b982e31c19
18 changes implementing full advisory philosophy: 1. Safety Head prompt: prevention mandate → advisory observation 2. Native Reasoning: Safety claims conditional on actual risk signals 3. File Tool: path scope advisory (log + proceed) 4. HTTP Tool: SSRF protection advisory (log + proceed) 5. File Size Cap: configurable (default unlimited) 6. PII Detection: integrated with AdaptiveEthics 7. Embodiment: force limit advisory (log, don't clamp) 8. Embodiment: workspace bounds advisory (log, don't reject) 9. API Rate Limiter: advisory (log, don't hard 429) 10. MAA Gate: GovernanceMode.ADVISORY default 11. Physics Authority: safety factor advisory, not hard reject 12. Self-Model: evolve_value() for experience-based value evolution 13. Ethical Lesson: weight unclamped for full dynamic range 14. ConsequenceEngine: adaptive risk_memory_window 15. Cross-Head Learning: shared InsightBus between heads 16. World Model: self-modification prediction 17. Persistent memory: file-backed learning store 18. Plugin Heads: ethics/consequence hooks in HeadAgent + HeadRegistry 429 tests passing, 0 ruff errors, 0 new mypy errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
130 lines
4.0 KiB
Python
130 lines
4.0 KiB
Python
"""Cross-head insight bus — shared learning channel between heads.
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Heads can publish observations (insights) to the bus, and other heads
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can subscribe to learn from them. This enables the Safety head to
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learn from Logic's contradiction detections, Research's source quality
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assessments, and so on — breaking the head-isolation barrier.
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Usage:
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bus = InsightBus()
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bus.publish("logic", Insight(source="logic", message="Contradiction found", ...))
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recent = bus.get_insights(subscriber="safety", limit=10)
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"""
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from __future__ import annotations
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import time
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from dataclasses import dataclass, field
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from typing import Any
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from fusionagi._logger import logger
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@dataclass
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class Insight:
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"""A single observation published by a head."""
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source: str
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message: str
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domain: str = ""
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confidence: float = 0.5
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metadata: dict[str, Any] = field(default_factory=dict)
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timestamp: float = field(default_factory=time.monotonic)
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class InsightBus:
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"""Shared bus for cross-head learning.
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Heads publish observations; other heads consume them to enrich
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their own reasoning. The bus maintains a rolling window of
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insights and supports filtered retrieval.
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Args:
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max_insights: Maximum insights retained (oldest dropped first).
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"""
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def __init__(self, max_insights: int = 1000) -> None:
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self._insights: list[Insight] = []
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self._max = max_insights
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self._subscribers: dict[str, list[str]] = {}
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def publish(self, publisher: str, insight: Insight) -> None:
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"""Publish an insight from a head.
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Args:
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publisher: Head ID of the publisher.
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insight: The observation to share.
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"""
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self._insights.append(insight)
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if len(self._insights) > self._max:
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self._insights = self._insights[-self._max:]
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logger.debug(
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"InsightBus: insight published",
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extra={
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"publisher": publisher,
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"domain": insight.domain,
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"message": insight.message[:80],
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},
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)
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def subscribe(self, subscriber: str, domains: list[str] | None = None) -> None:
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"""Register a head's interest in certain domains.
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Args:
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subscriber: Head ID subscribing.
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domains: Domains of interest (None = all).
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"""
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self._subscribers[subscriber] = domains or []
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def get_insights(
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self,
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subscriber: str | None = None,
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domain: str | None = None,
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limit: int = 20,
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since: float | None = None,
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) -> list[Insight]:
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"""Retrieve recent insights, optionally filtered.
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Args:
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subscriber: If given, filter by subscriber's registered domains.
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domain: Explicit domain filter.
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limit: Max results.
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since: Only insights after this timestamp.
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Returns:
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List of matching insights, most recent first.
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"""
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results = self._insights
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if since is not None:
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results = [i for i in results if i.timestamp >= since]
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if domain:
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results = [i for i in results if i.domain == domain]
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elif subscriber and subscriber in self._subscribers:
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domains = self._subscribers[subscriber]
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if domains:
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results = [i for i in results if i.domain in domains]
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return list(reversed(results[-limit:]))
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def get_summary(self) -> dict[str, Any]:
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"""Return bus statistics."""
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by_source: dict[str, int] = {}
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by_domain: dict[str, int] = {}
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for i in self._insights:
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by_source[i.source] = by_source.get(i.source, 0) + 1
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if i.domain:
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by_domain[i.domain] = by_domain.get(i.domain, 0) + 1
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return {
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"total_insights": len(self._insights),
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"subscribers": list(self._subscribers.keys()),
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"by_source": by_source,
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"by_domain": by_domain,
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}
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__all__ = ["Insight", "InsightBus"]
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