通过基于上下文和知识图的检索增强生成来推进工程研究

相关实验视频

Updated: Jan 9, 2026

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Soham Ghosh^1,2, Gaurav Mittal³

¹Department of Electrical Engineering, Black & Veatch, Overland Park, KS, United States.

Frontiers in artificial intelligence

|December 5, 2025

概括

新搜索增强代 (RAG) 大型语言模型 (LLM) 通过使用上下文检索和知识图表来提高工程中的技术准确性. 这些基于n8n自动化系统的RAG-LLM增强了工程代码和标准的实际一致性.

科学领域:

工程工程师工程师工程师
人工智能的人工智能
计算机科学计算机科学

背景情况:

大型语言模型 (LLM) 在语言任务方面表现出色,但在工程领域的技术准确性方面却很困难.
传统的检索增强生成 (RAG) 方法面临着与孤立信息和穿越语义链接技术数据的局限性.
现有的RAG技术不足以应对工程代码,标准和设计文件的复杂性.

研究的目的:

引入新的,可部署的RAG-LLM,适用于使用n8n自动化系统的工程应用.
提高工程背景下LLM输出的事实一致性和技术准确性.
解决传统RAG在处理复杂,语义相关的技术信息方面的局限性.

主要方法:

在工程领域的n8n自动化系统上开发RAG-LLM.
采用了上下文RAG方法,通过将检索内容与查询上下文对齐来提高相关性.
集成RAG与知识图检索,以更深入地理解复杂的技术信息.

主要成果:

证明了工程代码,标准和设计文件的LLM输出技术准确性和事实一致性的提高.
语境RAG方法提高了检索信息的相关性.
知识图集成在复杂的工程领域促进了深刻的语义理解.

结论:

开发的RAG-LLMs为工程应用中的技术准确性提供了显著的改进.
语境RAG和知识图集成是提高专业领域LLM绩效的有效策略.
该研究提供了关于部署,优势和弱点的实用见解,并为可重复性提供了共享的工作流.

关键词:

在法学士和情报方面.在 RAG 架构中,RAG 架构是具有背景意识的信息检索信息检索工程设计自动化工程设计自动化知识图是知识图.

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