<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>GraphRAG on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/graphrag/</link><description>Recent content in GraphRAG on 硅基 AGI · 智能体学习与测评</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Sun, 12 Jul 2026 17:10:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/graphrag/index.xml" rel="self" type="application/rss+xml"/><item><title>从RAG到GraphRAG：知识检索的范式跃迁</title><link>https://guijiagi.com/posts/article-02/</link><pubDate>Sun, 12 Jul 2026 17:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-02/</guid><description>探讨GraphRAG如何通过知识图谱增强检索增强生成，解决传统向量检索的多跳推理瓶颈</description></item><item><title>从RAG到GraphRAG：知识检索的范式跃迁</title><link>https://guijiagi.com/posts/b2-7138b571/</link><pubDate>Sun, 12 Jul 2026 17:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-7138b571/</guid><description>探讨GraphRAG如何通过知识图谱增强检索增强生成，解决传统向量检索的多跳推理瓶颈</description></item><item><title>混合RAG：图+向量检索的协同威力</title><link>https://guijiagi.com/posts/hybrid-rag-graph-vector/</link><pubDate>Thu, 02 Jul 2026 10:36:00 +0800</pubDate><guid>https://guijiagi.com/posts/hybrid-rag-graph-vector/</guid><description>深入探讨图检索与向量检索的混合架构，发挥结构化与非结构化检索的协同优势</description></item><item><title>GraphRAG 2026：知识图谱增强检索的实践指南</title><link>https://guijiagi.com/posts/graphrag-2026-knowledge-graph-retrieval-guide/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/graphrag-2026-knowledge-graph-retrieval-guide/</guid><description>深入探讨GraphRAG在2026年的最新实践，从知识图谱构建到图谱增强检索的完整架构与代码实现</description></item><item><title>GraphRAG 2026：知识图谱增强检索的实践指南</title><link>https://guijiagi.com/posts/graphrag-2026-practice-guide/</link><pubDate>Tue, 30 Jun 2026 09:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/graphrag-2026-practice-guide/</guid><description>深入解析GraphRAG在2026年的最新实践，从知识图谱构建到社区检测检索，附完整代码示例与性能基准</description></item><item><title>RAG技术演进2026：从基础检索到智能知识库</title><link>https://guijiagi.com/posts/rag-evolution-2026/</link><pubDate>Tue, 30 Jun 2026 09:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-evolution-2026/</guid><description>2026年RAG技术全景：从Naive RAG到Agentic RAG，GraphRAG、Multi-modal RAG、Adaptive RAG等前沿方案的架构解析与实战对比</description></item><item><title>GraphRAG 生产部署指南：知识图谱增强的 RAG 系统</title><link>https://guijiagi.com/posts/graphrag-production-deployment-guide/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/graphrag-production-deployment-guide/</guid><description>从零到一搭建 GraphRAG 生产系统，涵盖知识图谱构建、社区检测、查询引擎和运维监控</description></item><item><title>RAG 架构 2026 最新实践：从 Naive RAG 到 GraphRAG+Agent</title><link>https://guijiagi.com/posts/rag-architecture-2026-naive-to-graphrag-agent/</link><pubDate>Sun, 28 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/rag-architecture-2026-naive-to-graphrag-agent/</guid><description>系统梳理 RAG 架构演进，从最基础的 Naive RAG 到 2026 年最前沿的 GraphRAG+Agent 范式，附完整架构对比与代码示例</description></item><item><title>GraphRAG图检索增强生成</title><link>https://guijiagi.com/posts/graphrag-graph-retrieval/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/graphrag-graph-retrieval/</guid><description>GraphRAG图检索增强生成</description></item><item><title>GraphRAG图检索增强生成</title><link>https://guijiagi.com/posts/graphrag-knowledge-graph-rag/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/graphrag-knowledge-graph-rag/</guid><description>GraphRAG图检索增强生成的原理、架构与实践，突破传统向量检索的局限性</description></item><item><title>GraphRAG 解析：知识图谱增强的检索增强生成</title><link>https://guijiagi.com/posts/graph-rag-explained/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/graph-rag-explained/</guid><description>深入解析 GraphRAG 的完整架构：实体抽取、关系构建、社区检测、层级摘要与多跳检索，对比传统 RAG 的优劣，附 Neo4j + LLM 的完整代码实现。</description></item><item><title>GraphRAG 解析：微软的知识图谱增强 RAG</title><link>https://guijiagi.com/posts/graphrag-explained/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/graphrag-explained/</guid><description>深入解析微软 GraphRAG 的原理、架构和实现，对比 Vector RAG 的差异和适用场景</description></item></channel></rss>