Installation

Operating Environments

DACKAR runs on Microsoft Windows, Apple macOS, and Linux. Python 3.10–3.12 is supported; CI tests on 3.11.

1. Install uv

# Linux / macOS
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# or via Homebrew on macOS
brew install uv

2. Clone DACKAR

git clone https://github.com/idaholab/DACKAR.git
cd DACKAR

For SSH cloning see https://help.github.com/articles/connecting-to-github-with-ssh/.

3. Install Dependencies

Pick the install profile that matches your workflow:

uv sync                                                       # core only
uv sync --group rca --group kg --group nlp-extra --group dev  # full RCA workflow
uv sync --all-groups                                          # everything

Available optional dependency groups:

Group	Use when
nlp-extra	Optional NLP pipes (pywsd, contextual spell check)
anomaly	Using dackar.anomalies (matrix-profile, two-sample tests)
kg	Loading data into Neo4j via dackar.knowledge_graph
viz	Word-cloud rendering in dackar.utils.visualize
rca	Running the AI-enhanced RCA demos under src/dackar/RCA/
docs	Building Sphinx documentation
dev	Tests and notebook examples

4. Bootstrap Runtime Models

uv run python scripts/bootstrap_models.py

This downloads the NLTK corpora used by similarity analysis and retrains the quantulum3 classifier. The en_core_web_lg spaCy model is installed automatically as a project dependency.

Behind a Corporate SSL Proxy

If model downloads fail with SSL errors, pass --insecure-ssl:

uv run python scripts/bootstrap_models.py --insecure-ssl

This disables HTTPS certificate verification for the bootstrap downloads only.

Running DACKAR

uv run python -m dackar.main -i system_tests/ner.toml
uv run pytest tests/