What is a Vector Database?

A vector database is a type of database designed to store, manage, and search vector embeddings. It’s built for “similarity search,” meaning it can find and retrieve data points that are conceptually or semantically similar to a query, rather than just matching keywords.

How it Works

1. Vector Embeddings: Unstructured data like text, images, or audio is converted into numerical representations called vector embeddings. Think of a vector as a long list of numbers that captures the meaning of the data. Similar items will have vectors that are “close” to each other in a multi-dimensional space. For example, the vector for “dog” might be very close to “puppy” but very far from “car.”
2. Indexing: The vector database indexes these embeddings using specialized algorithms. This allows it to quickly search through millions or billions of vectors without checking every single one.
3. Similarity Search: When you perform a search, the database takes your query, converts it into an embedding, and then finds the closest or most similar vectors in its index. The results are ranked by how close they are to your query’s vector.

Vector databases are essential for modern AI applications like generative AI, semantic search, and recommendation systems.