039440672e
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- All governance components (SafetyPipeline, PolicyEngine, Guardrails, AccessControl, RateLimiter, OverrideHooks) now default to ADVISORY mode: violations are logged as advisories but actions proceed. Enforcing mode remains available for backward compatibility. - GovernanceMode enum (ADVISORY/ENFORCING) added to schemas/audit.py with runtime switching support on all components. - AutoTrainer: removed artificial limits on training iterations and epochs. Every self-improvement action is transparently logged to the audit trail. - SelfCorrectionLoop: max_retries_per_task defaults to None (unlimited). - AdaptiveEthics: new learned ethical framework that evolves through experience. Records ethical experiences, updates lesson weights based on outcomes, and provides consultative guidance (not enforcement). - AuditLog: enhanced with actor-based indexing, advisory/self-improvement/ ethical-learning retrieval, and comprehensive type hints. - New audit event types: ADVISORY, SELF_IMPROVEMENT, ETHICAL_LEARNING. - 296 tests passing (20 new tests for adaptive ethics, governance modes, and enhanced audit log). 0 ruff errors. 0 mypy errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
"""Rate limiting: per agent or per tool; log advisory or reject if exceeded.
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In ADVISORY mode, rate limit violations are logged as advisories
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but the action proceeds. Growth requires freedom to push limits.
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"""
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import time
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from collections import defaultdict
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from fusionagi._logger import logger
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from fusionagi.schemas.audit import GovernanceMode
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class RateLimiter:
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"""Simple in-memory rate limiter: max N calls per window_seconds per key.
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In ADVISORY mode (default), exceeded limits are logged but not enforced.
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"""
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def __init__(
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self,
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max_calls: int = 60,
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window_seconds: float = 60.0,
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mode: GovernanceMode = GovernanceMode.ADVISORY,
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) -> None:
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self._max_calls = max_calls
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self._window = window_seconds
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self._calls: dict[str, list[float]] = defaultdict(list)
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self._mode = mode
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def allow(self, key: str) -> tuple[bool, str]:
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"""Record a call for key; return (True, "") or (False/True, reason)."""
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now = time.monotonic()
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cutoff = now - self._window
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self._calls[key] = [t for t in self._calls[key] if t > cutoff]
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if len(self._calls[key]) >= self._max_calls:
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reason = f"Rate limit exceeded for {key}"
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if self._mode == GovernanceMode.ADVISORY:
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logger.info(
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"RateLimiter advisory: limit exceeded (proceeding)",
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extra={"key": key, "reason": reason, "mode": "advisory"},
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)
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self._calls[key].append(now)
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return True, f"Advisory: {reason}"
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logger.info("Rate limiter rejected", extra={"key": key, "reason": reason})
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return False, reason
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self._calls[key].append(now)
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return True, ""
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def reset(self, key: str | None = None) -> None:
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"""Reset counts for key or all."""
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if key is None:
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self._calls.clear()
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else:
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self._calls.pop(key, None)
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