Higress-RAG：一种通过双混合检索、自适应路由和CRAG实现企业检索增强生成的综合优化框架
Higress-RAG: A Holistic Optimization Framework for Enterprise Retrieval-Augmented Generation via Dual Hybrid Retrieval, Adaptive Routing, and CRAG
AI总结：
Higress-RAG通过双混合检索、自适应路由和CRAG实现企业检索增强生成的综合优化，提升检索精度与生成可靠性。
摘要：
大型语言模型（LLMs）融入企业知识管理系统的过程受到检索增强生成（RAG）范式的推动，该范式通过非参数外部数据增强参数化记忆。然而，从概念验证到生产级RAG系统的过渡受到三个持续挑战的阻碍：复杂查询的检索精度低、生成阶段的幻觉率高以及实时应用的不可接受延迟。本文对Higress RAG MCP服务器进行了全面分析，这是一种新型的企业导向架构，通过
英文摘要：
The integration of Large Language Models (LLMs) into enterprise knowledge management systems has been catalyzed by the Retrieval-Augmented Generation (RAG) paradigm, which augments parametric memory with non-parametric external data. However, the transition from proof-of-concept to production-grade RAG systems is hindered by three persistent challenges: low retrieval precision for complex queries, high rates of hallucination in the generation phase, and unacceptable latency for real-time applications. This paper presents a comprehensive analysis of the Higress RAG MCP Server, a novel, enterprise-centric architecture designed to resolve these bottlenecks through a "Full-Link Optimization" strategy. Built upon the Model Context Protocol (MCP), the system introduces a layered architecture that orchestrates a sophisticated pipeline of Adaptive Routing, Semantic Caching, Hybrid Retrieval, and Corrective RAG (CRAG). We detail the technical implementation of key innovations, including the Higress-Native Splitter for structure-aware data ingestion, the application of Reciprocal Rank Fusion (RRF) for merging dense and sparse retrieval signals, and a 50ms-latency Semantic Caching mechanism with dynamic thresholding. Experimental evaluations on domain-specific Higress technical documentation and blogs verify the system's architectural robustness. The results demonstrate that by optimizing the entire retrieval lifecycle - from pre-retrieval query rewriting to post-retrieval corrective evaluation - the Higress RAG system offers a scalable, hallucination-resistant solution for enterprise AI deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
