Vector Databases
Understand what vector databases are and how they power retrieval-augmented generation and semantic search in modern AI systems.
What is a Vector Database?
A vector database stores data as high-dimensional numeric vectors (embeddings). Similar vectors are located “near” each other in vector space, allowing fast nearest-neighbour search. This makes vector databases ideal for semantic search, recommendation engines, and retrieval-augmented generation (RAG).
Why Use One?
- Sub-second similarity search over millions of items.
- Language-agnostic: works for text, image, audio, or code embeddings.
- Scales horizontally and supports real-time updates.
🧒 Explain Like I’m 5
Think of a vector database like a magical toy box that groups toys that aresimilar together—even if they don’t share the same name. Put in a “dog” figurine and the box knows “puppy” and even “cat” are close by because they’re all animals. This makes it super fast to find things that are alike!