基于检索增强生成与对话历史管理的标准智能问答系统研究OA

To address the challenges in professional standard Q&A,such as high demands for answer accuracy,strong contextual dependencies,and personalized user needs,this paper develops an intelligent Q&A system based on a retrieval-augmented generation(RAG)architecture integrated with dialogue history management.The system employs a constructed standard semantic knowledge base as a precise knowledge source and adopts a dual-path"retrieval-generation"strategy:First,dense vector retrieval technology is used to quickly recall the most relevant standard clauses or knowledge subgraphs from the knowledge base.Then,the retrieved precise knowledge fragments,along with the original user query,are fed into a generative model pre-trained on large-scale standard texts and fine-tuned,to produce clear,fluent natural language answers,effectively ensuring both accuracy and readability.Furthermore,the system innovatively incorporates a dialogue history management module based on topic modeling and sequence encoding,which dynamically analyzes the objects,topics,and structure of user conversations,enabling intelligent storage and contextual retrieval of historical dialogues.This equips the system with multi-turn,coherent conversational capabilities.Application validation in the context of oilfield safety and environmental standards demonstrates that the system achieves high accuracy in single-turn Q&A and effectively resolves references and contextual understanding in multi-turn interactions,significantly enhancing the intelligence level of standard knowledge services.