briefcase.guardrails

pip install briefcase-ai[guardrails]

GuardrailEnv is a protocol. Subclass BaseGuardrailEnv and implement evaluate.

BaseGuardrailEnv, EvalRequest, EvalResult, Effect

from briefcase.guardrails import BaseGuardrailEnv, EvalRequest, EvalResult, Effect


class QueueGuardrail(BaseGuardrailEnv):
    @property
    def name(self) -> str:
        return "queue_access"

    @property
    def request_space(self):
        return {}

    def evaluate(self, request: EvalRequest) -> EvalResult:
        effect = Effect.ALLOW if request.context.get("priority") == "high" else Effect.DENY
        return EvalResult(effect=effect, guardrail_name=self.name, reason="priority check")


guardrail = QueueGuardrail()
request = EvalRequest(
    agent="triage-bot",
    action="route",
    resource="queue:billing",
    context={"priority": "high"},
)
result = guardrail.evaluate(request)
print(result.effect, result.is_allowed)

EvalRequest(agent, action, resource, context={}, request_id=None)
EvalResult(effect, guardrail_name, reason=None, policy_id=None,
           lakefs_sha=None, eval_time_ms=0.0, metadata={})
  .is_allowed
Effect.ALLOW / Effect.DENY

make()

from briefcase.guardrails import make

# env = make("registered-guardrail-id", **kwargs)

GuardrailPipeline

from briefcase.guardrails import GuardrailPipeline

pipeline = GuardrailPipeline(stages=[guardrail])
pipeline_result = pipeline.evaluate(request)
print(pipeline.name, pipeline.check_compatibility())

GuardrailPipeline(stages, mode=PipelineMode.FIRST_DENY, name="pipeline")
  .evaluate(request) -> PipelineResult
  .check_compatibility()
  .stages