Introduction to Vector Databases for RAG Applications

Modern AI applications require more than just keyword matching; they need a deep understanding of data context. This course provides a comprehensive introduction to vector databases, the engine behind today's most advanced Retrieval-Augmented Generation (RAG) and recommendation systems. You will transition from understanding basic data structures to implementing sophisticated search logic that powers large language models. By learning how to represent information as mathematical vectors, you will be able to retrieve relevant information with high precision and speed. - Understand the fundamental differences between relational databases and vector-based storage systems. - Learn how embedding models transform unstructured text into searchable high-dimensional vectors. - Practice similarity search techniques using distance metrics like cosine similarity and Euclidean distance. - Configure and navigate Chroma DB to store and manage vector collections. - Apply Retrieval-Augmented Generation (RAG) patterns to connect external data to AI models. - Explore modern indexing strategies and metadata filtering for optimized query performance. The course starts with essential terminology and the mathematical foundations of vectors before moving into practical database operations and RAG architecture. You will engage with written explanations and code-based exercises designed to solidify your understanding of the modern AI data stack. This course is built for beginners and aspiring AI developers who want to understand the infrastructure of modern search; no previous experience with vector databases or machine learning is necessary. Begin your journey into the world of high-dimensional data and AI retrieval.