本项目设计了一个基于 RAG 与大模型技术的医疗问答系统，利用 DiseaseKG 数据集与 Neo4j 构 建知识图谱，结合 BERT 的命名实体识别和 34b 大模型的意图识别，通过精确的知识检索和问答生成， 提 …

它从最基本原则和 RAG 范式的演变开始，介绍 Agentic RAG 的 7 种架构。 还重点介绍在 5 种应用场景的效果，如医疗保健、金融和教育等行业中的关键应用，且非常详细。

Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an …

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and …

Learn about retrieval-augmented generation (RAG), what it is and its uses. Examine its benefits, drawbacks and how RAG is used with large language models.

Discover Retrieval-augmented generation (RAG) inside out with this comprehensive guide on what it is, how it works, its benefits & top tools.

Here’s how retrieval-augmented generation, or RAG, uses a variety of data sources to keep AI models fresh with up-to-date information and organizational knowledge.

Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of generated texts cannot be secured solely by the parametric knowledge they encapsulate. Although retrieval …

An LLM-powered advanced RAG pipeline built from scratch - pchunduri6/rag-demystified

Large language models (LLMs) have achieved remarkable success, though they still face significant limitations, especially in domain-specific or knowledge-intensive tasks [1], notably producing …

文章浏览阅读675次，点赞7次，收藏15次。RAG（检索增强生成）技术已从简单的知识检索演变为复杂的动态知识合成架构。本文深入剖析RAG的核心价值与标准化流程，包括知识库构建（加载、切分 …

The document presents an introduction to Retrieval-Augmented Generation (RAG) and its applications, highlighting the importance of LLMs and the limitations they face. It discusses RAG's architecture, …

深入解析轻量级RAG系统LightRAG的完整教程,从基础概念、架构设计到实战部署。包含详细的代码示例、性能优化技巧、最佳实践案例,以及与主流LLM的集成方案。无论你是AI开发者还是技术爱好者,都 …