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fix: correct schema mismatches and invalid enum values in README examples

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- TemporalGraphQuery.query_time_range() and RDFExporter.export() both
  expect {entities/relationships} (or {relationships} with source_id/
  target_id keys), not ContextGraph.to_dict()'s {nodes, edges} shape.
  Map the output before passing it in, and add an actual temporally-
  bounded edge to the Temporal Intelligence example so the query has
  something to find.
- add_causal_relationship() only accepts relationship_type values of
  CAUSED, INFLUENCED, or PRECEDENT_FOR; replace the invented "triggers"/
  "enables" values used in Decision Intelligence and the audit-trail
  recipe, which would otherwise raise ValueError immediately.
This commit is contained in:
KaifAhmad1
2026-07-21 12:53:51 +05:30
parent 6cf5504585
commit 4119c21b6e
1 changed files with 35 additions and 8 deletions
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README.md
+35 -8
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@@ -206,9 +206,10 @@ rate_id = graph.record_decision(
confidence=0.99,
)
# Build the auditable causal chain
graph.add_causal_relationship(app_id, uw_id, relationship_type="triggers")
graph.add_causal_relationship(uw_id, rate_id, relationship_type="enables")
# Build the auditable causal chain - relationship_type must be one of
# CAUSED, INFLUENCED, or PRECEDENT_FOR
graph.add_causal_relationship(app_id, uw_id, relationship_type="CAUSED")
graph.add_causal_relationship(uw_id, rate_id, relationship_type="INFLUENCED")
# Query the intelligence
chain = graph.trace_decision_chain(rate_id)
@@ -277,14 +278,24 @@ d2 = graph.record_decision(
category="dosage_adjustment", scenario="INR monitoring plan for P-4821",
reasoning="Reduce warfarin dose per interaction severity; recheck INR in 5 days", outcome="dose_reduced_30pct", confidence=0.87,
)
graph.add_causal_relationship(d1, d2, relationship_type="triggers")
# relationship_type must be one of CAUSED, INFLUENCED, or PRECEDENT_FOR
graph.add_causal_relationship(d1, d2, relationship_type="CAUSED")
# Track provenance for every entity
prov.track_entity("patient_P4821", source="ehr/medication_orders_2024.json",
metadata={"extractor": "NamedEntityRecognizer"})
# Export W3C PROV-O for regulator submission
kg = graph.to_dict()
# Export W3C PROV-O for regulator submission - RDFExporter expects
# {"entities": [...], "relationships": [...]}, so map ContextGraph.to_dict()'s
# {"nodes": [...], "edges": [...]} shape onto it first
graph_dict = graph.to_dict()
kg = {
"entities": [{"id": n["id"], "type": n["type"], "text": n["content"]} for n in graph_dict["nodes"]],
"relationships": [
{"source_id": e["source"], "target_id": e["target"], "type": e["type"]}
for e in graph_dict["edges"]
],
}
RDFExporter().export(kg, "audit_trail.ttl", format="turtle")
```
@@ -811,6 +822,12 @@ graph = ContextGraph(advanced_analytics=True)
graph.add_node("alice_chen", "Person", role="VP Engineering")
graph.add_node("acme_corp", "Organization", valuation=1_200_000_000)
# A temporally-bounded edge - valid_from/valid_until define when it held true
graph.add_edge(
"alice_chen", "acme_corp", edge_type="works_for",
valid_from="2024-03-01T00:00:00", valid_until="2025-01-01T00:00:00",
)
# Point-in-time snapshots - replay history without reprocessing
snapshot_2023 = graph.state_at("2023-06-01")
snapshot_2024 = graph.state_at("2024-01-01")
@@ -823,10 +840,20 @@ fact = BiTemporalFact(
recorded_at=datetime(2024, 3, 5),
)
# Query facts valid within a time window
# Query facts valid within a time window - query_time_range() expects
# {"relationships": [...]} with source_id/target_id keys, which differs from
# ContextGraph.to_dict()'s {"nodes", "edges"} shape, so map it first
graph_dict = graph.to_dict()
kg_relationships = {
"relationships": [
{**e, "source_id": e["source"], "target_id": e["target"]}
for e in graph_dict["edges"]
]
}
tq = TemporalGraphQuery()
facts_in_window = tq.query_time_range(
graph.to_dict(), query="valid_facts", start_time="2024-01-01", end_time="2024-12-31"
kg_relationships, query="valid_facts", start_time="2024-01-01", end_time="2024-12-31"
)
# Normalize natural language temporal expressions - returns a (start, end) range