How Vector Databases Actually Work (Embeddings, HNSW, and RAG Explained)

Description

Hey guys, welcome back. In this video, I explain exactly what vector databases are and why they are becoming the backbone of modern AI applications. I start by comparing vector databases to traditional relational databases. While SQL is great for structured data like names and prices, it struggles with the unstructured data that AI models use, like text, images, and audio. I show you the core differences in how they store and retrieve information. To make this easy to understand, I go over four real-world examples. I also take you under the hood to explain the math and logic that make this possible. We look at embeddings, high-dimensional latent space, and indexing algorithms like HNSW. You will see how these databases calculate "distance" to find the most relevant information in milliseconds. If you are building AI agents or working with RAG (Retrieval-Augmented Generation), understanding this architecture is essential. If you have any questions/comments, please leave them down below! Thanks for watching! Interactive Demo Code: https://github.com/neupanic/vector-db-demo My Website: https://arpanneupane.com My GitHub: https://github.com/neupanic My Programming Gear: Keyboard: https://amzn.to/47eLpX4 Laptop: https://amzn.to/4546pyW Laptop Stand: https://amzn.to/3GOYlZ7 Desk: https://amzn.to/43wmIEt Monitor: https://amzn.to/42BAaFH Monitor Arm: https://amzn.to/3NR0APS Chair: https://amzn.to/42vvKAn Desk Mat/Mousepad: https://amzn.to/43AhKqj Microphone: https://amzn.to/45RuQ43 Headphones: https://amzn.to/445sf4R #LLM // #Anthropic // #OpenAI