Local Knowledge Graph

Ask a local model a question, watch it reason step by step, and see the steps drawn as a graph where the edges are how similar the steps are to each other.

The run starts by breaking the question into angles worth checking, then works through them — about eight steps, each with a named job rather than a quota to fill. Any arithmetic a step relies on is handed over as an expression and evaluated exactly, in fractions, by mpeqs. Those sums are shown above the graph: they are the one part of a run the model did not decide, and you can check 20-13.5 = 6.5 at a glance in a way you cannot check a paragraph of reasoning.

The graph draws in two colours, because it holds two kinds of claim. Blue links steps by how similar their embeddings are — an association, with no truth value, and the thing that makes indirect knowledge visible. Green is what an exact evaluator settled: a sum, or a conversion between two units, derived from exact ratios. A reader should never have to guess which is which.

Everything runs on your machine. Nothing is uploaded anywhere.

Headless, and as RDF

A run can be started without a browser, polled, and taken as RDF — the graph as text, for anything that would rather query it than look at it.

ID=$(curl -sX POST localhost:5100/jobs -H 'content-type: application/json' \
       -d '{"query":"How many seconds are there in 23 weeks?"}' | jq -r .id)

curl -s localhost:5100/jobs/$ID              # {"state":"running","steps":3,...}
curl -s localhost:5100/jobs/$ID/stream       # N-Triples, live, one triple per line
curl -s localhost:5100/jobs/$ID/rdf          # Turtle, once it has finished
curl -sX DELETE localhost:5100/jobs/$ID      # stop it

Two formats for two purposes. N-Triples streams: each line is a complete document, so a consumer can parse what has arrived without waiting for the end. Turtle is prefixed and readable, and needs the whole document, so it is what a finished run serialises to.

The lkg:basis predicate carries the same distinction as the colours, so a consumer can take only the part it can rely on:

<run/a1b2/link/Step1-Step2>  lkg:similarity "0.8371"^^xsd:decimal ;
                             lkg:basis      lkg:Embedding .     # measured association

<run/a1b2/conversion/1>      lkg:statement  "23 week = 13910400 second" ;
                             lkg:from       <unit/week> ;
                             lkg:to         <unit/second> ;
                             lkg:basis      lkg:Exact .         # derived, reproducible

Run it

pip install mpe-lkg
mpe-lkg

Then open http://localhost:5100.

It needs a local model, which it reaches through Ollama. You do not need to work that out from here — start it and it will tell you what it found, what is missing, and the one command that fixes it. mpe-lkg doctor reports the same thing without starting the server, and exits non-zero, so it works in a script.

Python 3.10 or newer. The wheel is py3-none-any, so nothing is compiled and the same artefact serves Linux, macOS and Windows — all three tested on every push.

git clone https://github.com/punnerud/Local_Knowledge_Graph
cd Local_Knowledge_Graph
python3 -m venv .venv && .venv/bin/pip install -e .
.venv/bin/mpe-lkg

from mpe_lkg import create_app, health

print(health())
create_app().run(port=5100)

More

Licence

The mpedb License 1.0 — the same licence as mpedb and MPEqs, byte for byte.

Free of charge for every person and every organization, with one exception: a group whose revenue or valuation exceeds five billion dollars owes a one-time fee of seven US cents per device. Not an OSI-approved licence.

_{Published to PyPI as mpe-lkg — Morten Punnerud-Engelstad Local Knowledge Graph.}