PIPELINE

Main Content Area

ASSISTANT

Each lab is organised as a sequence of steps stored in lab_steps.toml. The numbered buttons at the top let you jump between them. Ask questions or describe transformations in the text area—AGILab forwards the prompt to the Responses API together with the selected DataFrame metadata. The code editor reacts to the toolbar actions:

  • Save keeps the snippet as-is in the current step.

  • Next persists the snippet and advances to a fresh step.

  • Remove deletes the step from lab_steps.toml.

  • Run writes the snippet to lab_snippet.py, executes it and stores any produced dataframe under lab_out.csv so the preview and the Orchestrate/Analysis pages can consume the result.

The runtime is chosen from the Execution environment box below the editor. If you pick a concrete virtual environment path the snippet runs via run_agi inside that environment (the path is kept with the step under the E field). Leaving the selector on the default AGILab environment falls back to run_lab, reusing the managed runtime that ships with the app. In both cases the exported dataframe and history behave identically.

The assistant automatically reloads the most recent dataframe and shows it below the editor. If nothing has been saved yet, you will see a reminder to run a snippet first.

When your lab step is based on app execution, use the Pipeline add flow:

  • Generate the target snippet in ORCHESTRATE (typically AGI.run).

  • In Add step (or New step on an empty project), choose Step source = gen step to regenerate from prompt, or select an existing exported snippet to import it directly.

  • Imported snippets are marked read-only and run with the project manager runtime.

If you change values in Orchestrate arguments, regenerate or re-import the snippet in Pipeline before running the step.

HISTORY

Inspect or tweak the raw lab_steps.toml via the code editor. Saving the file here immediately refreshes the assistant tab.

Troubleshooting and checks

Use these checks if Pipeline steps are confusing or fail to execute:

  • If numbered step buttons do not match lab_steps.toml, open HISTORY and confirm the selected file is the current module’s lab file.

  • If execution fails on a stale path, regenerate or re-import the snippet in PIPELINE before rerunning the step.

  • If Run writes no dataframe, check the destination under ${AGILAB_EXPORT_ABS}/<module>/lab_out.csv and ensure Write permissions are enabled for the selected execution environment.

  • If an imported notebook is not loaded, re-upload .ipynb and then reopen the step editor to force a refresh.

  • If MLflow link fails to open, verify activate_mlflow completed and port forwarding is not blocked locally.

See also