RelationalAI Templates

This repository contains runnable RelationalAI templates that demonstrate end-to-end solution patterns across optimization, graph analytics, fraud detection, supply chain planning, and other decision intelligence workflows.

Each template lives in its own folder with code, sample data, and a template-specific README. Templates are grouped into versioned directories so the repository can support multiple generations of examples side by side.

Repository layout

Path	Purpose
sample-template/	Starting point for authoring a new template. Includes the expected file layout and README template.
v0.13/	Older templates for version 0.13 of the relationalai Python package. Included for reference, only.
v0.14/	Older templates for version 0.14 of the relationalai Python package. Included for reference, only.
v1/	Newer templates for versions >=1.0 of the relationalai Python package. Use these for new development.

Within a template folder, you will usually find:

• README.md with the problem statement, prerequisites, and run instructions
• pyproject.toml for template-local dependencies
• a main runner such as <template>.py or a notebook
• data/ containing sample input data when the template uses local files

Choose a template

Use the version folder and template README to pick the example that matches your goal.

• If you want a minimal onboarding example, start with v1/simple-start/.
• If you want to create a new template, start from sample-template/.

For a full list of templates and their descriptions, check out the v1/README.md file.

Getting started with a template

The exact setup is documented in each template's README, but the workflow is consistent:

1. Pick a template folder.
2. Create a virtual environment inside that folder.
3. Install the template's dependencies.
4. Configure RelationalAI access if the template connects to a live environment.
5. Run the script or notebook described in the template README.

Example workflow:

cd v1/simple-start
python -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
python -m pip install .

After installation, continue with the template-specific instructions in that folder's README.

What a template includes

Most templates are designed to be runnable and inspectable without additional repository-level setup.

• Code: a small, focused implementation of the use case
• Sample data: enough data to exercise the model end to end
• Documentation: problem framing, prerequisites, quickstart, and customization notes
• Metadata: template metadata used by the RelationalAI Docs site to surface the template in the template gallery.

Contributing

To add or update a template:

1. Copy sample-template/ into the version folder you are targeting.
2. Implement the model, runner, sample data, and metadata.
3. Replace the README placeholders with template-specific content.
4. Review the result before opening a pull request.

Repository-level linting for template Python code uses Ruff:

ruff check path/to/my/template

The same check runs in CI via .github/workflows/lint.yml.

See CONTRIBUTING.md for the full contribution workflow.