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...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.展开更多
文摘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.
