Vector data is a numerical representation of various other forms of data, such as text, images, audio, video, and more. These vectors are not simply lists of numbers; they encapsulate rich, multidimensional information about the object they represent. For example, a vector could represent the semantic meaning of a sentence, the features of an image, or the characteristics of a user’s interaction pattern. This type of information needs to be stored in a specialized database known as a vector database. This type of database has been specifically designed for storing, managing, and retrieving this multidimensional vector data.
The differences between traditional structured databases and vector databases are that a traditional database is designed to store and manage structured data. Structured data is information that is organized into clearly defined tables with rows and columns and is suited for data that fits well into a tabular format, like customer information or inventory. Whereas a vector database Is designed for storing and retrieving information about complex multidimensional data types like images, text, and audio that doesn’t necessarily lend themselves to being easily organized into clearly defined tables with rows and columns.
Some typical uses for a vector database include video and image recognition, natural language processing and recommendation systems. While these only represent a few of the things that a vector database can be used for, they represent some of the most common things you might find a vector database being used for today.
Vector databases are an ideal solution for use with video and image recognition models. An example of how a vector database would be useful in the image recognition process would be by using similarity searches. When looking through a photo album a user’s selection of a photo may trigger the website or app to suggest similar images, showcasing the power of similarity search in a vector database.
Another example of where the use of a vector database is ideal is with Chatbots that use Natural Language Processing (NLP). By converting words and text into vector embeddings that encapsulate the semantic meaning of text, a chatbot can quickly respond to user queries with semantically similar responses. Not only that, but a wide range of NLP tasks, such as text summarization, semantic analysis, speech recognition, and question answering are made possible through the use of vector databases.
Another good use for a vector database is a recommendation system. Using a vector database for this type of application is ideal because they can efficiently process and recommend semantically similar products or content which can enhance the users experience with a website or app. A good example of a recommendation system is how Netflix can recommend movies or series based on genres, actors, and user reviews. By searching the various points of a vector for semantically similar results, services like Netflix can offer personalized suggestions to each user.
REFERENCES:
Advocate, L. M. (2023, August 1). A gentle introduction to vector databases: Weaviate – Vector Database. Weaviate Vector Database RSS. https://weaviate.io/blog/what-is-a-vector-database
Kirvan, P. (2022, December 7). What is a vector?. WhatIs. https://www.techtarget.com/whatis/definition/vector
Math insight. An introduction to vectors – Math Insight. (n.d.). https://mathinsight.org/vector_introduction
Nawaz, S. (2023, October 2). Top 8 vector database use cases in 2023. LinkedIn. https://www.linkedin.com/pulse/top-8-vector-database-use-cases-2023-sarfraz-nawaz
Schwaber-Cohen, R. (n.d.). What is a vector database & how does it work? use cases + examples. Pinecone. https://www.pinecone.io/learn/vector-database/