python

Python / Notebook tutorial

No Hyper-Knowledge model key is required for this structured-data example. The four nodes and two hyperedges come from the short tutorial fixture, not an independent historical review.

Install the runtime in a retained Python environment, then run:

python examples/python/quickstart.py --output output/tutorial

Or use the same input through the CLI:

hk bundle import examples/python/graph.json --source notes.md=examples/python/notes.md -o output/cli-bundle --json
hk bundle validate output/cli-bundle --quality showcase --json
hk visualize output/cli-bundle -o output/cli-workbench.html --no-open --json

Open quickstart.ipynb in Jupyter. Select the kernel belonging to the environment where Hyper-Knowledge is installed. Its code runs without network access; its optional %pip installation instruction is in Markdown.

The notebook constructs its own input and uses a new temporary output directory on every execution. The script protects existing output; choose a new --output directory on subsequent runs.