关键词: 中文NL2SQL, 检索增强生成, 大语言模型, 重排序, 文物艺术品拍卖

Abstract: NL2SQL (Natural Language to SQL) can reduce the technical threshold for non-professionals to operate databases, and improve user experience and work efficiency. Retrieval-Augmented Generation (RAG) technology can improve the performance of NL2SQL by introducing external knowledge bases. In view of the problems of high missed detection rate of retrieval strategy and weak relevance of recall context in the current RAG application of NL2SQL, this paper provides a RAG method of sequential retrieval reranking (RAG-SRR), which optimizes the knowledge base construction, retrieval recall strategy, and prompt design. Firstly, the domain knowledge base was constructed from three aspects: question-answer pairs, professional terms, and database structures. The question-answer pairs were constructed based on the high-frequency processing and query questions of the auction supervision of cultural relics and artworks, the professional terms were constructed based on auction industry standards, and the database structures were constructed based on data of Artron Art Auction Network. Secondly, a sequential retrieval strategy was adopted in the retrieval stage, and different priorities were set for three types of knowledge bases. In the recall stage, retrieved information were reranked. Finally, the principles of prompt optimization design were given in the prompt word design, and prompt template was provided. Experimental results show that in domain dataset and Spider dataset, the execution accuracy of RAG-SRR and BERT-based model and RESDSQL model are improved by at least 19.50 percentage points, 24.20 percentage points, and 12.17 percentage points, 8.90 percentage points, respectively. Under the same large language model, the execution accuracy of RAG-SRR is at least 12.83 percentage points and 16.33 percentage points higher than that of the unoptimized RAG, respectively. The execution accuracy is at least improved by 0.30 percentage points and 3.90 percentage points respectively compared with DIN-SQL and other methods. It can be seen that the RAG-SRR method has strong practicality and portability.

Key words: Chinese NL2SQL, Retrieval-Augmented Generation &#40, RAG&#41, large language model