The vector database is fast becoming ubiquitous amongst those interested in data management and how it is being used to power applications such as artificial intelligence (AI).
In this digital transformation, efficient data processing has become paramount for applications relying on large language models (LLM), generative AI, and semantic searches. Traditional databases, such as relational databases, cannot cope with the ever-increasing amount of data being generated. NoSQL databases, although much more flexible and scalable, can’t perform the similarity searches that allow for the training of AI models. The vector database, however, is specifically designed to process data in a way that allows for discerning patterns, relationships, and underlying structures. For those interested in the future of data management, here is our ultimate guide to using a vector database.
A Vector Database Defined
Vector databases are unique because they store data on multi-dimensional vectors. Each of these vectors represents specific characteristics or qualities and can have varying dimensions, from a few to thousands, depending on how complex the data is. Once stored in a vector database, users can retrieve data based on vector proximity or similarity – known as a similar search.
What is a Vector?
In math and physics, a vector is a quantity that has both magnitude and direction. In vector databases, a vector is an embedding that holds all the data points of an inputted object in a list of numbers that can be broken down into many components. For example, an image of a car would contain data points on the brand, the color, the background, the image size, and much more. The image would be inputted into an embedding model, which would use an algorithm to convert the image into a vector embedding.
What Type of Data Can a Vector Hold?
One of the key advantages of a vector database is that it can hold any type of data. The most common usage is the storing and sorting of unstructured data. This is data without a fixed schema and can include everything from documents, text, and PDFs to audio, video, and image files. Nearly all of the data collected today is unstructured data, which is why vector databases are becoming extremely valuable.
How is the Data Stored in a Vector Database?
Once the input is converted into a vector embedding, it is stored and organized in the database through vector indexing. Unlike traditional databases, where each data point is singular and used to find exact matches, the vector contains many data points, and a vector database will group all the similar data points together in clusters. Take the image below:
In this vector database, we have data points on pets, fruit, and tech companies and they are all grouped together based on their category. However, we can also see how data points outside of their closest relationships are also grouped in proximity based on similarities. For example, apple the fruit and Apple the company are in close proximity to each other, but not as close as the apple and banana, which have a more direct relationship. This form of storing data allows the database to perform similarity searches.
Similarity Searches
Of all the databases, vector databases are very similar to how the human brain processes and retrieves information. When we think of something, we are not just looking for exact matches. Instead, we find patterns and relationships when recalling a memory. This semantic understanding in a vector database means that even if two pieces of data aren’t identical but are contextually or semantically similar, they can be matched. Using the above image again, the query is the word kitten.
The database uses a similarity search to find similar matches such as vectors on cats. Alongside the similar matches, it would also match the kitten query to vectors on puppies or wild cat cubs because they are contextually similar.
Why Use Vector Databases
Vector databases are most efficient when dealing with high-dimensional data and are adept at handling data points spanning hundreds or even thousands of dimensions. The unique way vector databases store and search for data allows for the training of AI applications, recommendation systems, and programs like image recognition.
Using an LLM as an example, a vector database allows the AI model to learn beyond its generic training data. In conjunction with a Retrieval Augmented Generation (RAG), a vector database that is constantly up-to-date with the latest information means the LLM can retrieve the relevant information when processing a query accurately.
The same process applies to recommendation systems on ecommerce stores or streaming services. Using the continuously updated data stored in the vector database, a recommendation system will find similar matches to the user’s query in order to recommend products, films, songs, etc. While vector databases are not ideal for exact matches, their ability to compare data based on similarity and context makes them useful in industries such as finance, healthcare, and cybersecurity.
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