meakash77/veritasgraph-enterprise-graphrag-verifiable-ai-platform/reason

from veritasreason.reasoning.deductive_reasoner import DeductiveReasoner, Premise
reasoner = DeductiveReasoner()
# Add base facts to working memory
# Facts can be strings like "Person(John)" or structured dicts
import json
facts = json.loads(facts_json) if facts_json else []
reasoner.add_facts(facts)
# Apply logic with explicit premises
# Premise objects have: statement, confidence, source
premises = [
    Premise(statement=fact, confidence=1.0)
    for fact in facts
]
conclusions = reasoner.apply_logic(premises=premises)

proof = reasoner.prove_theorem(theorem=theorem_text)

from veritasreason.reasoning.deductive_reasoner import DeductiveReasoner
reasoner = DeductiveReasoner()
import json
reasoner.add_facts(json.loads(facts_json) if facts_json else [])
proof = reasoner.prove_theorem(theorem=theorem)

Theorem: "<theorem>"
Result:  PROVED ✓  |  DISPROVED ✗  |  UNDECIDABLE ⚠
 
Proof steps:
  1. <premise> — <justification>
  2. ...
  → QED: <theorem>
 
Confidence: <proof.confidence>

from veritasreason.reasoning.abductive_reasoner import (
    AbductiveReasoner, Observation
)
reasoner = AbductiveReasoner()
import json
if knowledge_json:
    reasoner.add_knowledge(json.loads(knowledge_json))
obs = Observation(description=observation)
# Generate all hypotheses then rank them
hypotheses = reasoner.generate_hypotheses(observations=[obs])
ranked = reasoner.rank_hypotheses(hypotheses)
best = reasoner.get_best_explanation(obs)
# Also get full explanations with evidence
explanations = reasoner.find_explanations(observations=[obs])

Abductive Reasoning for: "<observation>"
 
Best explanation:
  <best.description>  (confidence: 0.87)
 
All hypotheses (ranked):
  | Rank | Hypothesis | Confidence | Supporting Evidence |
  |  1   | <hyp>      | 0.87       | <evidence>         |
  |  2   | ...
 
Full explanations:
  Explanation 1: <explanation.summary>
    Evidence: <evidence items>

from veritasreason.reasoning.datalog_reasoner import DatalogReasoner
from veritasreason.context import ContextGraph
graph = ContextGraph()
reasoner = DatalogReasoner()
# program is a string of Datalog rules and queries
results = reasoner.evaluate(program=program, graph=graph)

from veritasreason.reasoning.sparql_reasoner import SPARQLReasoner
from veritasreason.context import ContextGraph
graph = ContextGraph()
reasoner = SPARQLReasoner()
results = reasoner.query(sparql_query=query, graph=graph)

from veritasreason.reasoning.rete_engine import ReteEngine
import json
engine = ReteEngine()
rules = rules_str.split("|") if rules_str else []
facts = json.loads(facts_json) if facts_json else []
engine.load_rules(rules)
engine.process_facts(facts)
activations = engine.get_activations()

from veritasreason.reasoning.abductive_reasoner import AbductiveReasoner, Observation
import json
reasoner = AbductiveReasoner()
if knowledge_json:
    reasoner.add_knowledge(json.loads(knowledge_json))
obs = Observation(description=scenario)
hypotheses = reasoner.generate_hypotheses(observations=[obs])
ranked = reasoner.rank_hypotheses(hypotheses)
top_n = ranked[:int(n) if n else 5]