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Using Lumen AI

Once you're set up and exploring, here's how to get the most out of Lumen AI.

Message examples

Here are some ideas to spark your exploration:

Basic queries:

  • "What datasets are available?"
  • "Give me a summary of the dataset."
  • "What are the columns in the dataset?"
  • "What is the distribution of the 'species' column?"

Visualizations:

  • "Show me a scatter plot of 'flipper_length_mm' vs 'body_mass_g'."
  • "Create a histogram of 'bill_length_mm'."
  • "Show me a bar chart of average values by species."

Complex queries:

  • "Group by 'species' and show me the average 'flipper_length_mm'. Then plot a bar chart of the result."
  • "Filter the dataset for species 'Chinstrap' and calculate the median 'body_mass_g'. Then display and discuss the result."
  • "Create a histogram of 'bill_length_mm' and a box plot of 'flipper_length_mm' side by side."
  • "Add a new column calculating the ratio of 'flipper_length_mm' to 'body_mass_g'. Then plot a scatter plot of this ratio against 'bill_length_mm'."
  • "Select records where 'body_mass_g' is greater than 3500 and 'flipper_length_mm' is less than 200. Then provide summary statistics for these filtered records."

Get inspired:

  • "What could be interesting to explore?"

Combine multiple requests

You can ask the AI to perform several steps in one message. This helps you build complex analyses without multiple back-and-forths. For example: "Filter the data, create a visualization, then summarize the findings."

Understand the AI's reasoning

If you want to see how the AI arrived at an answer, enable Chain of Thought in the Settings menu (⚙️ icon in the left sidebar). This will show the LLM's reasoning steps in expandable cards within the chat.

Refine results

If results aren't what you expected, you have several options:

Rerun the query — Click the Rerun button to re-execute the last query. This is useful if there was a temporary error or if you want to see if the AI produces different results.

Continue the conversation — Send a new message to refine or adjust the results. For example: "Can you make that chart show only the top 5 items?" or "Add a trend line to the visualization."

Manually edit — Directly edit the SQL query or visualization specification in the editor panel. This works if you're comfortable with SQL or need precise control over the output.

Use manual editing for small tweaks (like changing chart colors or sort order), and send a new message for bigger changes to the underlying query or analysis approach.

Explorations

Each new computed result creates a new "Exploration" tab. Click the Explorations panel (accessible via the breadcrumbs at the top) to navigate between them and compare different analyses.

Export results

Export your session as a Jupyter notebook so you can reproduce, share, or build on your work.

Export current exploration: Click Export Notebook at the top-right of the screen to download a notebook containing the current exploration's questions, queries, and visualizations.

Export all explorations: Switch to Report mode (via the left sidebar) to see all explorations in a consolidated view, then click Export Notebook to download everything as a single notebook.

The notebook includes: - Markdown cells with your questions and AI responses - Code cells with SQL queries and Lumen specifications - Visualizations as executable code

Advanced options

Command-line configuration

Pass additional options when launching Lumen AI:

Specify agents
lumen-ai serve data.csv --agents SQLAgent VegaLiteAgent ChatAgent
Configure temperature
lumen-ai serve data.csv --temperature 0.8
Use specific provider
lumen-ai serve data.csv --provider anthropic --api-key your_key_here

Run lumen-ai serve --help to see all available options.

Agent names are flexible

Agent names are case-insensitive and the "Agent" suffix is optional: SQLAgent = sqlagent = sql

Python API configuration

For fine-grained control, use the Python API:

Advanced Python configuration
import lumen.ai as lmai

ui = lmai.ExplorerUI(
    data='data.csv',
    llm=lmai.llm.Anthropic(),  # (1)!
    default_agents=[lmai.agents.SQLAgent, lmai.agents.ChatAgent],
    log_level='INFO',
)
ui.servable()
  1. Use Anthropic instead of default OpenAI

See the configuration guides for all available options:

  • Prompts — Customize agent behavior
  • Sources — Connect to databases and files
  • LLM Providers — Configure your LLM
  • Agents — Use and customize agents
  • Tools — Extend capabilities with custom tools
  • Analyses — Add domain-specific analyses

Next steps

Now that you know the basics, dive deeper into specific topics:

  • Agents — Learn about the different agent types and how to customize them
  • Prompts — Fine-tune how agents respond
  • Context — Understand how agents share data