With the rapid advancement of natural language processing technologies,
generative artificial intelligence techniques, represented by large language
models (LLMs), are gaining increasing prominence and demonstrating significant
potential for applications in safety engineering. However, fundamental LLMs
face constraints such as limited training data coverage and unreliable
responses. This study develops a vector database from 117 explosion accident
reports in China spanning 2013 to 2023, employing techniques such as corpus
segmenting and vector embedding. By utilizing the vector database, which
outperforms the relational database in information retrieval quality, we
provide LLMs with richer, more relevant knowledge. Comparative analysis of LLMs
demonstrates that ChatSOS significantly enhances reliability, accuracy, and
comprehensiveness, improves adaptability and clarification of responses. These
results illustrate the effectiveness of supplementing LLMs with an external
database, highlighting their potential to handle professional queries in safety
engineering and laying a foundation for broader applications.

通过开发一个向量数据库，该研究证明外部数据库对大型语言模型具有补充作用，提高了可靠性、准确性和全面性，并改善了回答的适应性和解释能力，展示了大型语言模型在安全工程中处理专业问题的潜力，为更广泛的应用奠定了基础。