pgEdge Announces Support for pgvector Extension to Unleash the Power of AI in Distributed Applications
- The integration of pgvector with PostgreSQL enables faster similarity searches and inference using AI, which can positively impact the user experience and performance.
- The combination of pgEdge and pgvector brings search requests closer to users, resulting in faster search results regardless of their location.
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Powerful combination puts data closer to users for faster vector similarity search results
ALEXANDRIA, Va., Sept. 21, 2023 /PRNewswire/ -- pgEdge, the first company to offer a fully distributed database optimized for the network edge based on the standard and popular open source PostgreSQL database, today announced its support for the innovative pgvector extension that adds an open-source vector similarity search capability to PostgreSQL. This integration will allow PostgreSQL users to harness the power of AI to do similarity searches and inference closer to end users, for faster 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 Phillip Merrick, Co-founder and CEO of pgEdge. "Pairing pgvector with pgEdge's distributed Postgres database providing multi-region replication, users get results more quickly and a broader range of applications can take advantage of the AI capabilities it offers."
"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 Cemil Kor, Head of Product at Enquire AI. Enquire AI, a pgEdge customer and the company behind patented AI-powered knowledge discovery technology products Pulse Marketplace and Lumina, is deploying distributed pgvector via the pgEdge Distributed PostgreSQL database.
The pgvector extension is now available for both the pgEdge Cloud managed service offering, and the self-hosted and self-managed pgEdge Platform product.
For more information about pgEdge, read the blog or visit www.pgedge.com.
About pgEdge
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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SOURCE pgEdge
