🌉 Demo on San Francisco City Data: SanFranciscoGPT.com
🇺🇸 Demo on US Census Data: CensusGPT.com
Welcome to textSQL, a project which uses LLMs to democratize access to data analysis. Example use cases of textSQL are San Francisco GPT and CensusGPT — natural language interfaces to public data (SF city data and US census data), enabling anyone to analyze and gain insights from the data.
With CensusGPT, you can ask any question related to census data in natural language.
These natural language questions get converted to SQL using GPT-3.5 and are then used to query the census database.
Here are some examples:
- 🔍 Five cities with a population over 100,000 and lowest crime
- 🔍 10 highest income areas in california
Here is a similar example from sfGPT:
We're splitting the roadmap for this project broadly into two categories:
Currently, textSQL only supports visualizing zip codes and cities on an interactive map and bar chart using Mapbox + Plotly. But data can be visualized in other interesting ways such as Heatmaps and Pie charts. Not every kind of data can be (or should be) visualized on a map. For example, a query like "What percent of total crime in San Francisco is burglary vs in New York City" is perfect for visualizing as a stacked bar chart, but really hard to visualize on map.
Bar Chart:
[coming soon] Heatmap:
[coming soon] Visualization-GPT: A way to use natural language to create and iterate on data visualizations in natural language through a text-to-vega engine.
You can now connect your own database & datasets to textSQL. Our vision is to continue to modularize and improve this process.
- Business intelligence — reducing the burden on technical employees to build & run queries for non-technical
- Public-facing interactive interfaces for government & NGO data — democratizing public data
- Empowering researchers — enabling journalists and other researchers to more easily explore data
A lot of the users of this project have asked for additional data for both CensusGPT and sfGPT — historical census data (trends), weather, health, transportation and real-estate data. Feel free to create a pull request, drop a link to your dataset in our Discord, or contribute data via our dedicated submission form.
More data → Better CensusGPT and sfGPT
Users build complex queries progressively. They start with a simple query like "Which neighborhoods in LA have the best schools?" and then progressively add details like "with median income that is under $100,000". One of the most powerful aspects of textSQL is enabling iterating on a query as a process of uncovering insights.
Join our discord
ReadMe for the backend here
ReadMe for the frontend here
Note: Census data, like any other dataset, has its limitations and potential biases. Some data may not be collected or reported uniformly across different regions or time periods, which can affect the comparability of results. Users should keep these limitations in mind when interpreting the results of their queries and exercise caution when making decisions based on census data.