基于RAG与领域术语增强的电力通信客服知识库构建方法OA

In order to address the issues of factual inaccuracies and poor domain adaptability in the field of electric power communication customer service,The power communication customer service sector is highly specialized,characterized by extensive terminology and rapidly evolving knowledge.The direct application of general large language models in this domain often leads to factual inaccuracies and poor adaptability.To address these challenges,this paper proposed an automatic knowledge base construction method for electric power communication customer service based on retrieval-augmented generation(RAG)and domain terminology enhancement was proposed.Firstly,unsupervised new word discovery and domain dictionary construction techniques were employed to automatically build a domain-specific terminology library for power communication,which subsequently optimized the semantic understanding capabilities of the retrieval system.Secondly,a hierarchical retrieval strategy was designed,integrating key terminology with semantic vectors to enable precise and automatic retrieval of relevant knowledge fragments from standard documents and historical question-answer data.Finally,using the retrieved information as enriched context,a dynamic instruction template was developed to automatically guide a large language model in generating accurate and standardized question-answer pairs.A closed-loop optimization mechanism further enhanced the quality of the generated output.Experimental results show that the constructed power communication customer service knowledge base achieves a terminology standardization rate of 94.2%,a parameter accuracy of 92.8%,and standard citation accuracy of 86.7%.Domain terminology enhancement and the hierarchical retrieval strategy significantly contribute to the performance improvements.The generated question-answer pairs substantially outperform those produced by baseline direct generation models in terms of quality,effectively mitigating the problem of model hallucination.The proposed method provides a reliable technical pathway for the development of intelligent customer service systems in vertical industries such as power communication.