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本项目是一个面向大模型应用开发者的RAG（检索增强生成）技术全栈教程，旨在通过体系化的学习路径和动手实践项目，帮助开发者掌握基于大语言模型的RAG应用开发技能，构建生产级的智能问答和知识检索系统。 RAG（检索增强生成）是一种结合信息检索和生成模型的自然语言处理框架，通过引入外部知识库（如文档库、数据库等），增强生成模型的回答准确性与相关性。 RAG技术解析：知识库与检索增强生成的应用与局限。探讨RAG如何通过向量检索和提示词优化解决大模型实时信息不足问题，分析文档切分、语义检索等关键技术实现，对比传统RAG与GraphRAG的优缺点，帮助开发者合理选择知识库解决方案。
全面回顾了RAG的发展历程，从最初的 Naïve RAG 到 Advanced RAG，再到 Modular RAG 和 Graph RAG，每种范式都有优劣。 Agentic RAG 作为最新范式，通过引入自主Agent实现了动态决策和工作流程优化。 该框架采用统一的多模态知识图谱架构，能够处理并关联文档中的文字、图表、表格、数学公式等多种异构内容，实现“万物皆可RAG”的目标，极大拓展了RAG技术的应用场景与能力。 RAG-Anything的核心技术创新在于构建了统一的多模态知识图谱架构，能够同时处理并关联文档中的文字内容、图表信息、表格数据、数学公式等多种.
RAGFlow 可以为各种规模的企业及个人提供一套精简的 RAG 工作流程，结合大语言模型（LLM）针对用户各类不同的复杂格式数据提供可靠的问答以及有理有据的引用。
RAG（检索增强生成）技术通过结合信息检索和生成式 AI，有效解决了传统大语言模型的知识截止、幻觉等问题。 本节将深入解析 RAG 的核心概念和工作原理，详细介绍其两阶段处理流程：离线的知识库构建和实时的查询响应。 RAG（Retrieval-Augmented Generation，检索增强生成），RAG 是一种 AI 框架，它将传统信息检索系统（例如数据库）的优势与生成式大语言模型 (LLM) 的功能结合在一起。 RAG-Anything 是一个综合性多模态文档处理RAG系统。 该系统能够无缝处理和查询包含文本、图像、表格、公式等多模态内容的复杂文档，提供完整的检索增强 (RAG)生成解决方案。
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