Practical Vector Databases: Build Semantic Search and RAG Systems

Modern AI applications rely on retrieving the right information quickly, which makes vector search an essential skill for developers today. Understanding how to store, query, and manage high-dimensional embeddings is the key to building intelligent search engines and reliable AI assistants. This text-based course guides you through the fundamentals of vector databases and semantic search. You will transition from understanding basic high-dimensional vectors to deploying and querying industry-standard vector databases, enabling you to build robust Retrieval-Augmented Generation (RAG) systems. What you'll learn: - Understand the core concepts of vector embeddings, similarity metrics, and high-dimensional space. - Deploy and configure leading vector databases including Qdrant, Weaviate, and FAISS. - Implement k-NN search with metadata filtering for precise data retrieval. - Combine semantic vector search with traditional keyword search (BM25) to create hybrid search engines. - Build and optimize Retrieval-Augmented Generation (RAG) pipelines to answer questions using custom data. - Apply modern best practices for indexing, scaling, and managing vector databases. You will start with foundational concepts of vector math and embedding generation before moving into hands-on database operations. Through clear written explanations and structured code examples, you will learn how to initialize databases, insert embeddings, and build hybrid search queries. This course is designed for software developers, data enthusiasts, and beginners looking to enter the world of AI engineering. No prior experience with vector databases is required. Start reading today to master the infrastructure behind modern AI search.