AWS Vector Databases Part 1: Embeddings, Dimensions & Similarity

This is Part 1 of a series exploring vector databases on AWS. We recently evaluated multiple AWS vector database options to understand their trade-offs, performance characteristics, and real-world use cases. Before comparing services, it’s important to understand the core concepts that power vector search. In this part, we’ll cover embeddings, dimensions, and similarity search — the foundation of every RAG and semantic search system. What Are Embeddings? Let’s say you're building a customer support chatbot. A user asks: “How do I change my login info?” Your FAQ has: “Resetting your password.” A keyword search might miss this. But as humans, we know they mean the same thing. That’s the gap embeddings solve. An embedding is a numerical representation of content (text, image, code) where similar meaning leads to similar numbers. So even if the words differ, the intent stays close. How Embeddings Are Created Here's what happens under the hood when you pass a sentence to an embedding model: