SQLord: A Robust Enterprise Text-to-SQL Solution via Reverse Data Generation and Workflow Decomposition

TL;DR

SQLord is an enterprise-level Text-to-SQL framework that leverages reverse data generation and workflow decomposition, achieving high accuracy and robustness in complex real-world business scenarios.

Contribution

It introduces a novel reverse data generation method and an automated workflow decomposition approach for improved enterprise Text-to-SQL performance.

Findings

Offline tests outperform existing baselines.

Online accuracy exceeds 90%.

Successfully applied on a large B2B e-commerce platform.

Abstract

Transforming natural language into SQL queries (NL2SQL) is crucial for data-driven business applications. Existing frameworks, trained on open-source datasets, struggle with complex business logic and lack domain-specific data for fine-tuning. Additionally, evaluation methods often require annotated data and executable database environments, which are scarce in real-world scenarios. To address these challenges, we propose SQLord, an enterprise-level NL2SQL framework. First, SQLord introduces a data reverse generation approach to convert raw SQL statements into annotated data for supervised fine-tuning (SFT). Second, it proposes a decomposition method for complex queries using an automated workflow generator. Additionally, SQLord features a comprehensive GPT-Judge evaluation framework, including Execution Evaluation (EXE), Query-SQL Evaluation (QSE), and SQL-SQL Evaluation (SSE), tailored to diverse scenarios. Offline tests significantly outperform state of the art baselines, and online accuracy consistently exceeds 90, highlighting SQLord's advantages and effectiveness in complex real world scenarios. SQLord has been successfully applied across multiple scenarios on the world's largest B2B e-commerce platform.