Vector databases are designed to efficiently handle and retrieve vector embeddings of complex data types like images, videos, and audio, making them particularly suited for advanced search capabilities and AI-driven data analysis. Unlike traditional relational databases, vector databases store unstructured data like images and blog posts along with the vector embeddings of these items, capturing a huge amount of information about each piece of data. Vector databases are optimized to store these vectors and allow users to efficiently organize, search, and analyze complex information in ways that traditional databases can't. They use similarity measurements such as Euclidean distance and cosine similarity to find similar items between vectors. Query vectors are a fundamental concept in the functionality of vector databases, serving as the cornerstone for advanced search capabilities. Vector databases have gained prominence due to their pivotal role in supporting the development and deployment of AI applications, and are instrumental in powering various industries and use cases including recommendation systems, image and video retrieval, natural language processing, fraud detection, and biometric identification. The future of vector databases is closely intertwined with generative AI, promising transformative changes in how data is managed, searched, and utilized.