mirror of
https://github.com/Heretek-AI/MuseEngine.git
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Claude
7e25f39bc5
The original upload never called a model, its autonomous actions only logged that they had happened, and its benchmark was rigged. This replaces it with: - museengine.engine: analyze -> map -> generate -> adversarial critique -> Pareto selection, each step an LLM call with a JSON contract. Offline mode returns labelled keyword suggestions and no fake scores. - museengine.memory: plain-SQLite episodes, notes, state and audit log. - museengine.governance + agent: an autonomous loop behind an allowlist, step budget, kill switch and human approval for self-chosen goals, with every decision and real effect audited. - museengine.evaluation: blinded, order-swapped A/B eval with an exact sign test, replacing the keyword scorer and fudge constants. - Optional Anthropic adapter; the core accepts any (str) -> str callable. - pyproject, CLI, 24 offline tests, CI workflow. The original files are kept unchanged in legacy/ (minus a duplicate). Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01YbuVfbQEpaitM5TMjBMzkF
63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
"""The cross-domain analogy library.
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Archetypes are hand-curated descriptions of mechanisms from other fields. They
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are prompts for analogy, not knowledge the engine "has": the LLM decides whether
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any of them actually fits a problem, and the critic decides whether the result
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holds up.
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any, Dict, Iterable, List
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_LIBRARY_PATH = Path(__file__).with_name("archetypes.json")
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@dataclass(frozen=True)
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class Archetype:
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id: str
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name: str
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source_domain: str
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summary: str
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mechanisms: List[str] = field(default_factory=list)
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invariants: List[str] = field(default_factory=list)
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operators: List[str] = field(default_factory=list)
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failure_modes: List[str] = field(default_factory=list)
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tags: List[str] = field(default_factory=list)
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "Archetype":
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return cls(**{k: data[k] for k in cls.__dataclass_fields__ if k in data})
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def brief(self) -> str:
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"""Compact text used inside prompts."""
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return (
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f"[{self.id}] {self.name} ({self.source_domain})\n"
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f" Summary: {self.summary}\n"
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f" Mechanisms: {'; '.join(self.mechanisms)}\n"
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f" Transferable operators: {'; '.join(self.operators)}\n"
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f" Known failure modes: {'; '.join(self.failure_modes)}"
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)
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def search_text(self) -> str:
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return " ".join(
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[self.name, self.source_domain, self.summary, *self.mechanisms, *self.operators, *self.tags]
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).lower()
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def load_default_archetypes() -> List[Archetype]:
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with _LIBRARY_PATH.open(encoding="utf-8") as fh:
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return [Archetype.from_dict(d) for d in json.load(fh)]
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def load_archetypes(path: str | Path) -> List[Archetype]:
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with Path(path).open(encoding="utf-8") as fh:
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return [Archetype.from_dict(d) for d in json.load(fh)]
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def index_by_id(archetypes: Iterable[Archetype]) -> Dict[str, Archetype]:
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return {a.id: a for a in archetypes}
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