What Are Vector Databases and How Do They Work?

A vector database is a type of database designed to store, manage, and query high-dimensional vectors that represent complex data like images, text, or other information.

Vector databases are the powerhouses designed to store, manage, and query complex data like images, text, and even abstract concepts. But their true superpower lies in their ability to perform lightning-fast similarity searches, finding needles in digital haystacks in the blink of an eye.

In a world where understanding context and finding subtle connections can make or break a user experience, vector databases have become indispensable. They're the engines driving cutting-edge applications in image recognition, natural language processing (NLP), and recommendation systems.

This guide will dive into how vector databases work when it comes to storing and retrieving different types of data.

You'll discover how they store and retrieve different types of data, explore their practical applications, and learn how they can supercharge your business efforts – all while reducing your workload.

According to data sources from 2023, 42% of enterprise-scale companies surveyed report using AI in their business and an additional 40% of respondents say they are exploring AI. This technology is a game-changer for finding helpful patterns in data and saving businesses the time making profitable connections. However, it can’t do all the work on its own.

This is where vector databases shine. They're the powerhouse behind numerous machine learning algorithms, playing a crucial role in everything from traditional AI to cutting-edge generative models. By adding content to a vector database, you're not just storing data – you're fueling a system that learns and evolves with your business. The beauty of vector databases extends beyond machine learning, though. They unlock a world of possibilities, from supercharging search capabilities to enabling hyper-personalized customer experiences.

Simply put, a vector database is a collection of data that is converted and stored in sets of numbers (vectors). This makes it easy to manipulate this content mathematically. Since machines don’t interact with the world like we do, they perceive data as a set of numbers.

The algorithms enabled by vector databases give AI programs the ability to find patterns in content. These patterns are a foundation of the contextual learning you’ve experienced if you’ve interacted with an AI system. With more quality content over time, AI programs are able to find hidden correlations, make predictions, and generate or summarize content in remarkable ways.

In AI-based image recognition applications, images can be encoded as high-dimensional vectors. A vector database can help quickly find images that are visually similar to a query image by comparing the vectors that represent these images. This is much faster and more accurate than comparing images pixel by pixel.

In NLP, vectors can represent words, sentences, or even entire documents. A vector database can then be used to find text that is semantically similar, even if it doesn't contain the exact same words. This capability is foundational for applications like search engines, chatbots, and recommendation systems, where understanding the meaning behind the words is more important than just matching keywords. It is also important for effective use of generative AI in many systems.

Vector databases also enable advanced search techniques like hybrid search and support for Retrieval-Augmented Generation (RAG) patterns. Hybrid search combines traditional keyword-based search with vector similarity search, offering a powerful way to improve search relevance and accuracy.

RAG patterns, on the other hand, use vector databases to enhance the capabilities of large language models by providing them with relevant, up-to-date information from your own data sources.

Both vector databases and relational databases are similar in their structure. They’re both vital components of database management, but they have different end goals.

Relational databases focus on a consistent and structured approach to data. They function similar to a spreadsheet and help keep data organized with straightforward rows and columns. Every row acts as its own record, while each column will provide additional attributes. For example, a row in a relational database could be for "men’s clothes." A column will provide more specific information such as "jackets" or "socks."

A vector database is simple – it’s storing long strings of numbers – but the behavior in systems is more abstract. Since vector encodings can represent meaning, vectors provide emotional and contextual nuance.

If a relational database is more like a spreadsheet, it helps to think of a vector database like a web. Data management systems that seek to find compelling, subtle relationships between data benefit from encoding the data as vectors and using vector databases.

Consider the example above. A relational database can store information about men’s clothes and maintain relationships such as jackets and pants of the same color, but it won’t find these relationships unless they are explicitly in the data. A vector database allows for context, for example understanding that blouses and shirts are related, or that people who buy shoes might want to shop for socks too.

Moreover, a vector database can infer more nuanced relationships. For instance, it might recognize that a customer who frequently buys business attire could be interested in leather briefcases or high-end watches, even if these items aren't explicitly categorized as business wear.

It can also understand seasonal trends, suggesting light jackets in spring to someone who bought heavy coats in winter, without needing explicit rules for each season-item combination. This contextual understanding enables more sophisticated recommendation systems and search functionalities, allowing businesses to provide a more personalized and intuitive shopping experience.

Vector databases can fuel machine learning programs to provide better online experiences. They may recommend certain clothes or suggest timely discounts inspired by the customer’s activity.

Traditional databases aren’t just relational. For example, there is a class of “NoSQL” databases, which are inherently non-relational. Vector databases and NoSQL databases are very similar at a glance. They both use a non-relational foundation and provide unparalleled scalability for businesses.

However, NoSQL databases are tuned for working with documents and structures like JSON, and vector databases are tuned for working with long series of numbers in combinations. Manipulating and comparing vectors is mathematically intensive, and not something traditional NoSQL databases are good at.

Practically, there are important uses for relational, document, and vector storage and manipulation in databases, and systems tend to use more than one of these models, and use them together.

A vector database is a digital application that stores various forms of information in long sets of numbers known as "vectors" (just think back to your high school algebra class). Each data object takes on a numerical representation called a "vector embedding."

Below are common questions surrounding vector databases and vector search to give you a clearer picture.

A vector embedding is a numerical representation of data, such as words, images, or other entities, in the form of a high-dimensional vector. Most vectors used with embeddings are hundreds to thousands of dimensions, with each dimension having a number.

This embedding captures the semantic or structural information of the data, allowing for efficient comparison and similarity searches, and is commonly used in machine learning and artificial intelligence to convert complex data into a format that computers can easily process and analyze.

Imagine you have three words: "king," "queen," and "man." A vector embedding converts these words into lists of numbers (vectors) that capture their meanings in a way computers can understand. For example:

• "king" might be represented as [0.8, 0.6, 0.9, …..]
• "queen" might be represented as [0.8, 0.6, 0.8, …..]
• "husband" might be represented as [0.7, 0.4, 0.9, ……]

In these vectors, the way the numbers are calculated reflects similarities and differences in meaning. Here, "king" and "queen" have similar vectors because they are related words, while "husband" has a slightly different vector that is closer to “king” than “queen”.

This helps computers perform tasks like finding words and phrases with similar meanings or understanding the relationships between words. Vector embeddings are vital for poring through massive sets of unstructured and semi-structured data. The distance between each vector helps machines understand how similar – or dissimilar – data is from each other.

The more dimensions in the vector embedding, the more nuance a machine is able to glean from the data being encoded. Everything from single words to long web pages are transformed into vectors for storage in vector databases and use in vector-based algorithms. Many AI tools are based on vectors and vector embeddings, and cannot work without them. Organizing all these vector embeddings is done through the aid of "high-dimensional vectors."

Learn More About Vector Embeddings

High-dimensional vectors are lists of numbers that have many elements, each representing a different feature or characteristic of the data they describe. These vectors exist in a high-dimensional space, meaning they can have dozens, hundreds, or even thousands of dimensions, depending on the complexity of the data. For example, a high-dimensional vector could represent the various attributes of an image, such as color, texture, and shape, with each dimension capturing a different aspect of these attributes.

In machine learning and data analysis, high-dimensional vectors are used to encapsulate complex information in a structured way that allows for efficient computation and analysis. Minor attributes are also more easily captured with high dimensional vectors, adding complexity beyond simple facts.

While vector database and vector search are similar terms, their main difference lies in the function and the process of each. A vector database is an entire data management solution, while vector search is a type of semantic search tool.

When you conduct a vector search, your query vector will be compared to a large collection of vectors in an attempt to find similarities. This action is sometimes dubbed a similarity search. Unlike traditional databases, the goal here is to find similar matches in a short amount of time. Your database is where you conduct your vector search. Using indexing, you'll enjoy a lightning fast similarity search to take the legwork out of analyzing your data.

Indexing in a vector database is the process of organizing and structuring the stored vectors to enable efficient and fast retrieval of data during similarity searches.

Indexing basically provides a much faster way to retrieve and compare information than iterating through all of the different vectors. When you have billions of vectors, it’s essential.

Vector databases build in different types of indexes and often have a facility for developers to add new ones. Since a major use of vector databases is to start with a vector and find which other vectors are closest to it, the indexes and algorithms used are important to understand and to check when you are selecting a vector database.

The most common distance models you will hear about are Euclidean distance and Cosine similarity. You can think of Euclidean distance as the straight-line distance between any two points in multi-dimensional space. This is a simple and easy to understand metric. Cosine similarity is instead based on the angles between two vectors. Mathematically speaking, it compares the orientation of the vectors rather than the magnitude, and is really useful in text analysis where the number of dimensions is high. One of the downsides of these metrics is that they take a lot of computing and can be slow.

It turns out that finding approximately the nearest neighbors can be much faster and nearly as good. If your vector database includes Approximate Nearest Neighbor search (ANNS) that’s a sign that it will work well at a very large scale with almost no change in quality from Cosine similarity results.

Are you concerned about your brand's ability to keep pace with the rest of the world? According to a recent study, around 43% of IT decision-makers are worried their infrastructure won’t be able to handle future data demands.

Let’s dive further into the advantages your business will enjoy when switching to vector databases.

Since vector databases are inherently built to handle large sets of data, you can scale more easily. Businesses who use larger machine-learning applications will enjoy the ability to grow without switching over to a new database.

Businesses move at the speed of light (and so does your data). Vector databases provide you with real-time processing so you can enjoy faster query response times. This efficient data processing system makes it easier to make fast decisions on a day-to-day basis. This is particularly useful for agile industries who need faster response times such as healthcare or shipping.

When your data takes on several forms, you need a database that’s adaptable enough to sort them all. Vector databases are able to handle both multimedia and unstructured data for your business. To clarify: unstructured data is any amount of data that isn't organized and stored in a database format. In other words, this term refers to any collection of text, image, or video files that need to be turned into a useful dataset.

A traditional search engine will try to find an exact match to what you’re querying. A vector database offers a more subtle and nuanced approach to provide similar – but not exact – results. This approach offers a different level of accuracy. With the aid of a vector database, you’ll have query results based on similarity metrics. Similarity metrics offer you a deeper level of insight into connections and patterns within your datasets.

This level of sophistication is one of a few details that separate vector search from traditional relational databases.

A personalized approach is essential for your unique business operations. A vector database is one of the most practical tools for a personalized search experience thanks to its subtle approach. Not only can your business enjoy these tools, so can your customers. Personalization and recommendation systems are a popular addition to customers’ online buyer journeys. For example, e-commerce regularly uses vector databases to provide smart recommendations to site visitors.

These databases adapt to their behavior to tailor recommendations based on daily activity or previous purchases.

While vector databases are incredibly useful for businesses who need to scale, they’re still not for everyone. Before you make the switch, consider the following hurdles first.

Traditional databases may be more limited in their scope, but they’re also easier to implement. One of the biggest barriers of vector databases is the complexity with deploying and maintaining them in the long term.

Not only do you need to find optimal servers for your vector database, you have to go through the process of importing your data. You also need to figure out which vector database edition suits your business best.

Whether you decide to use a vector database service or an open-source model, you’ll have a higher learning curve. Vector databases are complex database management systems that are constantly growing in scope.

Some businesses may be concerned with their ability to keep up with the changes and additions to vector databases. Likewise, other businesses may not want to invest in the higher operating costs of stepping into the world of vector databases.

Now that you have a broader perspective on vector databases, let’s take a look at their most common use applications. You’re already familiar with their benefits for machine learning, but that’s just one of many end results.

Does your business regularly handle large volumes of images, .gifs, and videos? When you’re overwhelmed by the work that goes into organizing it all, vector databases can help. You can choose vector database models based on the types of data you handle most, or your preferred indexing techniques. For example, you can choose an image vector database if you usually handle static images. If you do a mixture of audio, images, and video, you can do a multimedia vector database instead.

The vector database is also able to recommend specific images or videos based on your query vector. This function can be incredibly helpful for tasks such as facial recognition or content marketing campaigns.

One of the most effective resources businesses need in their arsenal are natural language processing (NLP) tools. It’s not enough to simply match a query: you need to understand semantics and sentiment.

Vector databases are well-suited to NLP tasks for AI programs. They make it easy for both businesses and AI to find logical and subtle connections between data. With the aid of indexing and fast retrieval, these databases provide keen insights in a short amount of time. For example, if you've ever considered implementing a chatbot for your business, the vector database is a must-have addition. Chatbots perform best when aided by large language models.

Whenever you need to chart your business progress, vector databases will speed up your results. These database systems are useful for graph analytics with features such as community recognition and connection prediction.

One of the lesser-known benefits of a vector database involves reducing the risk of fraud. Since vector databases are so useful for finding connections in data, they can also spot contradictions.

This function is vital for quickly spotting unusual activity and taking immediate action. Your vector database can also help with details such as network traffic analysis and your current cybersecurity measures.

Hybrid search is an advanced technique that combines the strengths of traditional keyword-based search with the semantic understanding provided by vector search. This approach offers several benefits:

1. Improved relevance: By considering both exact keyword matches and semantic similarity, hybrid search can deliver more accurate and contextually relevant results.
2. Handling of edge cases: It can effectively deal with situations where either keyword search or vector search alone might fall short.
3. Flexibility: Hybrid search allows for fine-tuning the balance between keyword and semantic matching based on specific use cases.

Implementing hybrid search with a vector database typically involves:

• Performing both a keyword search and a vector similarity search
• Combining and ranking the results using a custom scoring algorithm
• Adjusting the weighting of keyword vs. vector search results based on the specific requirements of your application

RAG is an AI architecture that enhances large language models (LLMs) by providing them with relevant information retrieved from a knowledge base, often stored in a vector database.

This approach addresses several limitations of traditional LLMs:

1. Up-to-date information: RAG allows LLMs to access current information, overcoming the limitation of models trained on static datasets.
2. Domain-specific knowledge: By retrieving information from your own data sources, RAG enables LLMs to leverage domain-specific knowledge.
3. Reduced hallucinations: Providing relevant context helps minimize the likelihood of the model generating false or inconsistent information.

Implementing RAG with a vector database typically involves:

1. Indexing your knowledge base in a vector database
2. For each user query:
   
   • Generating a vector embedding of the query
   • Using the vector database to retrieve relevant information
   • Providing this information as context to the LLM along with the user's query
3. The LLM then generates a response based on both its pre-trained knowledge and the retrieved context

Vector databases play a crucial role in RAG patterns by enabling fast and accurate retrieval of relevant information, which is essential for maintaining the real-time performance expected in most applications.

By leveraging these advanced techniques, businesses can create more intelligent, accurate, and context-aware AI applications, further extending the capabilities of their vector database implementations.

When implementing a vector database, you have the option between open-source and commercial. Neither solution is superior to the other – simply different for your business needs.

Below are the basic steps to set up and configure your own vector database.

Before you choose a vector database model, you need to determine your end goal. Are you trying to train a machine learning model or do you need to organize your datasets more effectively?

Businesses that are well-suited to vector databases are ones handling complex data, using artificial intelligence, or operating with constantly changing data points.

Your first step is to figure out which model will suit your business needs. Some vector database models are developer-friendly with intuitive interfaces. Others provide more extensive support in filtering and indexing. Take your time reviewing the nuts and bolts of the database you plan on using – the smaller details make all the difference in your business operations.

Once you choose the vector database you want to use, do a little more research into each edition. Since these databases are fundamentally flexible, each version comes with various editions to better match your needs.

Once you choose your vector database and the edition you want, it’s time to configure it. Your database will come with a set of instructions for installation and transferring over your data. You’ll be able to modify your database according to your needs, such as choosing your preferred indexing solution.

After you’ve finished installing your database and transferring your data, you need to query it. This function allows you to find similar vectors in your database and start using your program more efficiently. Depending on the database you’ve chosen, this step can be automated or done manually.

Trial and error is key to getting the most out of your new database solution. Once you have everything up and running, monitor your vector database to see what’s working (and what’s not). You'll learn many interesting details during this process. You may find you need even more scaling to handle the volumes of data you’re receiving. You may realize certain methods of querying aren’t delivering the additional insights you’re looking for.

Your vector database capabilities are only as strong as your vision. As long as you keep an open mind and remain dedicated to growth, your business will benefit from the switch.

According to recent studies, the global vector database market is expected to surpass $4 billion by 2028. From healthcare to entertainment industries, the future is looking incredibly bright for advanced database technology.

Vector database technology has made it easier than ever to keep up with large quantities of data and glean useful insights. Businesses no longer have to manually sift through images, videos, and texts to understand what their target audience needs. The automated and flexible nature of these database systems frees up time to focus on the day-to-day demands of running a business.

At least 57% of businesses actively rely on machine learning to improve the customer experience. While machine learning is only one of the many case use applications, it’s a major player. Overall, the role of vector databases in future data management will only continue to grow. Businesses who want to scale and stay competitive need to take some of the work of data management off their plates. At InterSystems, we’re ready to help you implement vector database technology so you don’t fall behind.

Make a note about how the number of use cases is exploding and it’s important to choose something that’s flexible.

As we've seen, the applications for vector databases are expanding rapidly across industries. This explosion of use cases highlights a crucial point for businesses: when choosing a vector database solution, flexibility is paramount.

The fast-paced evolution of data management and AI demands solutions that can adapt to emerging needs and opportunities. Key aspects of flexibility to consider include:

1. Scalability to handle growing data volumes
2. Interoperability with existing and future systems
3. Customization capabilities for specific use cases
4. Support for various data types and query methods
5. Adaptability to new AI models and technologies

By prioritizing flexibility, you're not just solving today's challenges – you're future-proofing your data infrastructure. A flexible solution empowers your organization to pivot quickly, experiment with new applications, and stay competitive in an increasingly data-driven world.

At InterSystems, we understand the critical importance of flexibility in vector database solutions. Our technology is designed to embody these principles, offering a robust, scalable, and adaptable platform that can evolve with your needs.

From machine learning models to efficient similarity search, there's a database solution ready to grow your business. At InterSystems, we provide businesses across a variety of industries the means of harnessing these tools without barriers.

Vector databases provide more efficient methods of gathering, storing, and retrieving your data. Unlike traditional databases which focus more on accuracy, a vector search provides valuable insight. You'll gain new and compelling details around sentiment, similarities, and even safety issues.

We helped one of our past clients, Les Voies Navigables de France, create a more reliable data hub for its land-based information system. Another one of our past clients, Mondial Tissus, benefitted from our expertise in using data to improve the customer experience. No matter your end goal, we're prepared to match you with an effective database solution.

Our solutions span industries such as health and life sciences, digital manufacturing, mining operations, and supply chains. If you've been wondering how vector search can help your business scale, contact us today.

We're ready to help your business model enjoy the benefits that come with vector data, large language models, and more.