bibinprathap /veritasreason:decision
Full decision lifecycle management. Usage: /veritasreason:decision <sub-command> [args]
record <category> "<scenario>" "<reasoning>" <outcome> <confidence>
Record a decision with full context.
from veritasreason.context import AgentContext
ctx = AgentContext(decision_tracking=True)
decision_id = ctx.record_decision(
category=category, # "loan_approval", "deployment", "hiring"
scenario=scenario, # natural-language situation description
reasoning=reasoning, # why this decision was made
outcome=outcome, # "approved", "rejected", "deferred"
confidence=float(confidence),
entities=entities or [],
decision_maker="ai_agent",
valid_from=valid_from, # optional ISO date string
valid_until=valid_until,
)
Output: Decision <decision_id> recorded | <category> | <outcome> (conf: 0.95)
query "<question>" [--hops N] [--hybrid]
Query decisions using natural language with multi-hop graph traversal.
from veritasreason.context import AgentContext
ctx = AgentContext(decision_tracking=True, advanced_analytics=True)
results = ctx.query_decisions(
query=question,
max_hops=int(hops) if hops else 3,
include_context=True,
use_hybrid_search="--hybrid" in args,
)
For structured lookups use DecisionQuery:
from veritasreason.context.decision_query import DecisionQuery
dq = DecisionQuery(graph_store=ctx.graph_store)
# dq.find_by_category(category, limit=100)
# dq.find_by_entity(entity_id, limit=100)
# dq.find_by_time_range(start, end, limit=100)
# dq.multi_hop_reasoning(start_entity, query_context, max_hops=3)
# dq.trace_decision_path(decision_id, relationship_types)
# dq.analyze_decision_influence(decision_id, max_depth=3)
Return: | ID | Category | Scenario | Outcome | Confidence | Timestamp |
precedents "<scenario>" [--category <cat>] [--advanced] [--hops N] [--as-of <date>]
Find similar past decisions using hybrid semantic + structural + vector search.
from veritasreason.context import AgentContext
ctx = AgentContext(decision_tracking=True, kg_algorithms=True, vector_store_features=True)
if "--advanced" in args:
precedents = ctx.find_precedents_advanced(
scenario=scenario, category=category, limit=10,
use_kg_features=True,
similarity_weights={"semantic": 0.5, "structural": 0.3, "vector": 0.2},
)
else:
precedents = ctx.find_precedents(
scenario=scenario, category=category, limit=10,
use_hybrid_search=True,
max_hops=int(hops) if hops else 3,
include_context=True,
include_superseded=False,
as_of=as_of_date or None, # temporal filter: only precedents that existed as_of this date
)
Return ranked: | Rank | ID | Scenario | Outcome | Confidence | Similarity | Date |
influence <decision_id> [--depth N]
Analyze how a decision influences others across the graph.
from veritasreason.context import AgentContext
ctx = AgentContext(decision_tracking=True, advanced_analytics=True, kg_algorithms=True)
influence = ctx.analyze_decision_influence(decision_id, max_depth=int(depth) if depth else 3)
predictions = ctx.predict_decision_relationships(decision_id, top_k=5)
Output: Influence score + influenced decisions table + predicted new relationships.
explain <decision_id>
Full explainability trace � reasoning steps, causal antecedents, policy compliance.
from veritasreason.context import AgentContext, ContextGraph
ctx = AgentContext(decision_tracking=True)
explainability = ctx.trace_decision_explainability(decision_id)
graph = ContextGraph(advanced_analytics=True)
chain = graph.trace_decision_chain(decision_id, max_steps=5)
causality = graph.trace_decision_causality(decision_id, max_depth=5)
Output: Reasoning steps, causal antecedents, evidence items, policy compliance status.
insights
Comprehensive analytics across all tracked decisions.
from veritasreason.context import ContextGraph, AgentContext
ctx = AgentContext(decision_tracking=True, advanced_analytics=True)
graph = ContextGraph(advanced_analytics=True)
insights = graph.get_decision_insights()
summary = graph.get_decision_summary()
context_insights = ctx.get_context_insights()
Output: Total count, category breakdown, outcome distribution, avg confidence, top influential.
list [--category <cat>] [--entity <id>] [--from <date>] [--to <date>]
from veritasreason.context.decision_query import DecisionQuery
from veritasreason.context import AgentContext
from datetime import datetime
ctx = AgentContext(decision_tracking=True)
dq = DecisionQuery(graph_store=ctx.graph_store)
if category: decisions = dq.find_by_category(category, limit=100)
elif entity: decisions = dq.find_by_entity(entity, limit=100)
elif from_date: decisions = dq.find_by_time_range(
start=datetime.fromisoformat(from_date),
end=datetime.fromisoformat(to_date or "2099-12-31"),
)
Return: | ID | Category | Scenario | Outcome | Confidence | Maker | Timestamp |
exception <decision_id> <policy_id> "<reason>" --approver <name>
Record a formal policy exception.
from veritasreason.context.decision_recorder import DecisionRecorder
from veritasreason.context import AgentContext
ctx = AgentContext(decision_tracking=True)
recorder = DecisionRecorder(graph_store=ctx.graph_store)
exception_id = recorder.record_exception(
decision_id=decision_id, policy_id=policy_id,
reason=reason, approver=approver,
approval_method="manual_override", justification=reason,
)
from veritasreason.context.decision_query import DecisionQuery
dq = DecisionQuery(graph_store=ctx.graph_store)
similar = dq.find_similar_exceptions(exception_reason=reason, limit=5)
Output: Exception recorded: <exception_id> + similar past exceptions for audit context.