Research and Implementation of Enterprise-level Private Knowledge Base in the Construction Field Based on GraphRAG Technology
Keywords:
Knowledge, Artificial Intelligence, GraphRAG, Large Model, Neo4JAbstract
As one of the important applications of AI, Question Answering (QA) combines NLP and machine learning technologies to achieve efficient and accurate knowledge 问答. General large models such as ChatGPT, Wenxin Yiyan, and Tongyi Qianwen are powerful, but they perform poorly in specific fields such as construction professional knowledge 问答. This paper relies on Microsoft’s GraphRAG technology, combines large models with vector databases, learns construction professional knowledge, policies, and regulations, and significantly enhances the learning ability in the construction field. By constructing a visual knowledge graph with Neo4J, flexible search from local to global is achieved, and efficient local storage and sharing of knowledge are realized. Practice shows that the performance of this system in construction field 问答 exceeds that of general large models, and local deployment ensures data privacy and security, providing users with a reassuring and efficient service experience, and providing a new solution for specific field knowledge 问答.
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Copyright (c) 2026 Guojin Li, Zhenhao Wu, Jiujia Huang
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