Vector Databases Demystified: Part 1 - An Introduction to the World of High-Dimensional Data Storage

Lately, I've seen people talking more and more about "vector databases," especially when it comes to making their own AI tools. I wasn't quite sure what this meant, so I took some time to learn more. In this article, I'll share what I found out.

To start us off, let's first break it down into its two main components: vectors and databases:

A vector is an ordered list of numerical values or elements that can represent various types of data, such as coordinates in a multi-dimensional space, features of an object, or points in a graph. Vectors are essential in various fields, including computer graphics, machine learning, and data science.

A database, on the other hand, is an organised collection of data that can be accessed, managed, and updated. In computer systems, databases are used to store and manipulate large amounts of structured or semi-structured data efficiently.

Now, let's combine these concepts to understand what a vector database is. A vector database is a specialised type of database designed to store, manage, and manipulate large collections of vectors efficiently. These databases are particularly useful when working with high-dimensional data, such as images, videos, and text, as they can handle complex mathematical operations, search, and comparison tasks quickly.

Some key features of vector databases are:

1. High-dimensional indexing: Vector databases provide an efficient way to index high-dimensional data, allowing for quick search and retrieval of similar vectors based on their similarity or distance in the vector space.
2. Scalability: These databases can handle large datasets, scaling up to millions or billions of vectors while maintaining efficient search and retrieval times.
3. Similarity search: Vector databases support similarity search, which allows users to find the most similar vectors to a given query vector. This is particularly useful in applications like content-based recommendation systems, image or document search, and clustering.
4. Distributed storage and processing: Many vector databases are designed to work with distributed systems, allowing for parallel processing and storage across multiple nodes or machines. This enables them to handle even larger datasets and improve search and retrieval times.

Some popular vector databases and libraries include FAISS (Facebook AI Similarity Search) and Annoy (Approximate Nearest Neighbors Oh Yeah) by Spotify.

To sum it up, vector databases are special databases made to easily handle lots of big lists of numbers, called vectors. They're really important in areas like machine learning, data science, and computer graphics because they help us quickly find, compare, and get similar vectors from huge sets of data.

I hope this introduction to vector databases has caught your interest and helped you understand the basics. In the next part of this series, I'll dig deeper and show you how to build your very own vector database using Python. I'll guide you through the process step by step, making the code easy to understand even if you're new to programming.