nl2sql

Curated tutorials and resources for Large Language Models, Text2SQL, Text2DSL、Text2API、Text2Vis and more.

A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL

[TKDE'25] This is a continuously updated handbook for readers to easily track the latest Text-to-SQL techniques in the literature and provide practical guidance for researchers and practitioners. Official repo for A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?

基于大模型和 RAG 的智能问数系统

追一科技首届中文NL2SQL挑战赛决赛第3名方案+代码

A relation-aware semantic parsing model from English to SQL

AI Tool for querying natural language on tabular data.

阿里天池首届中文NL2SQL挑战赛top6

The Resources for "Natural Language to Logical Form" ; "自然语言转逻辑形式"研究资料收集。

The Open Data QnA python library enables you to chat with your databases by leveraging LLM Agents on Google Cloud. Open Data QnA enables a conversational approach to interacting with your data by implementing state-of-the-art NL2SQL / Text2SQL methods.

Content Enhanced BERT-based Text-to-SQL Generation https://arxiv.org/abs/1910.07179

Code for PointerSQL, PT-MAML, Execution-guided Decoding papers

This repository is intended for those looking to dive deep on advanced Text-to-SQL concepts.

CRM Data Q&A Agent - Advanced RAG with NL2SQL over Salesforce Data

This repo accelerates development of RAG applications with rich data sources including SQL Warehouses and documents analysed with Azure Document Intelligence

[arXiv'25] Official Repository for the Paper "SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning"

Code and trained model for Hybrid ranking network for text-to-SQL on WikiSQL

The easiest and most comprehensive framework for building enterprise-grade NL2SQL solutions at scale.

A NL2SQL plugin based on FocusSearch keyword parsing, offering greater accuracy, higher speed, and more reliability!

Powerful tool designed to generate SQL queries from natural language (NL2SQL) using Microsoft’s Semantic Kernel framework. This project aims to bridge the gap between human-readable queries and SQL, enabling easy and efficient database interactions with AI-driven language models.