// Explain VeritasReason reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale.
VeritasReason full-stack knowledge graph skill for context graphs, decision intelligence, explainability, extraction, reasoning, visualization, ontology, provenance, policy, and export workflows.
Analyze cause-and-effect relationships in the VeritasReason knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores.
Track and inspect graph changes, diffs, temporal updates, and the impact of new data on VeritasReason knowledge graphs.
Full decision lifecycle in VeritasReason � record, query, find precedents (hybrid/advanced), analyze influence, explain, insights dashboard, list, and record exceptions. Uses AgentContext, ContextGraph, DecisionQuery, CausalChainAnalyzer, DecisionRecorder.
Detect duplicate entities, duplicate groups, and relationship duplicates in VeritasReason using fuzzy matching, schema heuristics, and graph similarity.
Generate, inspect, and use node/text embeddings in VeritasReason — compute Node2Vec embeddings, find similar nodes, score link predictions, batch similarity, and pairwise similarity. Uses NodeEmbedder, SimilarityCalculator, LinkPredictor, and AgentContext. Sub-commands: compute, similar, similarity, predict-link, top-links, batch, pairwise.
| name | explain |
| description | Explain VeritasReason reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale. |
Produce explanations for decisions, rules, and graph analytics. Usage: /veritasreason:explain <target> [args]
$ARGUMENTS = explanation target + optional detail level.
decision <decision_id> [--detail <level>]Explain why a decision was reached.
from veritasreason.reasoning.explanation_generator import ExplanationGenerator
# For decision explainability in VeritasReason contexts:
decision_trace = ctx.trace_decision_explainability(decision_id=decision_id)
# For reasoning/proof explanations:
generator = ExplanationGenerator(detail_level=detail)
explanation = generator.generate_explanation(reasoning_result)
Output: decision factors, rule traces, confidence, and suggested next steps.
graph <node_id> [--path N]Explain graph relationships and why a node is connected.
# Use AgentContext explainability + causal tracing for graph-connected decisions
graph_explanation = ctx.trace_decision_explainability(decision_id=node_id)
upstream = ctx.get_causal_chain(decision_id=node_id, direction="upstream", max_depth=depth)
downstream = ctx.get_causal_chain(decision_id=node_id, direction="downstream", max_depth=depth)
Return: cause/effect chains, supporting evidence, and relevant metadata.