97971bd636
Introduces teaching/ module with three-stage correction pipeline: 1. correction.py — extracts CorrectionCandidate from correction intents, binding correction text to the prior turn it references 2. review.py — validates candidates: rejects identity overrides (17 marker patterns) and empty corrections; produces ReviewedTeachingExample with deterministic SHA-256 review hash 3. store.py — bounded FIFO store for accepted examples; emits PackMutationProposal objects instead of mutating the vocab manifold directly; retrievable by subject Design invariants: - Identity override attempts are rejected at the review gate - Pack mutations are proposal-only (applied=False by default) - All traces are deterministic: same input → same candidate_id and review_hash Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
"""Correction capture — extract a typed correction from dialogue context.
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A CorrectionCandidate binds the correction text to the prior turn it
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corrects, carrying both the intent classification and the prior turn's
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proposition for downstream review.
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"""
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from __future__ import annotations
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import hashlib
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import json
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from dataclasses import dataclass
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from generate.intent import DialogueIntent, IntentTag
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@dataclass(frozen=True, slots=True)
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class CorrectionCandidate:
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correction_text: str
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intent: DialogueIntent
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prior_surface: str
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prior_turn: int
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candidate_id: str
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def as_dict(self) -> dict[str, object]:
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return {
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"correction_text": self.correction_text,
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"intent_tag": self.intent.tag.value,
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"intent_subject": self.intent.subject,
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"prior_surface": self.prior_surface,
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"prior_turn": self.prior_turn,
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"candidate_id": self.candidate_id,
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}
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def _candidate_id(correction_text: str, prior_surface: str, prior_turn: int) -> str:
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payload = json.dumps(
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{"correction_text": correction_text, "prior_surface": prior_surface, "prior_turn": prior_turn},
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sort_keys=True,
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ensure_ascii=False,
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)
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return hashlib.sha256(payload.encode("utf-8")).hexdigest()[:16]
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def extract_correction(
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correction_text: str,
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intent: DialogueIntent,
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prior_surface: str,
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prior_turn: int,
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) -> CorrectionCandidate | None:
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"""Extract a correction candidate from a correction-tagged intent.
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Returns None if the intent is not a correction.
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"""
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if intent.tag is not IntentTag.CORRECTION:
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return None
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return CorrectionCandidate(
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correction_text=correction_text,
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intent=intent,
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prior_surface=prior_surface,
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prior_turn=prior_turn,
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candidate_id=_candidate_id(correction_text, prior_surface, prior_turn),
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)
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