Vector Similarity Search with PostgreSQL's pgvector

🔗 Vector Similarity Search with PostgreSQL's pgvector
severalnines.com

As the AI revolution surges forward, promising significant innovations, it also introduced new types of vector databases. A vector database stores data as high-dimensional vectors called embeddings, which are mathematical representations of features or attributes of the data. These vectors are generated by applying an embedding function to the raw data, such as text, images, audio, video, etc. The embedding function can be machine learning models, feature extraction algorithms, etc.

We all know PostgreSQL as a relational database for transactional workloads. But, at the same time, with the pgvector extension, you can turn PostgreSQL into a complete vector database to power your AI applications.

This blog will provide a high-level overview of vector search and its application before going in-depth on pgvector, delving into its creation, features, use cases, and how to enable it into your PostgreSQL database manually and using ClusterControl (CC).

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