What is a vector database?

A vector database is a type of database that stores, manages and retrieves data as high-di­men­sion­al vectors. Read on to find out more about how vector databases work and when they are used.

What does a vector database do?

As with tra­di­tion­al struc­tured databases, vector databases can store many different types of data, including text, images and other types of media. The dif­fer­ence between the two lies in how the data is stored and queried. While data in regular databases is often stored in an indexed tabular format, data objects in vector databases are rep­res­en­ted as high-di­men­sion­al numeric vectors. The values contained in a vector can be un­der­stood as para­met­ers that each describe a property of the original data. In this way, data sets can be para­met­erised and then compared and bundled according to sim­il­ar­ity metrics.

With vector databases, it is much easier to cat­egor­ise and query data according to its rough prop­er­ties. This is par­tic­u­larly ad­vant­age­ous for machine learning and deep learning systems.

How do vector databases work?

Compared to tra­di­tion­al re­la­tion­al databases, vector databases offer many ad­vant­ages in the field of ar­ti­fi­cial in­tel­li­gence and machine learning. However, there are some chal­lenges in storing and managing vector data. The first major challenge of these databases is the con­ver­sion of tra­di­tion­al digital data objects to numeric vectors that ac­cur­ately represent the prop­er­ties of these data objects. This is where vector embedding models come into play.

Vectors can be un­der­stood as co­ordin­ate points in a mul­ti­di­men­sion­al space. The high-di­men­sion­al space where the vectors stored in a vector database are located is called vector embedding. To get from a digital data object to a cor­res­pond­ing vector embedding, you need a vector embedding model. A vector embedding model is a spe­cial­ised machine learning model that analyses data objects and generates a suitable vector rep­res­ent­a­tion based on their meaning and context.

Let’s consider a vector database that stores and cat­egor­ises words as an example. The words ‘Sushi’ and ‘Pasta’, despite their different spellings, share a similar semantic meaning. Therefore, the embedding model should produce similar vector em­bed­dings for these words. To ac­com­plish this, the model could analyse the textual contexts in which both words commonly appear.

Re­triev­ing data from the vector database is done similarly to the process of entering data. The embedding model generates an ap­pro­pri­ate vector (co­ordin­ate point in high-di­men­sion­al space) for the query. Spe­cial­ised math­em­at­ic­al al­gorithms for vectors are then used to find the closest vectors. This approach allows for the retrieval of not only exact matches but also data objects whose vectors are similar to the query vector. For instance, if you query ‘food’, the results might include entries like ‘pasta’ and ‘sushi’. However, if you query ‘Japanese food’, the query vector will be much closer to the ‘sushi’ vector than to the ‘pasta’ vector.

What are the benefits of vector databases?

Vector databases such as ChromaDB offer a number of ad­vant­ages over tra­di­tion­al re­la­tion­al databases that are par­tic­u­larly valuable for AI ap­plic­a­tions. We’ll discuss some of these in more detail below.

Efficient sim­il­ar­ity search

By rep­res­ent­ing a data object as a point in a high-di­men­sion­al space, al­gorithms that spe­cial­ise in vector pro­cessing can be applied. This allows for the rapid and efficient iden­ti­fic­a­tion of nearby vectors (or them­at­ic­ally relevant content). This cap­ab­il­ity is crucial for ap­plic­a­tions like image re­cog­ni­tion, where identi­fy­ing similar images is necessary, and for re­com­mend­a­tion systems that suggest similar products or content.

Per­form­ance and scalab­il­ity

Vector database systems often use a whole range of tech­niques to ef­fi­ciently ac­cel­er­ate query speed and data pro­cessing. In addition to the efficient pro­cessing of high-di­men­sion­al vector data, vector databases are often designed in such a way that many op­er­a­tions can be executed in parallel. The rep­res­ent­a­tion of complex data as vectors also allows the efficient handling of very complex data struc­tures. Overall, these tech­niques con­trib­ute to the fact that vector databases can contain and process large amounts of data without a sig­ni­fic­ant loss of per­form­ance.

In­teg­ra­tion of machine learning models

Since neural networks often use vectors as input and output, many AI models can be seam­lessly in­teg­rated into vector databases. This enables direct storage, man­age­ment and querying of model output and input, which sim­pli­fies and ac­cel­er­ates the de­vel­op­ment and de­ploy­ment process of AI ap­plic­a­tions.

Where are vector databases used?

One use case of vector databases that is very relevant today is machine learning and gen­er­at­ive AI. In machine learning, vector databases are used to perform sim­il­ar­ity searches, which is required for tasks such as clas­si­fic­a­tion, clus­ter­ing and re­com­mend­a­tion systems. Models can be trained to quickly identify similar data points and make pre­dic­tions or decisions based on them. For example, a re­com­mend­a­tion algorithm can be based on a vector database to suggest products or content to users that are similar to their previous pref­er­ences.

In addition, vector databases can be used to ac­cel­er­ate the training of new neural networks. Vector databases make it possible to ef­fi­ciently manage and search very large training datasets, which sig­ni­fic­antly improves both the accuracy and the training time of the model.

One specific ap­plic­a­tion that benefits from this op­tim­isa­tion is gen­er­at­ive AI models such as OpenAI’s GPT. These use vector databases to recognise complex patterns in data and create new content. The ef­fi­ciency gain from vector databases is crucial to the per­form­ance of these systems.