smrti

# Salience-ranked retrieval
results = engine.recall("deployment risks")

for r in results:
    print(
        f"{r.atom.content}",
        f"salience={r.salience:.2f}",
        f"confidence={r.atom.truth.confidence:.2f}",
        f"valence={r.atom.valence.valence:.1f}",
    )

# KNN search → 1-hop graph expansion → salience scoring
# Negative-valence atoms surface first when relevant

# Assert a belief with supporting evidence
engine.believe(
    "Feature flags reduce deployment risk",
    probability=0.85,
    evidence="3 successful staged rollouts"
)

# Evidence is appended, not overwritten.
# Conflicting observations create contradiction links.
# The consolidation epoch merges via PLN.

# Run a consolidation epoch
epoch = engine.reflect()

print(epoch)
# EpochResult(
#   beliefs_updated=3,
#   atoms_decayed=12,
#   atoms_pruned=1,
#   lti_promoted=2,
#   new_connections=4,
#   contradictions_resolved=1
# )

# Process evidence → decay STI/LTI → promote high-LTI
# → resolve contradictions → prune low-salience atoms

# Soft-forget: recall matching atoms, decay confidence
targets = engine.recall("Friday deployment outage", top_k=5)

for r in targets:
    engine.db.execute(
        "UPDATE atoms SET confidence = confidence * 0.3 WHERE id = ?",
        (r.atom.id,)
    )

# No hard delete — the consolidation epoch
# prunes when salience drops low enough

# Switch preset at runtime
engine.set_personality("analytical")

# 6 presets: balanced, analytical, curious,
# empathetic, maverick, deterministic
# Each tunes 16 hyperparameters that control
# learning rate, decay, promotion, and salience weights