摘要：FAQ问答系统有着广泛的实际应用场景,但传统问答系统的搭建通常需要将相关领域的知识转化为一系列的规则和知识图谱,且构建过程重度依赖人力,换个场景或用户都需要大量的重复劳动。针对上述问题并结合Bert模型和向量搜索引擎faiss,本文设计了一种基于深度学习语义匹配的FAQ问答系统的解决方案,介绍了系统的工作原理和设计流程,可以方便快速地搭建面向特定领域的问答系统,有效地减少了文本预处理的过程,实现了秒级的查询响应。系统测试发现,可以很好地对用户的查询进行语义匹配,并返回正确的答案,为构建各种问答系统提供了一种通用的解决思路。

摘要：随着信息技术的快速发展,用户对高效获取精准信息的需求日益增长。此外,为了支持国产操作系统软件生态,文章基于Deepin操作系统,集成RocketQA和ChatGLM模型,设计并实现了一个智能文档问答系统。系统首先使用RocketQA模型将问题向量化,利用faiss进行检索和排序,然后由RocketQA模型二次搜索和排序,对结果进行增强,检索到的文档通过ChatGLM模型转化为自然语言答案,以“参考链接 + 答案”的格式呈现给用户。最后,在deepin wiki数据集上进行了广泛的测试,结果表明系统在问答准确性和响应速度方面均表现优异。With the rapid development of information technology, users’ demand for efficiently obtaining accurate information is growing. In addition, to support the ecosystem of domestic operating system software, this paper designs and implements an intelligent document question-answering system based on the Deepin operating system, integrating the RocketQA and ChatGLM models. The system first uses the RocketQA model to vectorize the question, utilizes faiss for retrieval and sorting, then uses the RocketQA model for a second search and sorting to enhance the results. The retrieved documents are converted into natural language answers by the ChatGLM model and presented to the user in the format of “reference link + answer”. Finally, extensive testing was conducted on the deepin wiki dataset, and the results show that the system performs excellently in both question-answering accuracy and response speed.