There are 85 repositories under text-to-sql topic.
Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.
🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using Agentic Retrieval 🔄.
⚡️ GenBI (Generative BI) queries any database in natural language, generates accurate SQL (Text-to-SQL), charts (Text-to-Chart), and AI-powered business intelligence in seconds.
Interact with your SQL database, Natural Language to SQL using LLMs
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
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.
[TKDE2025] Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL | A curated list of resources (surveys, papers, benchmarks, and opensource projects) on large language model-based text-to-SQL.
MindSQL: A Python Text-to-SQL RAG Library simplifying database interactions. Seamlessly integrates with PostgreSQL, MySQL, SQLite, Snowflake, and BigQuery. Powered by GPT-4 and Llama 2, it enables natural language queries. Supports ChromaDB and Faiss for context-aware responses.
Multiple paper open-source codes of the Microsoft Research Asia DKI group
[VLDB' 25] Synthesizing High-quality Text-to-SQL Data at Scale. SynSQL-2.5M is the first million-scale cross-domain text-to-SQL dataset.
Continuously updated paper list on advancements in Data Agents. Companion repo to our paper "A Survey of Data Agents: Emerging Paradigm or Overstated Hype?"
Translating natural language questions to structured query language (SQL)
A Model Context Protocol (MCP) server that enables natural language queries to databases
Make sense of it all. Semantic data modeling and analytics with a sprinkle of AI. https://totalhack.github.io/zillion/
SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL
A solution guidance for Generative BI using Amazon Bedrock, Amazon OpenSearch with RAG
Efficient, consistent and secure library for querying structured data with natural language
[ACL 2021] This is the project containing source codes and pre-trained models about ACL2021 Long Paper ``LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations".
LLM based AI Agent to automate Data Analysis for dbt projects with remote MCP server
🔥[VLDB'24] Official repository for the paper “The Dawn of Natural Language to SQL: Are We Fully Ready?”
This repository is intended for those looking to dive deep on advanced Text-to-SQL concepts.
Code and data for the paper "DBCᴏᴘɪʟᴏᴛ: Natural Language Querying over Massive Database via Schema Routing" (EDBT 2025)
🔥[ICML'25] Official repository for the paper "Alpha-SQL: Zero-Shot Text-to-SQL using Monte Carlo Tree Search"
[EMNLP'24] EHRAgent: Code Empowers Large Language Models for Complex Tabular Reasoning on Electronic Health Records
[NeurIPS'22] EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
A framework for converting natural language text inputs to corresponding Pandas, MongoDB, Kusto and Neo4j (Cypher) queries.
Labs for the "Build an agentic LLM assistant on AWS" workshop. A step by step agentic llm assistant development workshop using serverless three-tier architecture.
Code and trained model for Hybrid ranking network for text-to-SQL on WikiSQL
Leverage AI to generate GraphQL queries from plain text.
Easily get started with Spring-AI to develop various AI applications, including TextToSQL and private data AI application development. In addition to these capabilities, Spring-AI also supports integration with several other advanced AI technologies and platforms such as DeepSeek, Azure, Ollama, Vector Databases, Function Calling, MCP and RAG.
[ACL'24] Code and data of paper "When is Tree Search Useful for LLM Planning? It Depends on the Discriminator"