mirror of
https://github.com/Heretek-AI/MuseEngine.git
synced 2026-11-07 10:56:06 -10:00
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
65 lines
2.8 KiB
Python
65 lines
2.8 KiB
Python
"""Wrap any agent function so it receives a MuseEngine scaffold first."""
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from __future__ import annotations
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import functools
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from typing import Any, Callable, Dict, List, Optional, Union
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from .engine import MuseEngine, Report
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Messages = List[Dict[str, Any]]
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class MuseMiddleware:
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"""Decorator for ``fn(prompt: str, ...)`` or ``fn(messages: list[dict], ...)``.
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For a string prompt, the scaffold is prepended. For a message list, a system
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message holding the scaffold is inserted at the front (or merged into an
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existing leading system message). The latest user message is what gets
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analyzed. The report from the last call is kept on ``last_report``.
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"""
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def __init__(self, engine: Optional[MuseEngine] = None, top_k: int = 3, return_report: bool = False):
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self.engine = engine or MuseEngine()
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self.top_k = top_k
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self.return_report = return_report
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self.last_report: Optional[Report] = None
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def __call__(self, agent_fn: Callable[..., Any]) -> Callable[..., Any]:
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@functools.wraps(agent_fn)
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def wrapped(prompt_or_messages: Union[str, Messages], *args: Any, **kwargs: Any) -> Any:
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if isinstance(prompt_or_messages, str):
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report = self.engine.imagine(prompt_or_messages, top_k=self.top_k)
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result = agent_fn(f"{report.scaffold()}\n\n{prompt_or_messages}", *args, **kwargs)
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elif isinstance(prompt_or_messages, list):
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user_text = _latest_user_text(prompt_or_messages)
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if not user_text:
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raise ValueError("MuseMiddleware needs at least one user message with text")
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report = self.engine.imagine(user_text, top_k=self.top_k)
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result = agent_fn(_inject_system(prompt_or_messages, report.scaffold()), *args, **kwargs)
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else:
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raise TypeError("expected a str prompt or a list of message dicts")
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self.last_report = report
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return {"response": result, "report": report} if self.return_report else result
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return wrapped
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def _latest_user_text(messages: Messages) -> str:
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for msg in reversed(messages):
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if msg.get("role") != "user":
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continue
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content = msg.get("content", "")
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if isinstance(content, str):
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return content
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if isinstance(content, list): # content blocks
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return " ".join(b.get("text", "") for b in content if isinstance(b, dict) and b.get("type") == "text")
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return ""
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def _inject_system(messages: Messages, scaffold: str) -> Messages:
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if messages and messages[0].get("role") == "system" and isinstance(messages[0].get("content"), str):
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first = dict(messages[0], content=f"{messages[0]['content']}\n\n{scaffold}")
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return [first, *messages[1:]]
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return [{"role": "system", "content": scaffold}, *messages]
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