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

追一科技首届中文NL2SQL挑战赛决赛第3名方案+代码

This is a continuously updated handbook for readers to easily track the latest NL2SQL (Text-to-SQL) techniques in the literature and provide practical guidance for researchers and practitioners. If we missed any interesting work, feel free to contact us.

A relation-aware semantic parsing model from English to SQL

AI Tool for querying natural language on tabular data.

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

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

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.

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.

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

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

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

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

Table2answer: Read the database and answer without SQL https://arxiv.org/abs/1902.04260

Using Database Rule for Weak Supervised Text-to-SQL Generation https://arxiv.org/abs/1907.00620

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

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.