Building a RAG System from Scratch with pgvector and Gemini — Introduction

<!-- SC_OFF --><div class="md"><p>Hi Reddit, we are a team of database researchers (including a PhD from MIT DB Group) and we just open-sourced an embedded vector database for

<blockquote> <p><strong>🚀 Technical Briefing:</strong> This tutorial is part of our deep-dive series on Agentic Workflows at <a href="https://gateofai.com" rel="noopener

<h1> RAG Systems in Production: Building Enterprise Knowledge Search </h1> <h2> Introduction </h2> <p>Retrieval-Augmented Generation (RAG) has revolutionized how enterprises build

<h2> From Overwhelm to Insight: Navigating Codebases with AI </h2> <p>We've all been there. You join a new project, inherit a legacy system, or simply need to understand a complex

<p><strong>What I Learned</strong></p> <p>Today’s session introduced me to <strong>Retrieval-Augmented Generation (RAG)</strong> and why it’s becoming essential in AI. The focus

<p>In the <a href="https://dev.to/hiroki-kameyama/building-a-rag-system-from-scratch-with-pgvector-and-gemini-introduction-c8i">previous article</a>, we covered the three core

<p>Vertex AI has been Google Cloud's AI development platform since 2021. On April 22, 2026, at Google Cloud Next in Las Vegas, Google retired it — not with a deprecation notice,

<h1> Beyond Vector Search: Why GraphRAG is the Next Frontier for LLMs </h1> <p>For the past year, the industry standard for augmenting LLMs has been <strong>Retrieval-Augmented