← All use cases
The scenario
You have been recording facts for months. Nobody built an ontology, nobody
tagged anything — and there is already a graph of who and what keeps appearing
together, because extraction names entities as it stores each memory.
Step 1 — Read the graph
Nodes are entities; edges are co-occurrence — two entities appearing in the
same memory, weighted by how often.
Step 2 — Address one entity
observations fills as the space consolidates what it has stored about that
entity — which is how you get a dossier nobody wrote.
Step 3 — Cut the noise
On a real space most entities are mentioned once. min_count is what turns a
hairball into something readable.
Evals
- Record four facts that connect three entities. Check the expected edges exist
with the expected weights.
- Raise
min_count and confirm the long tail drops out.
- Fetch an entity and check
observations is populated — if it is empty on a
busy space, consolidation has not run yet rather than failed.
Guardrails
Edges are co-occurrence, not typed relationships. The graph says two
entities keep showing up together. It does not say one owns, acquired or
reports to the other. Do not build a UI, or a sales claim, that implies
typed predicates.
- Entity names come from extraction, so the same person can appear under two
spellings if your source text does.
- Read it as a map, not a database. It is excellent for “what is this space
mostly about” and wrong for anything needing exactness.
The script
Complete and runnable:
knowledge_graph.py. 
