Open Source Friday with LIDA - Generate Infographics with LLMS

Project LAA

• Project LAA is an open-source project from Microsoft that helps generate visualizations and infographics for data using a declarative visualization language.
• LAA aims to reduce the cognitive burden of extracting insights from data by automatically generating potential visualization goals and complex visualizations without user input.
• LAA assumes that the data is in a decent format and does not focus on data cleaning.

Lia

• Lia is a tool that helps users create visualizations from data by generating natural language summaries, natural language intent, and reliable visualizations.
• Lia provides a Python API and a web API, has a visualization-specific user experience, and focuses on metrics for evaluating its performance.
• The summarizer module in Lia computes statistics and generates a semantic type and description for each column in the data set, improving the representation of the data.
• The goal exploration module generates questions for the data, and the visualization module generates code to create visualizations, which are then executed and displayed in the user interface.
• Users can interact with the visualizations, modify the code, and ask the language model to explain the visualizations or evaluate them across multiple dimensions.
• Lia can automatically repair visualizations by converting them to a different type or adding annotations.
• Lia can be configured to use different libraries for visualization, such as Matplotlib, Seaborn, Plotly, and Altair.
• It supports multiple model providers, including OpenAI, PaLM, Cohere, Hugging Face, and Anthropic.
• To control the quality of the generated charts, users can provide prompts, use self-evaluation modules, and implement self-repair mechanisms.
• Lia assumes that the input data is clean and ready for visualization, and it does not currently perform hypothesis testing or data cleaning.
• To evaluate the quality of the visualizations, users can reserve a more capable model as an evaluator or use a multimodal model that can assess both the code and the image of the visualization.

Li

• Li is a tool that allows users to generate visualizations and insights from their own data by dragging and dropping a CSV file into the interface.
• Li will automatically generate a summary of the data, along with questions, charts, and explanations.
• Li can also be used to create visualizations based on a specific persona or to modify existing visualizations based on text chat.
• Li supports multiple LLM providers, including OpenAI, Cohere, PaLM, and Hugging Face models.
• Li does not currently support real-time data streams, but users can implement a chunking strategy to visualize data as it becomes available.
• Li is most valuable to users who have no visualization experience or who want to quickly generate insights from their data.

Lighter

• Lighter is a tool that helps users visualize and understand data by translating natural language instructions into visualizations.
• It is particularly useful for non-experts in machine learning or data science, making it accessible for a wider range of users.
• Lighter has limitations, such as potential logic errors in code generation and differences in behavior when using smaller models.
• The quality of visualizations depends on the grammar used, with CBOR being a popular choice due to its extensive examples on GitHub.
• Lighter currently supports data sources that Pandas can process, but there is an opportunity to contribute by rewriting the data ingestion engine for larger-scale data.
• Data connectors for production deployments that use data sources beyond CSV files are not yet supported but are being considered for future development.
• Lighter is not designed to compare data or visualizations directly, but users can generate multiple visualizations and compare their evaluation scores.