusiasts who are new to vector databases. No prior experience with artificial intelligence is required, though a basic understanding of database concepts and Python is helpful. Start reading today to unlock the potential of semantic search and modern AI data storage.