Conversational Search over Structured and Unstructured Data with LLMs

Online demo: https://yelpbot.genie.stanford.edu

SUQL stands for Structured and Unstructured Query Language. It augments SQL with several important free text primitives for a precise, succinct, and expressive representation. It can be used to build chatbots for relational data sources that contain both structured and unstructured information. Similar to how text-to-SQL has seen great success, SUQL can be used as the semantic parsing target language for hybrid databases, for instance, for:

Several important features:

• SUQL seamlessly integrates retrieval models, LLMs, and traditional SQL to deliver a clean, effective interface for hybrid data access;
  • It utilizes techniques inherent to each component: retrieval model and LM for unstructured data and relational SQL for structured data;
• Index of free text fields built with faiss, natively supporting all your favorite dense vector processing methods, e.g. product quantizer, HNSW, etc.;
• A series of important optimizations to minimize expensive LLM calls;
• Scalability to large databases with PostgreSQL;
• Support for general SQLs, e.g. JOINs, GROUP BYs.

One important component of SUQL is the answer function. answer function allows for constraints from free text to be easily combined with structured constraints. Here is one high-level example:

For more details, see our paper at https://arxiv.org/abs/2311.09818.

There are two main ways of installing the SUQL library.

Ideal for integrating the SUQL compiler in a larger codebase / system. See install_pip.md for details.

Ideal for using this repo to build a SUQL-powered conversational interface to your data out-of-the-box, like the one for https://yelpbot.genie.stanford.edu discussed in the paper. See install_source.md for details.

Check out conv_agent.md for more information on best practices for using SUQL to power your conversational agent.

Check release_notes.md for new release notes.

If you encounter a problem, first check known_issues.md. If it is not listed there, we welcome Issues and/or PRs!

To replicate our results on HybridQA and restaurants in our paper, see paper_results.md for details.

If you find this work useful to you, please consider citing us.

@inproceedings{liu2024suql,
    title={SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models}, 
    author={Shicheng Liu and Jialiang Xu and Wesley Tjangnaka and Sina J. Semnani and Chen Jie Yu and Monica S. Lam},
    booktitle = {Findings of the Association for Computational Linguistics: NAACL 2024},
    year={2024}
}