By :Seraphina Quinn
It’s not hype. It’s the future, here today. For a long time, generative AI was the stuff of fantasy – computers that think and reason, so you could be forgiven for initially dismissing it as another rettech ch tall tale.
But that point is long past. Generative AI is already revolutionizing countless industries and is set to pump billions, if not trillions, into the economy.
If generative AI is the technological embodiment of the future, vector databases are its beating heart.
They are what allows data to be organized in such a way that powerful AI can reach for remarkably accurate answers to complex questions with lightning speed, and they continue to be a driving force in generative AI’s incredible progress.
What is a Vector?
A vector is a piece of data that has several components. In math and science, it is a quantity that has a magnitude, or size, and a direction.
In data science it’s a representation of a piece of data. This could be an ordered list or a sequence of numbers. The important thing is that each item in that list or sequence represents a feature of the data.
What’s a Vector Database?
Any piece of data, structured or unstructured, can be turned into a vector – or vectorized.
Whether it’s a piece of text, an image, a video or a piece of audio, it can be turned into an ordered list or array where each number represents a feature of that data.
This process of transforming data into vectors, or embeddings, allows those different types of data to be represented in a consistent format.
Not only does this make it possible to search through different types of data, but it also means similarities can be found between those different pieces of data.
A vector database stores, retrieves, and searches for vectors. It allows you to look for those similarities instead of exact matches.
Why does this Matter for AI?
Vector databases have been transformational in the progress of AI applications because they encapsulate the semantic essence of a piece of data, as well as the context of the original data.
This is a game-changer.
Relational databases allow you to look for exact matches, which often doesn’t quite return what the searcher is looking for – especially as we increasingly think of searches as the act of asking a question rather than scouring a list.
Searching “restaurants near me” will, when searching a relational database, look for entries that include those words.
Vector databases will look for results that share similarities with the search components. “Restaurants” means food, “near me” means close to current GPS data and so on.
Why does this Matter to Us?
From a developer’s perspective, vector databases can handle data points that have many thousands of dimensions, allowing algorithms to be optimized for effective similarities. The ways in which data can be connected increase exponentially.
Vector search also offers more flexibility, allowing for new data types to be accommodated.
From a user’s perspective, this means results beyond the limits of your search. Recommendation systems and image recognition can leverage this focus on similarity rather than exact matches to return results the user wants without realizing they were looking for them.
What Next?
Because vector databases create consistent data formats, and can manage data that has many dimensions, they can search for similarities with remarkable speed.
This is especially important when it comes to systems that rely on augmented reality – that is using a camera or other input to recognize something in the real world and return with some information about it. For example, a lens that comes back with some information about what it sees.
This technology only really works if it happens in real-time, otherwise it will never be helpful. So the speed and flexibility afforded by vector databases really is changing the way our technology is integrated with our lived environment.
So, the next time a chatbot comes back with a suggested solution that seems remarkably sensible, it’s because the system powering the AI application has been able to vectorize your search query and look for similarities among the high-dimensional data points of its vector database.
Published by: Nelly Chavez
