LLM assistant add-on

The optional HUGIML LLM assistant adds a natural-language interface for common modeling and governance workflows. It can answer package-usage questions from the local documentation index, run supported HUGIML actions, summarize fitted model evidence, and produce lightweight HTML outputs for review.

Installation

Install the optional dependencies with one of the Streamlit-oriented extras:

pip install "hugiml-core[llm]"
# or install the dashboard stack if you want both Streamlit UIs
pip install "hugiml-core[dashboard]"

Launch the assistant from an installed environment:

hugiml-llm

Useful CLI commands include:

hugiml-llm status
hugiml-llm list-datasets
hugiml-llm chat --dataset churn_synthetic --no-llm
hugiml-llm ask "tune churn_synthetic and explain the strongest patterns" --dataset churn_synthetic
hugiml-llm demo-html --output examples/governance_qna_churn.html

Fast and thinking response modes

The assistant supports two response styles:

  • Fast uses deterministic routing and local HUGIML summaries. This is the best default for quick API, dataset, tuning, and fitted-run questions.

  • Thinking can use an Ollama-backed writing pass when an installed local model is available. This is useful for longer governance-style explanations or more polished narrative summaries.

The deterministic path remains available without Ollama. When Ollama is used, the add-on checks lightweight local model options and keeps execution bounded by the supported HUGIML action schema.

Supported workflows

The add-on is designed around safe, structured workflows rather than unrestricted source editing. Supported actions include dataset inspection, model fitting, hyperparameter tuning, model comparison, pattern and feature-importance review, controlled pruning summaries, governance report generation, prediction review, and local documentation Q&A.

Built-in assets

The package includes small built-in demonstration datasets and model-profile configuration under hugiml.llm package data. User datasets can be registered through the UI or CLI, and installed environments store user-side LLM assets under the configured HUGIML LLM home directory.

API modules

See API reference for the public Python modules in hugiml.llm.