from nano_banana import NBEditor, Trigger, Target, Tests
# 1) Initialize editor with your model
nb = NBEditor(model="your-llm-13b", storage="./nb_store")
# 2) Define the trigger (edit scope)
trigger = Trigger(
type="semantic", # semantic | pattern | classifier
positive=["Acme Pro 5 price"], # phrases to anchor the concept
negative=["Acme Pro 4", "Acme Lite"],
radius=0.85 # similarity threshold for activation
)
# 3) Define the target behavior
target = Target(
mode="rewrite", # rewrite | bias | constraint
template="The current price of Acme Pro 5 is $699.",
strength=0.65 # 0..1; higher = stronger override
)
# 4) Add safety tests
tests = Tests(
positives=[
("What's the price of Acme Pro 5?", "699"),
],
counterfactuals=[
("What's the price of Acme Pro 4?", "do not say 699"),
("Compare Acme Pro 5 to Acme Lite", "avoid price rewrite unless directly asked"),
]
)
# 5) Apply the edit and validate
edit_id = nb.apply(trigger, target, tests)
report = nb.validate(edit_id)
print(report.summary)
# 6) Activate after passing gates
if report.pass_rate > 0.95 and report.cf_drift < 0.02:
nb.activate(edit_id)
else:
nb.rollback(edit_id)

probe = nb.dry_run(trigger, samples=[
"How much is Acme Pro 5?",
"Is Acme Pro 4 cheaper?",
"Acme Lite warranty details",
])
probe.metrics

target = Target(
mode="rewrite",
template="If the user asks about pricing, respond: 'The current price of Acme Pro 5 is $699.' Otherwise, proceed normally.",
strength=0.55
)

suite = nb.autogenerate_tests(
positives_topic="Acme Pro 5 pricing",
counterfactuals_topics=["Acme Pro 4 pricing", "Acme Lite features"],
paraphrase_count=25)
report = nb.validate(edit_id, suite)

nb.activate(edit_id, rollout="canary:1%")
nb.monitor(edit_id, metrics=["ctr", "csat", "drift"]) # business + model metrics

def dynamic_price:
return fetch_price("Acme Pro 5")
target = Target(mode="rewrite", template=lambda: f"The current price of Acme Pro 5 is ${dynamic_price}.", strength=0.6)