ClickHouse for AI: Building RAG Applications with LLMs and Streamlit

Modern AI applications require databases that can handle massive amounts of analytical data alongside vector embeddings. This text-based course guides you through using ClickHouse as the backbone for Retrieval-Augmented Generation (RAG) and LLM-powered solutions. By reading and working through the practical examples, you will build a fully functional AI application. You will learn how to ingest data, generate and store embeddings, query them efficiently inside ClickHouse, and connect everything to a user-friendly Streamlit web interface. What you'll learn: - Understand ClickHouse architecture, core terminology, and basic table engines. - Configure ClickHouse for vector search to store and retrieve high-dimensional embeddings. - Integrate large language models to perform Retrieval-Augmented Generation. - Build interactive, data-driven web interfaces using Streamlit. - Design efficient data pipelines that connect analytical queries with AI capabilities. - Practice optimizing search queries for speed and cost-efficiency. The course starts with foundational database concepts before diving into vector search mechanics, LLM integration patterns, and interface development. It is designed for beginners, data analysts, and software engineers looking to expand their skills into AI engineering, with no prior ClickHouse experience required. Start reading today and build your first database-backed AI application.