Lumen is an open-source, extensible agent-based framework for data analysis and retrieval-augmented generation (RAG), designed to empower users, including non-programmers, to perform complex analyses through natural language. It facilitates the generation of data pipelines, visualizations, and insights from various data sources like files, SQL databases, and data lakes.

How It Works

Lumen leverages a declarative data model that allows Large Language Models (LLMs) to generate data transformation pipelines, visualizations, and other outputs. This approach enables LLMs to understand and manipulate data, generating SQL or Python pipelines, providing context through documents for RAG, and creating visualizations ranging from charts to dashboards. Outputs are serializable, allowing for sharing, further analysis in notebooks, or integration into dashboards. Lumen's extensibility with custom agents, analyses, and tools allows for domain-specific insights and tailored outputs.

Quick Start & Requirements

• Installation: conda install -c pyviz lumen or pip install 'lumen[ai]'
• Running: lumen-ai serve data.csv (replace data.csv with your data)
• Prerequisites: Python 3+ on Linux, Windows, or Mac. Anaconda or Miniconda recommended for installation.
• Documentation: [Link to docs not provided in README]

Highlighted Details

• Generates SQL and Python data pipelines from natural language.
• Supports RAG by providing LLMs with document context and embeddings.
• Outputs are serializable for sharing and integration into dashboards.
• Built on Panel, enabling rendering of diverse output types with minimal effort.

Maintenance & Community

• No specific details on contributors, sponsorships, or community channels were provided in the README.

Licensing & Compatibility

• The README states Lumen is "fully open-source" but does not specify a license type or compatibility details for commercial use.

Limitations & Caveats

The README does not detail specific limitations, known bugs, or deprecation status. Further investigation into the project's repository and community channels would be necessary to identify potential caveats.