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What is a Vector Database? | A Deep Dive Into Vector Databases

A vector database is a specialized storage system designed to efficiently handle and query high-dimensional vector data, commonly used in AI and machine learning applications for fast and accurate data retrieval. With the rapid adoption of AI and the innovation that is happening around Large Language Models we need, at the center of all of it all, the ability to take large amounts of data, contextualize it, process it, and enable it to be searched with meaning. Generative AI processes and applications that are being built to natively incorporate Generative AI functionality all rely on the ability to access Vector Embeddings, a data type that provides the semantics necessary for AI to have a similar long-term memory processing to what we have, allowing it to draw on and recall information for complex task execution. Vector embeddings are the data representation that AI models (such as Large Language Models) use and generate to make complex decisions. Like memories in the human brain there is complexity, dimension, patterns, and relationships that all need to be stored and represented as part of the underlying structures which makes all of this difficult to manage. That is why, for AI workloads, we need a purpose-built database (or brain), designed for highly scalable access and specifically built for storing and accessing these vector embeddings. Vector databases like Datastax Astra DB (built on Apache Cassandra) are designed to provide optimized storage and data access capabilities specifically for embeddings. A vector database is a type of database that is specifically designed to store and query high-dimensional vectors. Vectors are mathematical representations of objects or data points in a multi-dimensional space, where each dimension corresponds to a specific feature or attribute.