<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Vector on Vonng</title><link>https://vonng.com/en/tags/vector/</link><description>Recent content in Vector on Vonng</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>rh@vonng.com (Ruohang Feng)</managingEditor><webMaster>rh@vonng.com (Ruohang Feng)</webMaster><copyright>© 2025 Ruohang Feng</copyright><lastBuildDate>Wed, 10 May 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://vonng.com/en/tags/vector/index.xml" rel="self" type="application/rss+xml"/><item><title>AI Large Models and Vector Database PGVector</title><link>https://vonng.com/en/pg/llm-and-pgvector/</link><pubDate>Wed, 10 May 2023 00:00:00 +0000</pubDate><author>rh@vonng.com (Ruohang Feng)</author><guid>https://vonng.com/en/pg/llm-and-pgvector/</guid><description>This article focuses on vector databases hyped by AI, introduces the basic principles of AI embeddings and vector storage/retrieval, and demonstrates the functionality, performance, acquisition, and application of the vector database extension PGVECTOR through a concrete knowledge base retrieval case study.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://vonng.com/pg/llm-and-pgvector/featured.jpg"/></item></channel></rss>