English(EN)Building Production-Grade RAG Systems: From Prototype to Bank-Scale Reliability
构建生产级RAG系统:从零开始到云部署
作者PulseAugur 编辑部·[10 个来源]·
一系列文章详细介绍了检索增强生成(RAG)系统的开发,重点关注实际实现和设计选择。项目从基础RAG进展到整合工具使用、AI代理和用于将工具公开为服务器的模型上下文协议(MCP)。关键决策包括使用pgvector而非专用向量数据库、优化嵌入维度以及使用Gemini 2.5 Flash进行生成。该系列还涉及生产挑战,如数据过时、检索失败以及评估和可观察性的重要性。
AI
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*Ld1-bXZHwkxKuEgkpjD8qw.jpeg" /></figure><p>Building a RAG demo takes an afternoon. Building a RAG system that works reliably in production — handling thousands of real business queries daily, in multiple language…
dev.to — MCP tag
TIER_1English(EN)·Hiroki Kameyama·
<p>In the <a href="https://dev.to/hiroki-kameyama/building-a-rag-system-from-scratch-ai-agents-memory-planning-and-multi-step-reasoning-1kp9">previous article</a>, we built AI Agents that autonomously search our pgvector database. One limitation remained: the tools were hardcoded…
Medium — MLOps tag
TIER_1English(EN)·Banasree Ghosh·
<p>In this final article, we'll recap what we built across the series, consolidate the design decisions, and point to where to go next.</p> <h2> What We Built </h2> <p>Starting from a blank Python project, we built a complete AI system step by step:<br /> </p> <div class="highlig…
dev.to — LLM tag
TIER_1English(EN)·Hiroki Kameyama·
<p>In the <a href="https://dev.to/hiroki-kameyama/building-a-rag-system-from-scratch-design-decisions-explained-40hd">previous article</a>, we examined the design decisions behind our RAG pipeline. Now we'll give the LLM the ability to call our search functions autonomously — thi…
dev.to — LLM tag
TIER_1English(EN)·Hiroki Kameyama·
<p>In the <a href="https://dev.to/hiroki-kameyama/building-a-rag-system-from-scratch-with-pgvector-and-gemini-implementation-3n28">previous article</a>, we built a working RAG pipeline. Now let's step back and ask <em>why</em> we made each design decision — and what alternatives …
dev.to — LLM tag
TIER_1English(EN)·Hiroki Kameyama·
<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 concepts behind RAG. Now let's build it.</p> <p>By the end of this article you'll have a working RAG pip…
dev.to — LLM tag
TIER_1English(EN)·Hiroki Kameyama·
<h2> What This Guide Covers </h2> <p>When you start building LLM-powered applications, one pattern becomes unavoidable: <strong>RAG (Retrieval-Augmented Generation)</strong>.</p> <p>LLMs only know what they were trained on. Your company's internal documents, the latest spec sheet…
<p>Retrieval-augmented generation looks trivial in a tutorial: embed some documents, drop them in a vector database, stuff the top matches into a prompt, done. Then you point it at real company data and real users, and you discover that the demo was the easy 10%.</p> <p>We build …