通过LLM驱动的对话智能体连接工业专业知识与XR
Bridging Industrial Expertise and XR with LLM-Powered Conversational Agents
AI总结：
本文提出将RAG增强的LLMs与XR技术集成，构建含动态工具编排的LLM聊天引擎和语音驱动XR应用的系统，经多策略评估验证其工业知识检索性能，早期应用显示能提升工业培训与操作效率，助力工业5.0发展。
摘要：
本文介绍了一种将检索增强生成（RAG）增强的大型语言模型（LLMs）与扩展现实（XR）技术新颖集成的方法，以解决工业环境中的知识转移挑战。所提出的系统通过自然语言界面将特定领域的工业知识嵌入XR环境，为工人提供免提、上下文感知的专家指导。我们展示了该系统的架构，包括具有动态工具编排的LLM聊天引擎和具有语音驱动交互的XR应用程序。对各种分块策略、嵌入模型和向量数据库的性能评估表明，语义分块、平衡嵌入模型和高效向量存储为工业知识检索提供了最佳性能。该系统的潜力通过在机器人装配、智能基础设施维护和航空航天部件维修等多个工业用例中的早期实施得到了证明。结果表明，该系统有可能提高培训效率、远程协助能力和操作指导，符合工业5.0以人为本和弹性的工业发展方法。
英文摘要：
This paper introduces a novel integration of Retrieval-Augmented Generation (RAG) enhanced Large Language Models (LLMs) with Extended Reality (XR) technologies to address knowledge transfer challenges in industrial environments. The proposed system embeds domain-specific industrial knowledge into XR environments through a natural language interface, enabling hands-free, context-aware expert guidance for workers. We present the architecture of the proposed system consisting of an LLM Chat Engine with dynamic tool orchestration and an XR application featuring voice-driven interaction. Performance evaluation of various chunking strategies, embedding models, and vector databases reveals that semantic chunking, balanced embedding models, and efficient vector stores deliver optimal performance for industrial knowledge retrieval. The system's potential is demonstrated through early implementation in multiple industrial use cases, including robotic assembly, smart infrastructure maintenance, and aerospace component servicing. Results indicate potential for enhancing training efficiency, remote assistance capabilities, and operational guidance in alignment with Industry 5.0's human-centric and resilient approach to industrial development.
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。
