As society has evolved into one that increasingly uses devices and the internet, the collection, storage, and ability to use data have become increasingly important.
Data management systems have had to rapidly adapt to the vast amount of data that is continuously being made available. One data management system that more and more companies and users are turning to in order to meet this demand is the vector database. These databases store data uniquely, allowing them to hold massive amounts of different data types and use them in many ways.
Due to this demand, the global vector database market is expected to grow from being worth $1.5 billion in 2023 to $4.3 billion by 2028, with a CAGR of 23.3%. Yet, despite their increasing popularity, vector databases are still an unknown quantity to many. Here are 7 things you need to know about vector databases.
Stores Data on Vectors in a Multidimensional Space
What sets vector databases apart from relational and NoSQL databases is how they store data. MongoDB’s guide to vector databases outlines how data is stored on a vector, which is represented as an ordered list or sequence of numbers in the database.
Data is converted into a vector through an embedding model, and different data points are used to represent one piece of information in the sequence of numbers. Instead of storing the data in a table or NoSQL format, vectors are stored in a multidimensional space that allows them to form clusters with similar vectors. The result is that it allows vector databases to provide semantic searches.
Semantic Searches
Most databases ensure that data is stored so that an exact match to the inquiry can be quickly found. While vector databases do have that ability, their main strength is the capability to perform semantic searches (also known as vector searches). A semantic search identifies vectors that reside in close proximity to the given query vector to produce a selection of results.
This means that they can be matched even if two pieces of data aren’t identical but are contextually or semantically similar. This allows the vector database to be used for recommendation systems, chatbots(see below), and extensive searches for images and documents, as well as other use cases.
Holds Unstructured Data
Vector databases are very flexible due to the fact that they can store unstructured data. Unstructured data is data without a fixed schema, including videos, books, social media posts, PDFs, and audio files. With 80% of all data collected being unstructured, vector databases are one of the most versatile data management systems, allowing businesses and users to tailor the databases to their needs.
Highly Scalable
Semantic searches within vector databases are most effective when used on large datasets. A key advantage of the vector database is that it is designed to not only hold vast amounts of data, but also to easily increase their volume load without performance degradation.
This is achieved through sharding, which is where data is divided across multiple nodes to distribute the workload. The ability to scale allows vector databases to be able to train AI models.
Trains AI Models
From AI-driven Chatbots and Customer Service in Fintech, which is transforming the financial industry, to AI Image generation in SEO, AI is becoming increasingly prominent in modern society. AI models are trained on massive datasets to be able to provide responses and generate new content accurately. However, when an AI model, like a large language model (LLM), is trained, it becomes stateless and can’t learn anything new.
A vector database attached to an LLM can act as external knowledge (giving it state) so that it can continuously update the LLM with the latest data. The semantic search feature allows the LLM to find the closest result to the user query. This is how chatbots are able to respond to customers with the latest up-to-date information.
Can Integrate with Existing Systems
Vector databases are flexible in the type of data they can store and how adaptable they are to existing systems. Most vector databases can seamlessly integrate into existing systems through application programming interfaces (APIs) or software development kits (SDKs). This allows the database to easily collect data from a company’s existing system without the need for a costly upgrade.
Used Across Multiple Industries
As demonstrated above, vector databases are very flexible, and this is why many different industries are adopting this type of database to their specific needs. Writing about vector databases on HIStalk, FaiyazShikari, CTO of HHS Tech Group, outlines how, in the health industry, a vector database can “analyze a patient’s unique medical profile and identify others with similar vector representations, potentially leading to faster diagnoses and treatment options.
This would allow doctors to move beyond the one treatment-fits-all approach. Another industry where vector databases are being widely integrated is the entertainment industry. Music and video streaming services can use vector databases to provide personalized recommendations using a semantic search based on previous user history.
Vector databases are fast becoming the future of data storage and sorting. As more industries adopt vector databases, they will continue to transform how we collect and use data. For more on the latest Tech news, do visit the rest of our site.
