🔥 基于大模型和 RAG 的智能问数系统，对话式数据分析神器。Text-to-SQL Generation via LLMs using RAG.

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

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.

A relation-aware semantic parsing model from English to SQL

AI Tool for querying natural language on tabular data.

Continuously updated paper list on advancements in Data Agents. Companion repo to our paper "A Survey of Data Agents: Emerging Paradigm or Overstated Hype?"

阿里天池首届中文NL2SQL挑战赛top6

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

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

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

Agentar-Scale-SQL is a novel framework that leverages scalable computation to significantly improve Text-to-SQL performance.

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

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

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.

The Most Powerful Open-Source Autonomous SQL Agents - RAG, NL2SQL

EvalBench is a flexible framework designed to measure the quality of generative AI (GenAI) workflows around database specific tasks.

End-to-end natural language to SQL system: schema-aware model fine-tuning, retrieval-augmented prompting, and production-grade CLI, powered by a custom fine-tuned Phi-4 Mini.

Official repository for the paper "EllieSQL: Cost-Efficient Text-to-SQL with Complexity-Aware Routing".

[NAACL 2024] TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition