Vizzy

Vizzy - Rapid data visualization with LLMs

Vizzy is a data visualization assistant that leverages large language models (LLMs) to help you visualize any kind of data. It supports connecting your own OpenAI API key or using a paid account, and emphasizes browser-stored API tokens rather than server storage. It highlights a public-project approach by default, encouraging contributions on GitHub while noting that all projects and data are public unless otherwise configured.

Key capabilities include creating visualizations from user-provided data files or exploring public projects, guided by a success rate metric (currently 77.3%). The platform also provides links to Vega-datasets for ready-to-use data and emphasizes community collaboration through GitHub issues and contributions.

Usage overview

• Provide a data file (upload) or explore existing public projects to visualize data.
• Optionally supply your own OpenAI API key for LLM-powered insights and visualizations.
• If you prefer not to use your own key, inquire about paid account options.
• Track progress and contribution invitations via GitHub (issues, forks, pull requests).

API token and authentication

• You can connect your OpenAI API key via a prompt-formula (the app will save the key only in your browser; it is not stored on Vizzy servers).
• You are responsible for any charges incurred when using the OpenAI API; typical usage incurs less than $1 per project but depends on behavior and can have bugs.
• It is recommended to set spending limits on your OpenAI account.

Privacy and data handling

• API tokens are stored in the browser only; Vizzy does not retain your key on its servers.
• Projects and data may be public by default; users should be mindful of privacy when uploading sensitive data.

Getting started

• Start visualizing by uploading a file or selecting a public project.
• Use the built-in guidance to generate visualizations with LLM-assisted reasoning.
• Visit GitHub to contribute or report issues; all public by default.

Safety and legal considerations

• When using public projects, ensure you comply with licenses and data privacy expectations.
• Be aware that public projects mean data and visualizations may be visible to others.

Core features

• LLM-powered data visualization generation from user data or public projects
• Optional integration with your own OpenAI API key stored locally in the browser
• Public project exploration with a 77.3% success rate metric to guide results
• Quick onboarding to Vega-datasets for readily usable data sources
• GitHub-based collaboration: issues, forks, and contributions
• Clear acknowledgment of usage costs and the need to manage OpenAI spend
• Emphasis on privacy by avoiding server-stored API keys