This module introduces the foundational concepts behind vector embeddings and their role in powering modern AI applications. You’ll learn how text and other data types are converted into high-dimensional vectors using embedding models, enabling semantic understanding and similarity search. We’ll then explore vector databases like FAISS, Chroma, and Pinecone—purpose-built systems optimized for storing, indexing, and retrieving vector representations at scale. Key Learning Outcomes: Understand the concept of vector embeddings and why they matter. Learn how embedding models map data into vector space. Discover how vector similarity search enables applications like semantic search and RAG. Get an overview of vector database features, use cases, and architecture. Run hands-on examples to store and query embeddings from documents.