text2sql

GenBI (Generative BI) for AI agents, an open-source, governed text-to-SQL through an open context layer that turns natural-language questions into trusted dashboards, charts, and SQL across 20+ data sources, such as BigQuery, Snowflake, PostgreSQL, ClickHouse, Amazon Redshift, Databricks and more.

一款真正的 AI 报表诞生了！JimuChatBI —— 首款免费开源对话式智能数据分析产品（Chat2BI）。 一句话生成报表和数据大屏，支持对话式 AI 修改优化；类 Excel 在线报表设计，兼容国产信创数据源。 对着 AI 说句话，报表就出来了——表格、图表、分组汇总自动生成，复杂报表场景轻松应对。想做数据大屏？同样一句话搞定，让数据分析进入 AI 时代。

🔥 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.

Chat-based SQL Client and Editor for the next decade

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

Analysis, Comparison, Trends, Rankings of Open Source Software, you can also get insight from more than 10 billion with natural language (powered by LLM). Follow us on Twitter: https://twitter.com/ossinsight

Aix-DB 基于 LangChain/LangGraph 框架，结合 MCP Skills 多智能体协作架构，实现自然语言到数据洞察的端到端转换。

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

OpenChatBI is an intelligent chat-based BI tool powered by large language models, designed to help users query, analyze, and visualize data through natural language conversations. It uses LangGraph and LangChain to build chat agent and workflows that support natural language to SQL conversion and data analysis.

Aser is a lightweight, self-assembling AI Agent frame.

五星大厨:全面Multi-Agent 的客服机器人，基于langraph实现，txt2sql ,txt2cypher, lightrag, 多模态 等

AI-native data platform: 60+ connectors, dlt-based ETL, DAG scheduling, per-source RAG knowledge bases, and sandboxed AI agents that query, chart, and analyze your data.

Asking yours data in a natural language way through pre-modeling (data models and semantic models).

A solution guidance for Generative BI using Amazon Bedrock, Amazon OpenSearch with RAG

Text2Sql.Net 是一个使用DotNet和Semantic Kernel开发的Text2Sql工具

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

Fine-Tuning Dataset Auto-Generation for Graph Query Languages.