textSQL democratizes data analysis by enabling users to query databases using natural language. It targets researchers, journalists, and business users who need to extract insights from data without writing SQL. The project provides natural language interfaces to public datasets like US Census and San Francisco city data, simplifying data exploration and discovery.

How It Works

The system leverages Large Language Models (LLMs), specifically GPT-3.5, to translate natural language questions into executable SQL queries. These queries are then run against the target database. This approach allows users to interact with data conversationally, progressively refining queries to uncover deeper insights, which is a key advantage over traditional query interfaces.

Quick Start & Requirements

• BYOD Setup: Instructions for connecting your own database are available here.
• Prerequisites: Requires access to OpenAI API keys for GPT-3.5. Specific dataset requirements depend on the chosen data source.
• Demos: Live demos are available for San Francisco data (SanFranciscoGPT.com) and US Census data (CensusGPT.com).

Highlighted Details

• Enables progressive query building for iterative data exploration.
• Supports basic visualizations (maps, bar charts) via Mapbox + Plotly, with more planned.
• Offers "Bring Your Own Data" (BYOD) functionality for self-hosting and custom datasets.
• Provides natural language interfaces for public datasets like SF city data and US Census data.

Maintenance & Community

The project is associated with Julius.ai. Community engagement is encouraged via their Discord Server.

Licensing & Compatibility

The README does not explicitly state the license. Users should verify licensing for commercial use or integration with closed-source projects.

Limitations & Caveats

The project relies on external LLM APIs (OpenAI), incurring associated costs and potential rate limits. Census data, like any dataset, may contain limitations and biases that users should consider during analysis.