Vector databases are specialized database systems designed to store and retrieve high-dimensional vector embeddings. Unlike traditional relational databases (RDBMS), which organize data into tables with rows and columns, vector databases function using mathematical distances in a multi-dimensional vector space.
How Vector Databases Differ:
Optimized for High-Dimensional Spaces: Designed to efficiently search for similar embeddings in large AI-driven applications (e.g., recommendation systems, image search).
Similarity-Based Retrieval: Uses distance metrics such as cosine similarity, Euclidean distance, or Manhattan distance to find the closest vectors.
Indexing Techniques: Implements approximate nearest neighbor (ANN) algorithms to speed up searches.
Why Other Options Are Incorrect:
(A) is incorrect because vector databases are optimized for high-dimensional spaces.
(C) & (D) are incorrect because vector databases do not use row-based or tabular storage.
🔹 Oracle Generative AI Reference:
Oracle integrates vector databases into its AI and ML solutions, enabling efficient similarity searches and AI-driven applications.