Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases through Text-to-NoSQL Translation

Jinwei Lu, Yuanfeng Song, Zhiqian Qin, Haodi Zhang, Chen Zhang +1 more
2/16/2025
cs.DBcs.AI

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Abstract

NoSQL databases have become increasingly popular due to their outstanding performance in handling large-scale, unstructured, and semi-structured data, highlighting the need for user-friendly interfaces to bridge the gap between non-technical users and complex database queries. In this paper, we introduce the Text-to-NoSQL task, which aims to convert natural language queries into NoSQL queries, thereby lowering the technical barrier for non-expert users. To promote research in this area, we developed a novel automated dataset construction process and released a large-scale and open-source dataset for this task, named TEND (short for Text-to-NoSQL Dataset). Additionally, we designed a SLM (Small Language Model)-assisted and RAG (Retrieval-augmented Generation)-assisted multi-step framework called SMART, which is specifically designed for Text-to-NoSQL conversion. To ensure comprehensive evaluation of the models, we also introduced a detailed set of metrics that assess the model's performance from both the query itself and its execution results. Our experimental results demonstrate the effectiveness of our approach and establish a benchmark for future research in this emerging field. We believe that our contributions will pave the way for more accessible and intuitive interactions with NoSQL databases.

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Cite this paper

@article{lu2025bridging,
  title  = {Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases through Text-to-NoSQL Translation},
  author = {Jinwei Lu and Yuanfeng Song and Zhiqian Qin and Haodi Zhang and Chen Zhang and Raymond Chi-Wing Wong},
  year   = {2025},
  eprint = {2502.11201},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.11201v2}
}

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