pgEdge Announces Support for pgvector Extension to Unleash the Power of AI in Distributed Applications
Powerful combination puts data closer to users for faster vector similarity search results
pgvector is an increasingly popular vector extension for PostgreSQL to store vector embeddings from AI models and to provide similarity search capabilities. This extension enhances PostgreSQL by introducing a new vector data type named "vector," along with three query operators designed for similarity searching - Euclidean, negative inner product, and cosine distance. It also incorporates the "ivfflat" (inverted file with stored vectors) indexing mechanism, which accelerates approximate distance searches for vectors, leading to improved performance.
The pgvector extension is particularly useful with applications involving natural language processing, including those built on OpenAI's GPT models. However, the rise of large language AI models (LLMs) has created a tremendous need to manage and search large-scale, high-dimensional data.
"The solution to this challenge lies in vector databases – a powerful and increasingly popular embedding technology that enables faster and more accurate searches," said
"pgEdge combined with the pgvector extension is a powerful combination that puts inference and similarity search requests closer to the users giving them faster search results regardless of where they are located," said
The pgvector extension is now available for both the pgEdge Cloud managed service offering, and the self-hosted and self-managed pgEdge Platform product.
pgEdge's mission is to make it easy to build and deploy highly distributed database applications across the global network. Founded by industry veterans who have championed enterprise usage of the PostgreSQL database for several decades and helped run the world's largest managed database cloud services, pgEdge is headquartered in
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