Vector Databases and Search Algorithms: Foundations of RAG and Embeddings

As artificial intelligence and large language models reshape software development, storing and searching semantic data has become a critical skill. This text-only course guides you through the foundational concepts of vector databases, embeddings, and high-performance search algorithms. You will transition from understanding basic high-dimensional vectors to implementing efficient similarity search systems. By studying clear written explanations and analyzing code snippets, you will learn how to configure vector indexes, choose similarity metrics, and integrate retrieval-augmented generation (RAG) pipelines. What you'll learn: Understand the core terminology of vector spaces, embeddings, and high-dimensional data representation; Compare similarity metrics including cosine similarity, dot product, and Euclidean distance; Explore search algorithms such as Hierarchical Navigable Small World (HNSW) and Approximate Nearest Neighbor (ANN); Implement text chunking strategies and generate embeddings using modern API standards; Configure and query vector databases to retrieve relevant semantic information; Apply retrieval-augmented generation (RAG) patterns to connect external data with language models. The curriculum begins with essential mathematical concepts and vector representations before progressing to index optimization and practical search implementation. Each concept is reinforced with written exercises and structured code walkthroughs. This course is designed for software developers, data enthusiasts, and beginners curious about AI infrastructure, with no prior vector database experience required. Start reading today to unlock the power of semantic search in your applications.