Ahsan Hadi: Embedding near the edge: pgEdge Distributed PostgreSQL with pgVector

Introduction We are excited to be announcing that we now support the increasingly popular pgVector Postgres extension for storing and searching vector embeddings in AI-powered applications. Bringing pgVector and pgEdge’s distributed capabilities together makes for a powerful combination that greatly improves performance for users regardless of their geographic location.In this blog we'll demonstrate how to configure pgVector with pgEdge to provide similarity search functionality across a pgEdge Distributed PostgreSQL cluster. I will start with brief summary of the products mentioned in the title of this blog:      pgEdge is fully-distributed PostgreSQL, optimized for the network edge and deployable across multiple cloud regions or data centers. pgEdge is available as pgEdge Platform, self-hosted software available for download from [download link]; or as pgEdge Cloud, a fully managed service.  This blog is applicable to both pgEdge Cloud and pdEdge Platform.pgvector is an open source extension for PostgreSQL that enables efficient similarity search and other vector-based operations. It's often used for applications like recommendation systems and image search. The pgvector extension provides an indexable vector data type that stores vectors in a PostgreSQL database. pgvector supports the  index, which implements the  method of indexing. Vector Database Vector data stores data as high-dimensional vectors, which are mathematical representations of features or attributes. The number of dimensions in a vector ranges from tens to thousands, depending on the complexity and granularity of the data. The main advantage of a vector database is that it allows for fast and accurate similarity search and retrieval of data based on their vector distance or similarity. So instead of using the conventional methods for searching data using predefined criteria or exact matches or wildcards, one can use the vector database to find similar or relevant data based on semantic or contextual meaning.Vector databases enable accurat[...]