Does it support Retrieval-Augmented Generation (RAG)?

Yes, Lumen AI can incorporate document context from PDFs or Markdown files to help the AI understand specific business rules and data dictionaries.

Lumen AI is an open-source framework for building AI-powered conversational data exploration tools that translate natural language into queries and charts.

Can I use this with my own SQL database?

Yes, Lumen AI supports various data sources including PostgreSQL, DuckDB, Snowflake, and standard file formats like CSV and Parquet.

Is Lumen AI different from standard Lumen dashboards?

Lumen AI is for ad-hoc, conversational exploration where questions vary, while standard dashboards are for fixed, declarative, and repeatable views.

Which LLMs does this skill support?

It supports a wide range of providers including Anthropic Claude, OpenAI, Google Gemini, Mistral, and local models via Ollama or LlamaCPP.

Lumen AI Explorer

Name: Lumen AI Explorer
Author: cdcore09

bycdcore09

•

Ciencia de Datos y ML

Enables natural language data exploration, automated SQL generation, and interactive visualization using agent-based AI.

Acerca de

Lumen AI is a specialized framework for building conversational data analysis interfaces that translate plain English queries into SQL, statistical summaries, and interactive charts. It leverages an agent-based architecture to coordinate tasks between different LLMs—including Claude, GPT, and local models—making it ideal for self-service analytics, ad-hoc data exploration, and integrating RAG-based document context into data workflows. By abstracting the complexity of data querying, it allows developers to deploy powerful, intuitive analytics tools for non-technical stakeholders.

Características Principales

Extensible framework for custom agents and domain-specific analysis
Multi-agent architecture for specialized data tasks
Natural language to SQL and visualization conversion
Integrated RAG for document-aware data context
Support for cloud (Anthropic, OpenAI) and local (Ollama) LLMs
2 GitHub stars

Casos de Uso

Rapid ad-hoc data exploration and prototyping of interactive visualizations
Building self-service business intelligence portals for non-technical users
Implementing AI-driven analytics on sensitive data using local LLM deployments

Acerca de

Características Principales

Extensible framework for custom agents and domain-specific analysis
Multi-agent architecture for specialized data tasks
Natural language to SQL and visualization conversion
Integrated RAG for document-aware data context
Support for cloud (Anthropic, OpenAI) and local (Ollama) LLMs
2 GitHub stars

Casos de Uso

Rapid ad-hoc data exploration and prototyping of interactive visualizations
Building self-service business intelligence portals for non-technical users
Implementing AI-driven analytics on sensitive data using local LLM deployments