In today’s Generative AI world, Vector database has become one of the integral parts while designing LLM-based applications. Whether you are planning to build an application using OpenAI or Google’s Generative AI or you are thinking to solve use cases like designing a recommendation engine or building a computer vision (CV) or Vector database, would be an important component to consider.
What Is Vector Database and Why Are They Different Than the Traditional Database?
In the machine learning world, Vector or Embeddings represent the numerical or mathematical representation of data, which can be text, images, or media contents (Audio or Video). LLM from OpenAI or others can transform the regular data into Vector Embeddings with high-level multi-dimensions and store them in the vector space. These numerical forms help determine the semantic meaning among data or identify patterns or clustering, or draw relationships. Regular columnar-based RDBMS or NoSQL databases are not equipped to store Vector Embeddings data with multi-dimensions and efficiently scaling if needed. This is where we need a Vector database, which is a special kind of database that is designed to handle and store this kind of Embeddings data and, at the same time, offers high performance and scalability.