Vector Databases: What are they, and why have they gotten so much interest lately?

If you’re like me, you’ve started to see the term “Vector Database” start popping up all over your technology news feeds. However, is this actually something that is useful to understand, or is this just another hyped up technology trying to overthrow good ole relational databases like the NoSQL database hype that was all the rave back in the early 2010’s?

Well the first thing that may surprise you is that Vector databases, are not really new. They’ve been around for a little while now, but recently, there has been an explosion in storing and retrieving vector datatypes. So let’s first talk about what vectors really are, and why they are being stored and used as a datatype in more and more applications.

When we talk about vectors and storing vectors in vector database, we are typically referring to vectors in the context of machine learning and data analysis. Here what we mean by a vector is “a mathematical representation of data”. In simple terms, a vector is a list of numbers that can represent a piece of data (a typical javascript array could be another name for a vector… it is a very general term). For instance, an image, voice snippet, sentence, or genome sequence can all be represented as vectors. A simple vector might just be [255, 100, 0] to represent an RGB value of a pixel in an image.

Why is this important? Well let’s go over some types of applications that need to convert certain forms of data to their mathematical representation in order to better analyze the details of their data.

1. Image Recognition Systems:

In image recognition systems, each image is converted into a numeric matrix, which can also be considered as a multi-dimensional vector. Each pixel in an image can be represented by an integer, indicating the grayscale level. In a colored image, typically an RGB image, each pixel is a three-dimensional vector (like mentioned above), with each dimension representing the red, green, and blue channels, respectively.

2. Voice Recognition Systems:

In voice recognition systems, a sound wave is typically converted into a spectrogram that shows how the frequencies of the sound wave change over time. This spectrogram can then be digitized and converted into a matrix, where each cell corresponds to the intensity of a particular frequency at a particular time. Like with images, this matrix can also be considered as a multi-dimensional vector.

3. Natural Language Processing Applications:

For natural language processing (NLP), each word, sentence, or document is converted into a vector representation using techniques like TF-IDF, word2vec, or BERT embeddings. For instance, word2vec, a popular method, looks at the context in which words appear and assigns similar vector representations to words that appear in similar contexts. The resulting vectors can capture complex semantic relationships between words. For example, the vector arithmetic “king – man + woman” results in a vector that’s close to “queen.”

4. Genome Sequence Analysis:

In genome sequence analysis, DNA sequences are usually transformed into numeric representations before analysis. One common approach is one-hot encoding, where each nucleotide is represented by a distinct vector: for example, adenine (A) might be [1, 0, 0, 0], cytosine (C) could be [0, 1, 0, 0], guanine (G) might be [0, 0, 1, 0], and thymine (T) could be [0, 0, 0, 1].

Machine Learning

In today’s world machine learning algorithms have shown to be an effective way to classify and perform analysis on the historically difficult types of data described above. However, in order to use machine learning algorithms, you typically need to get the data into a representation that the machine learning algorithms can work with and that data representation is vectors. In all these cases, the vectorized data can be fed into machine learning algorithms for further analysis. For example, in a vector database, these vectors can be used to perform efficient similarity searches. Given a query vector, the database can quickly return the most similar vectors, which can then be mapped back to the original data.

The conversion of data into vectors is a crucial step in many modern data analysis pipelines, as it allows for mathematical manipulation of the data and makes it compatible with a wide variety of machine learning algorithms.

Before we dive into the nitty-gritty of vector databases, let’s quickly remind ourselves what we’re comparing them to – our well-established friend, the relational database.

Relational Databases – A Quick Recap

In a relational database, we store data in a tabular format with rows and columns. Each row represents a unique record, and each column represents a distinct attribute or field. It’s like an Excel sheet on steroids. These databases are great for structured data and provide extensive support for SQL, the lingua franca of database interaction. But when it comes to dealing with complex data types, like images or natural language, and performing similarity searches, traditional relational databases can fall short.

Enter Vector Databases

Unlike relational databases, vector databases store data in the form of vectors – numeric representations of objects. These objects can be a document, image, sound, or any other data type. This unique data structure allows vector databases to perform similarity searches, returning vectors closest to a given query vector.

Similarity Search

In the realm of data management and machine learning, similarity search, also known as nearest neighbor search, is a process that finds the most similar items to a given query item in a dataset.

To illustrate, let’s imagine we have a collection of images, and we want to find images that are similar to a specific input image. The first step would be to convert all the images, including the input image, into numeric vector representations using some kind of feature extraction or embedding technique.

Once this is done, the similarity search comes into play. It calculates the ‘distance’ between the input vector and each vector in the dataset. This distance is a measure of how different or similar two vectors are. There are several ways to calculate this distance, such as the Euclidean distance (geometric distance in multi-dimensional space), cosine similarity (angle between two vectors), or Manhattan distance (sum of the absolute differences), among others.

The search process ranks each item in the database based on this distance, and the items with the smallest distances (i.e., the most similar ones) are returned as the search results.

In essence, similarity search is about finding the ‘closest’ items to a particular item within a large dataset. It is a crucial operation in many applications, including recommendation systems, image recognition, voice recognition, natural language processing, and bioinformatics.

The Advantages of Vector Databases

So, what makes vector databases attractive?

  1. Superior Similarity Searches: Like we covered above, this is by far the number one reason you would want to consider a vector database. Vector databases have an edge over traditional databases when it comes to similarity searches, courtesy of their specialized index structures like KD-trees and Vantage-point trees.
  2. Handling Complex Data Types: They can effortlessly handle complex data types like images, text, and audio, which can be transformed into vector representation using machine learning models.
  3. Scalable: Handling large volumes of high-dimensional data? Vector databases have you covered.

The Drawbacks

But as with all technologies, vector databases aren’t without their drawbacks.

  1. Complexity: Compared to relational databases, vector databases can be complex to set up and manage. Optimizing a vector database often requires tweaking parameters related to the index structure.
  2. Limited SQL Support: Most vector databases offer limited, if any, SQL support. If your existing systems heavily rely on SQL, integration might be a bit tricky.
  3. Limited Transactional Workloads Support: Vector databases shine in analytics and search but aren’t built to handle transactional workloads, a common requirement in many business applications.

When Should You Consider Vector Databases?

Given their strengths and weaknesses, vector databases are a perfect fit when you need to perform speedy similarity searches on large volumes of complex data. They are particularly useful in fields where pattern recognition and similarity-based search is crucial, such as machine learning, data analysis, recommendation systems, and bioinformatics.

From image or voice recognition systems to natural language processing applications and genome sequence analysis, vector databases bring a new level of efficiency and performance. They may not replace relational databases, but they certainly complement them, filling in gaps where traditional databases might not excel.

A main take-a-way is that these databases are not meant to replace other traditional types of databases, but are built specifically for the types of tasks that working with vectors will entail and are optimized for that type of work. You can still store vectors in a relational database and you wont typically have any issues other than lower performance. It’s all about using the right tool for the right job. And with the rise of machine learning algorithms, these databases have gotten more popular for that particular use case.

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