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105 lines
2.8 KiB
Python
105 lines
2.8 KiB
Python
"""Op-emit guards: banned-phrase filter + budgets.
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Run at op-emit time (during the loop) so the model gets an observation
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back when an op is rejected and can rewrite. Today's pre-redesign code
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filtered banned phrases after the LLM call returned all ops at once,
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which meant rejection was a silent drop.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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import logging
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import re
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from typing import Iterable
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from deeptutor.services.memory.ops import Op
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logger = logging.getLogger(__name__)
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# L3 objectivity guard: phrases the LLM is prompt-banned from emitting.
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# Runtime enforces by dropping any L3 op whose text contains one of these
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# (outside of quoted user verbatim 「」 / "…"). Logged as a warning so we
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# can tune the list against real prompt regressions.
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BANNED_PHRASES: tuple[str, ...] = (
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# English absolutes
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"deeply",
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"truly",
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"mastered",
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"expert in",
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"passionate",
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"loves",
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"hates",
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"always",
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"never",
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"fully understands",
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# Chinese absolutes
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"深刻",
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"彻底",
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"完美掌握",
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"完美理解",
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"完全理解",
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"完全掌握",
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"专家",
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"热爱",
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"总是",
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"从来不",
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)
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# Per-loop budgets. Beyond these the dispatcher emits a hint observation
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# instead of executing the action; the prompt nudges the model to finish.
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@dataclass(frozen=True)
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class ToolBudgets:
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read_entity: int = 30
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search: int = 20
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list_pending: int = 50 # cheap nav, generous
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list_sections: int = 50
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recent_changes: int = 3
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add_entry: int = 12
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edit_entry: int = 12
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delete_entry: int = 12
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note: int = 8
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_QUOTED_RE = re.compile(r"「[^」]*」|\"[^\"]*\"")
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def _has_banned(text: str) -> bool:
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"""Return ``True`` iff a banned phrase appears outside every quote.
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Quoted regions (CJK 「…」 or ASCII "…") are stripped first, because
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the prompt allows verbatim user quotations to contain the otherwise-
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banned absolutes.
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"""
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stripped = _QUOTED_RE.sub("", text).lower()
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for phrase in BANNED_PHRASES:
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if phrase in stripped:
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return True
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return False
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def _op_text(op: Op) -> str:
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text = getattr(op, "text", "") or getattr(op, "new_text", "")
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return str(text)
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def _filter_banned(ops: Iterable[Op]) -> list[Op]:
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"""Drop ops whose text contains banned absolutist phrasing.
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Used post-loop as a safety net even though the per-op emit path
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already rejects them. Kept callable by name for legacy tests and
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the apply_ops_payload preview/apply round-trip.
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"""
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kept: list[Op] = []
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for op in ops:
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text = _op_text(op)
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if text and _has_banned(text):
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logger.warning(
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"memory consolidate: dropped op with banned phrase: %s",
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text[:80],
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)
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continue
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kept.append(op)
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return kept
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