Getting Started with Vector Databases Using Amazon Aurora PostgreSQL + pgvector
This article introduces vector databases, particularly focusing on Amazon Aurora PostgreSQL with pgvector. It explains the basic concepts, use cases, and the Retrieval-Augmented Generation (RAG) technique that enhances the accuracy of language model responses. The target audience is beginners who are looking to understand the relationship between vector databases and large language models (LLMs).
- ▪Vector databases store data as vectors and search using similarity rather than exact matches.
- ▪Common use cases include semantic search, recommendation systems, and anomaly detection.
- ▪RAG improves LLM responses by incorporating relevant external information from vector databases.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3965426) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Satoshi Kaneyasu for AWS Community Builders Posted on Jun 3 Getting Started with Vector Databases Using Amazon Aurora PostgreSQL + pgvector #aws #ai #database #vectordatabase Hello! I'm Satoshi Kaneyasu, DevOps engineer at Serverworks. In this article, I'll introduce the basic concepts and terminology of vector databases for those who are just starting to learn about them. Target Audience This article is aimed at beginners to vector databases.
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