  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.
