Definition

A vector database stores data as embeddings—lists of numbers that capture meaning. Instead of matching exact words, it helps AI search by finding things that are similar in context, even if the words are different.

Example

A vector database can help an AI find images of ‘dogs playing’ even if you searched for ‘puppies running.’

How It’s Used in AI

Used in semantic search, recommendation systems, chatbots, and RAG (Retrieval-Augmented Generation) apps. It helps AI find the most relevant data by comparing vector distances, not just keywords. This allows smarter, faster, and more accurate results.

Brief History

Vector databases became popular as AI started using embeddings more. Tools like FAISS (from Meta) and newer databases like Pinecone, Weaviate, and Qdrant were built to handle this type of data at scale.

Key Tools or Models

Common tools include Pinecone, Weaviate, FAISS, Qdrant, and Milvus. These are designed to search millions of vectors quickly and power AI applications like chatbots and document search.

Pro Tip

Vector databases don’t replace regular databases—they add meaning-based search. Combine them for the best results.