16e791e044
8월 7일까지 워킹트리에만 남아 있던 미커밋 작업을 커밋한다. 여러 사본 폴더(worktree·clone)에 흩어져 있던 중간 스냅샷을 정리하기 전에 원본을 git 이력으로 고정하는 것이 목적이다. - contracts/routes/services: measurement, outcome_trajectory, rupture_repair, deliberate_practice, calibration_transfer, supervision_research, multimodal_alliance, continuous_improvement 계열 신규 모듈과 테스트 - infra/db/init: 07~16 마이그레이션(측정 기반~calibration transfer 실행) - apps/web: 세션 리뷰 카드·관리 화면·E2E 스펙 추가 - docs/ops: G0~G8 라이브 통합·배포·롤백 증거 문서와 evidence JSON/PNG - scripts: smoke·ledger·릴리스 에이전트·NAS 프리뷰 운영 스크립트 engine.public 로그 .bak과 apps/web/test-results 산출물은 커밋에서 제외했다.
285 lines
10 KiB
Python
285 lines
10 KiB
Python
"""종료된 재연습 회기의 durable evaluator 근거를 G4 시도로 변환한다.
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이 adapter는 학습자의 자기평가나 브라우저가 보낸 성공/전이 플래그를 읽지
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않는다. 저장된 턴 UUID, 정규화된 fast-loop 라벨, 내담자 반응 라벨과 회기
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identity만 사용하며 원문은 결과 원장에 복제하지 않는다.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Iterable
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from uuid import UUID, uuid5
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from ..contracts.deliberate_practice import (
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CriterionObservation,
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PracticeAttemptObservation,
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PracticeEpisodeInput,
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PracticeEvidenceRef,
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PracticePrescription,
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ScenarioNovelty,
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)
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_OBSERVER_NAMESPACE = UUID("2d2df938-056c-56cc-86d2-99ad53bd3507")
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OBSERVER_VERSION = "practice-runtime-observer-v1"
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_POSITIVE_CLIENT_STATES = frozenset(
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{
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"affect_contact",
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"thought_organizing",
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"responds_to_exploration",
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"expresses_plan",
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"defense_loosening",
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}
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)
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_WITHDRAWN_CLIENT_STATES = frozenset(
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{"defensive", "involuntary", "affect_masking", "active_passivity"}
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)
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class RuntimePracticeObservationError(ValueError):
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"""durable 근거가 독립 판정에 충분하지 않을 때 fail-closed한다."""
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@dataclass(frozen=True, slots=True)
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class EvaluatedTurnPair:
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counselor_turn_id: UUID
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counselor_turn_seq: int
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client_turn_id: UUID | None
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client_turn_seq: int | None
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technique_codes: tuple[str, ...]
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client_state_codes: tuple[str, ...]
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appropriateness: str
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intent_deviation_dimensions: tuple[str, ...] = ()
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evaluator_error: str | None = None
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utterance_fingerprint: str | None = None
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has_voice_feature: bool = False
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def observation_model_run_id(
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*, prescription_id: str, practice_session_id: UUID, counselor_turn_id: UUID
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) -> UUID:
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return uuid5(
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_OBSERVER_NAMESPACE,
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f"{OBSERVER_VERSION}:{prescription_id}:{practice_session_id}:{counselor_turn_id}",
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)
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def _target_techniques(competency_id: str) -> frozenset[str]:
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key = competency_id.lower()
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if any(token in key for token in ("empathy", "empathic", "reflection")):
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return frozenset({"empathy", "reflection", "validation", "restatement"})
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if any(token in key for token in ("open_question", "open-question")):
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return frozenset({"facilitative_question", "exploration", "clarification"})
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if any(token in key for token in ("rupture", "repair", "impact")):
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return frozenset(
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{"opinion_check", "validation", "reflection", "here_and_now_focus"}
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)
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if any(token in key for token in ("goal", "collaborative", "reagreement")):
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return frozenset(
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{"consent_motivation_check", "opinion_check", "restatement"}
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)
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if any(token in key for token in ("presence", "response-space")):
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return frozenset({"holding", "reflection", "here_and_now_focus"})
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raise RuntimePracticeObservationError(
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f"unsupported runtime practice competency: {competency_id}"
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)
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def _novelty(
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*,
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source_case_id: UUID | None,
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source_persona_id: UUID | None,
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practice_case_id: UUID | None,
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practice_persona_id: UUID | None,
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) -> ScenarioNovelty:
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# identity가 불완전하면 전이를 낙관적으로 추론하지 않는다.
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if source_case_id is None or practice_case_id is None:
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return "familiar"
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if source_case_id == practice_case_id:
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return "familiar"
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if (
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source_persona_id is not None
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and practice_persona_id is not None
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and source_persona_id == practice_persona_id
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):
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return "familiar"
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return "unseen_transfer"
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def _client_response(states: Iterable[str]) -> str:
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normalized = frozenset(states)
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positive = bool(normalized & _POSITIVE_CLIENT_STATES)
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negative = bool(normalized & _WITHDRAWN_CLIENT_STATES)
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if "compliant_surface" in normalized:
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return "compliance_only"
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if positive and negative:
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return "mixed"
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if "expresses_plan" in normalized and positive:
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return "explicit_alignment"
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if positive:
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return "engaged"
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if negative:
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return "withdrawn"
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return "mixed"
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def derive_runtime_episode(
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*,
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prescription: PracticePrescription,
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practice_session_id: UUID,
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source_case_id: UUID | None,
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source_persona_id: UUID | None,
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practice_case_id: UUID | None,
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practice_persona_id: UUID | None,
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turn_pairs: Iterable[EvaluatedTurnPair],
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) -> PracticeEpisodeInput:
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targets = _target_techniques(prescription.competency_id)
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pairs = tuple(sorted(turn_pairs, key=lambda item: item.counselor_turn_seq))
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if not pairs:
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raise RuntimePracticeObservationError(
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"completed practice session has no durable counselor turn evidence"
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)
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novelty = _novelty(
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source_case_id=source_case_id,
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source_persona_id=source_persona_id,
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practice_case_id=practice_case_id,
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practice_persona_id=practice_persona_id,
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)
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scene_id = f"practice-session:{practice_session_id}"
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variant_id = f"runtime-session-{practice_session_id}"
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attempts: list[PracticeAttemptObservation] = []
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for sequence_no, pair in enumerate(pairs, start=1):
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model_run_id = observation_model_run_id(
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prescription_id=prescription.prescription_id,
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practice_session_id=practice_session_id,
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counselor_turn_id=pair.counselor_turn_id,
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)
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learner_ref = PracticeEvidenceRef(
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ref_id=str(pair.counselor_turn_id),
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scene_id=scene_id,
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turn_index=pair.counselor_turn_seq,
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actor="learner",
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kind="learner_behavior",
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)
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technique_match = bool(set(pair.technique_codes) & targets)
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target_deviation = any(
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token in prescription.competency_id.lower()
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or token in prescription.criterion_id.lower()
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for token in pair.intent_deviation_dimensions
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if token
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)
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counterevidence: list[str] = []
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if pair.client_turn_id is None or pair.client_turn_seq is None:
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criterion = CriterionObservation(
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criterion_id=prescription.criterion_id,
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status="error",
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source_kind="model_inferred",
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perspective="independent_observer",
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model_run_id=model_run_id,
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evidence_refs=(),
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counterevidence=(),
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uncertainty=1.0,
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error_code="client_response_turn_missing",
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)
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attempts.append(
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PracticeAttemptObservation(
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attempt_id=f"oas-g4-attempt-{practice_session_id.hex}-{sequence_no}",
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prescription_id=prescription.prescription_id,
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competency_id=prescription.competency_id,
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sequence_no=sequence_no,
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scenario_variant_id=variant_id,
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scenario_novelty=novelty,
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difficulty_level=prescription.activity.difficulty_level,
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criterion=criterion,
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evidence_refs=(learner_ref,),
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uncertainty=1.0,
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counterevidence=("client_response_turn_missing",),
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utterance_template_id=pair.utterance_fingerprint,
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error_code="client_response_turn_missing",
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)
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)
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continue
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response = _client_response(pair.client_state_codes)
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observed = (
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technique_match
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and pair.appropriateness == "pos"
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and not target_deviation
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and not pair.evaluator_error
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)
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if not technique_match:
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counterevidence.append("target_technique_not_observed")
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if pair.appropriateness != "pos":
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counterevidence.append("appropriateness_not_positive")
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if target_deviation:
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counterevidence.append("target_intent_deviation_observed")
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if pair.evaluator_error:
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counterevidence.append("turn_evaluation_error")
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if response not in {"engaged", "explicit_alignment"}:
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counterevidence.append("client_response_does_not_support_effect")
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client_ref = PracticeEvidenceRef(
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ref_id=str(pair.client_turn_id),
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scene_id=scene_id,
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turn_index=pair.client_turn_seq,
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actor="client",
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kind="client_response",
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)
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voice_refs: tuple[PracticeEvidenceRef, ...] = ()
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if pair.has_voice_feature:
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voice_refs = (
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PracticeEvidenceRef(
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ref_id=str(pair.counselor_turn_id),
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scene_id=scene_id,
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turn_index=pair.counselor_turn_seq,
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actor="runtime",
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kind="voice_feature",
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),
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)
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criterion = CriterionObservation(
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criterion_id=prescription.criterion_id,
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status="observed" if observed else "not_observed",
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source_kind="model_inferred",
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perspective="independent_observer",
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model_run_id=model_run_id,
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evidence_refs=(learner_ref,) if observed else (),
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counterevidence=tuple(counterevidence) if not observed else (),
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uncertainty=0.25 if observed else 0.4,
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)
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attempts.append(
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PracticeAttemptObservation(
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attempt_id=f"oas-g4-attempt-{practice_session_id.hex}-{sequence_no}",
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prescription_id=prescription.prescription_id,
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competency_id=prescription.competency_id,
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sequence_no=sequence_no,
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scenario_variant_id=variant_id,
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scenario_novelty=novelty,
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difficulty_level=prescription.activity.difficulty_level,
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criterion=criterion,
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client_response=response,
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evidence_refs=(
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(client_ref, *voice_refs)
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if observed
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else (learner_ref, client_ref, *voice_refs)
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),
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uncertainty=0.25 if observed else 0.4,
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counterevidence=tuple(counterevidence),
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utterance_template_id=pair.utterance_fingerprint,
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learner_claimed_success=False,
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)
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)
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return PracticeEpisodeInput(
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episode_id=f"oas-g4-episode-{practice_session_id.hex}",
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prescription_id=prescription.prescription_id,
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attempts=tuple(attempts),
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)
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__all__ = [
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"EvaluatedTurnPair",
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"OBSERVER_VERSION",
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"RuntimePracticeObservationError",
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"derive_runtime_episode",
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"observation_model_run_id",
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]
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