Vector Databases: Foundations of Semantic Search and RAG

As artificial intelligence and large language models scale, traditional relational databases struggle to search unstructured data like text, images, and audio. Vector databases solve this by enabling ultra-fast similarity search across millions of high-dimensional data points. This text-based course guides you from the fundamental mathematics of embeddings to deploying and querying vector databases. You will understand how to convert unstructured data into vector representations and implement efficient retrieval systems that ground AI models with relevant context. What you will learn: 1. Understand the core concepts of vector embeddings and high-dimensional space. 2. Compare key vector indexing algorithms like HNSW, IVF, and Flat indexing. 3. Configure and query popular vector databases such as Chroma, Pinecone, and pgvector. 4. Implement similarity search metrics including Cosine Similarity, Euclidean Distance, and Dot Product. 5. Apply vector search patterns to build Retrieval-Augmented Generation (RAG) pipelines for AI applications. 6. Optimize search performance and manage index trade-offs between speed, accuracy, and memory. You will begin with basic definitions of vector space and similarity metrics before moving on to hands-on text-based tutorials demonstrating indexing strategies and database configuration. The course concludes with practical architectural patterns for connecting your vector store to modern language models. This course is designed for software developers, data enthusiasts, and AI beginners who want to understand the storage layer of modern AI applications. No prior experience with vector databases or machine learning is required. Start reading today to master the core infrastructure powering modern semantic search and generative AI.