What Is a Vector Database?

Most people think of vector data as driving the recommendation engines that power retail as well as music and video streaming sites. But generative AI (GenAI) goes well beyond these use cases, putting vector data and vector databases center stage in a raft of AI-driven capabilities, such as semantic search, content summarizations, and natural language processing.

A vector is a set of numbers representing the features of an object, whether that object is referenced in a word, a sentence, a document, an image, a video, or an audio file. A few examples: Is this document about a certain technology or policy? Does an image include grass, a white dog, or a wooden building? Does this song track contain female vocals or an electric guitar? Sophisticated AI models capture the features, objects, and meaning of  words and represent them in numbers as vector data. The collection of vector data that sum up the features of an object are its vector embedding. Vectors let AI become very good at comparing or searching content based on similarity of those concepts based on well understood math. 

Vectors are used in a database as a representation of the features of the data objects they describe. The database arranges the vectors so those that are mathematically close to one another tend to describe objects with similar features. For example, the vector that notes the orange of a carrot and the orange of a shirt will be located close together. Creating indexes on these vectors allows you to quickly compare or search a wide range of objects and find those that are alike.  

Vector indexes and vector databases work in tandem as parts of a system designed to efficiently store and retrieve vectors, that is, sets of numbers that represent the features of an object, such as the topics discussed in a document, the objects appearing in an image, or the speaker's tone in an audio recording.

Vector databases improve the performance of AI applications by efficiently storing, indexing, and searching vector-indexed data. For companies looking to bring more specificity and timeliness to the answers and other content generated by their AI models, vector databases can be part of a cost-effective solution. These systems, which combine vector indexes, semantic search, and retrieval-augmented generation, or RAG, curate the data that LLMs use to generate their outputs by guiding the model to use up-to-date enterprise information in its outputs. In such a system, a multimodal vector database, such as Oracle Database, which stores accumulated enterprise data alongside vectors, can result in less complexity and improved speed and performance. 

Unsurprisingly, the use of databases optimized for storing and analyzing vectors is rising. Once used primarily for mapping and data analysis, vector databases have become a critical technology for the recommendation engines commonly used by the most popular retailers and music and video streaming providers as well as for virtual assistants, biometric pattern recognition, anomaly detection, and more. And now, vector databases have found a new and spectacular use: storing large volumes of unstructured data that can be accessed to inform the outputs of GenAI models. 

A growing trend is for established databases, such as MySQL and Oracle Database, to incorporate vector data as a native data type alongside the rest of an organization’s data, such as JSON, graph, spatial, and relational. This convergence negates the need to move data to a separate database for generative AI operations, which both simplifies the process and leaves valuable data in trusted repositories. 

The growth of generative AI use cases means there are many new vector databases on the market, in addition to the established  NoSQL and  relational databases that have added vector data type management. 

Vector databases are used by various applications and organizations that deal with large amounts of spatial and geometric data, such as in the retail and logistics industries and for systems that pilot autonomous vehicles. And now, companies exploring advanced AI and machine learning initiatives are adopting vector databases too. GenAI models, for example, depend on vector databases to expose context from organizations that the LLM has not been trained on. 

Other specific use cases include the following:

• Finance firms use vectors in several ways. For example, in portfolio analysis, vectors can represent aspects of a client’s portfolio. They can be used to track account performance over time and can also be instrumental to certain anomaly detection systems used to vet transactions.
• Healthcare researchers use vector databases to support their research and clinical trials. They store and analyze data related to patient demographics, locations, and treatment outcomes, allowing researchers to assess the impact of many different factors on treatment efficacy. The anomaly detection capabilities of vector search are also used in drug discovery use cases.
• Online retailers use vector databases to reference past purchases and browsing habits, then combine those insights with real-time product availability and customer location to recommend the most relevant products.
• Shipping logistics companies use vector databases to store information about logistics, manifests, and approvals that also include locations and distances, allowing them to accurately map and track objects in motion.
• Streaming services use vectors to run recommendation engines, allowing them to present real-time recommendations based on many factors, including genre, lead actors, release date, and reviews, as well as a customer’s past choices.

The use cases for vector databases are as varied as the organizations and applications that depend on them. In addition to real-time data analytics, financial systems, and recommendation engines, vector databases are optimized to handle the complex data structures commonly required for tasks such as image recognition and natural language processing.

By storing and processing data efficiently, vector databases enable companies to leverage complex data structures for a wide range of applications, including the following:

• Recommendation systems. Vector embeddings are used to quickly find similar product or entertainment options that are likely to interest a shopper or browser.
• Search engines. Search engines use vector databases to index queries and documents with their vector embeddings, allowing them to quickly locate similar search results or similar documents.
• Personalization. These systems use demographic information and past choices as guides for vector searches that pinpoint products or services that are likely matches for a particular user.
• Anomaly detection. Vector databases facilitate efficient search for anomalous vectors, even in very large data sets. This improved performance can help to, for example, spot attempted breaches and fraudulent transactions quickly enough to stop them before damage is done.
• Genomics and bioinformatics. Vectors can help researchers match genetic sequences for comparison of large volumes of genetic data. This can aid in areas such as disease prediction and drug discovery.
• Healthcare and medical research. Healthcare providers are using vector databases to store and manage information relevant to patient care, such as medical records, demographic data, lab results, and even the genomes of bacteria. In clinical trials, geospatial data related to trial sites, patient demographics, treatment outcomes, and adverse events can be accurately and quickly analyzed to determine the efficacy of a treatment.
• Image and video retrieval. Image and video retrieval operations employ vector databases for similarity and semantic searches that quickly pinpoint images or videos amid deep catalogs of options.

Vector databases offer many advantages, including fast similarity search. They are optimized for efficient nearest-neighbor searches, allowing quick retrieval of similar items in large data sets. This makes them ideal for applications and industries that require real-time processing and analysis of unstructured data and for emerging generative AI use cases.

Other advantages include the following:

• Cost-effectiveness. Vector databases, particularly open source options, such as MySQL, Postgres with pgvector vector extensions, or multimodal databases with native vector stores, offer cost-effective solutions for many demanding applications, including training or augmenting AI models. In GenAI applications designed to use local or specialized data, a vector-database-backed RAG system can offer a cost-effective approach for incorporating private enterprise data for more accurate answers.
• Fast retrieval. Vector databases are indexed for fast retrieval of data based on an object’s many attributes. They do this by noting relationships and proximity and using those to quickly execute searches.
• Integration with machine learning. Vector databases are designed to integrate with machine learning frameworks and algorithms, which drives the development of predictive models, anomaly detection, clustering, and other machine learning–based analyses.
• Personalization. Vector databases allow retailers, music streaming services, and even healthcare businesses to tailor their services to quickly find matches for an individual’s preferences and needs.
• Real-time analysis. Vector databases can support in-memory operations for fast query response times and efficient data processing. This enables them to perform real-time analysis for day-to-day decision-making.
• Reduced development complexity. Vector databases can provide APIs, libraries, and query languages that abstract away the complexities of data management and application development. This can vastly reduce the time involved in the application development process and, thus, the cost. 
• Scalability. Vector databases can efficiently manage and process millions or even billions of vector objects and, with the right infrastructure, quickly grow to keep up with demand.
• Versatility. Vector databases support a wide range of unstructured data, such as audio recordings, text documents, and images. This versatility allows them to accommodate many use cases and applications.

Whether you’re using GenAI or nearly any other operation using vectors, Oracle can help. 

Oracle AI Database, the world’s most popular enterprise database, provides a single data platform for vectors and all your accumulated business data. Now you can effortlessly harness the capability of similarity search for your company's data without the need to oversee and synchronize various databases. An easy-to-use feature called AI Vector Search allows you to conduct semantic searches on both structured and unstructured data in your Oracle Database. 

Combining relational data, JSON documents, graphs, geospatial data, text, and vectors in a single database enables you to rapidly build new features in your applications. AI Vector Search in Oracle Database can also be used in a RAG pipeline together with any GenAI service. In addition, Oracle MySQL HeatWave database handles vectors natively to support vector search and other use cases. For example, you can use it together with the RAG service in Oracle Cloud Infrastructure (OCI) to bring a generative AI interface to your proprietary documents, giving you an AI that’s an expert in your organization’s operational data. 

Whether you’re using vectors for data analysis, geospatial applications, product recommendations, or as an enabling technology for generative AI, Oracle can help. Both Oracle’s flagship database and Oracle MySQL HeatWave manage vectors as a native data type alongside many other data types for a simpler development experience. Both databases run on Oracle Cloud Infrastructure. OCI is designed with the latest processors and supercluster architecture to efficiently handle the most demanding AI workloads, including generative AI, computer vision, and predictive analytics. Whether you build with Oracle AI Database or open source MySQL database, you can start taking advantage of vector search today. 

In the age of generative AI, vector databases have become more important to businesses than ever before. As more development teams look to store and manage the vector data type, they’ll have a decision to make: Bring in a specialized, purpose-built vector database or use multimodal databases, such as Oracle Database, that support not only vectors but many other data types as well.