회기 무발화 0턴 분리, 자기예측 락 불변식 및 TDD 회귀 검증 완료

apps/api/app/services/calibration_transfer_store.py

@@ -2,7 +2,6 @@
 
 from __future__ import annotations
 
-import hashlib
 import json
 from collections.abc import Mapping, Sequence
 from datetime import UTC, datetime
@@ -26,6 +25,13 @@ from .calibration_transfer import (
     assess_synthetic_subgroup_drift,
     assess_transfer,
 )
+from .outcome_repository_values import (
+    canonical_hash as _canonical_hash,
+    created_role as _created_role,
+    public_row as _public_row,
+    value as _value,
+)
+from .practice_competency import target_techniques
 
 
 class CalibrationTransferNotFoundError(LookupError):
@@ -64,36 +70,6 @@ _POSITIVE_CLIENT_STATES = frozenset(
 )
 
 
-def _value(row: Mapping[str, Any], key: str, default: Any = None) -> Any:
-    try:
-        return row[key]
-    except (KeyError, TypeError):
-        return default
-
-
-def _public_row(row: Mapping[str, Any]) -> dict[str, Any]:
-    """API 응답에서 학습자 피드백 정책 판정 전용 열을 제거한다."""
-
-    payload = dict(row)
-    payload.pop("source_learner_feedback_enabled", None)
-    return payload
-
-
-def _canonical_hash(payload: Mapping[str, Any]) -> str:
-    serialized = json.dumps(
-        payload,
-        ensure_ascii=False,
-        separators=(",", ":"),
-        sort_keys=True,
-        default=str,
-    )
-    return hashlib.sha256(serialized.encode("utf-8")).hexdigest()
-
-
-def _created_role(principal: Principal) -> str:
-    return "instructor" if principal.role == Role.TEACHER else principal.role.value
-
-
 def _ensure_unique_evidence(
     evidence_turn_ids: Sequence[UUID], *, required: bool = False
 ) -> tuple[UUID, ...]:
@@ -182,6 +158,16 @@ async def append_prediction_revision(
     reason = revision_reason.strip()
     if not reason:
         raise CalibrationTransferStateError("revision_reason must not be blank")
+    if not (0.0 <= predicted_success_probability <= 1.0):
+        raise CalibrationTransferStateError(
+            "predicted_success_probability must be between 0.0 and 1.0"
+        )
+    if not (0.0 <= confidence <= 1.0):
+        raise CalibrationTransferStateError(
+            "confidence must be between 0.0 and 1.0"
+        )
+    if instrument_id == "vignette.calibration-self-prediction":
+        instrument_id = "calibration-mirror-g5"
     payload = {
         "prediction_revision_id": str(prediction_revision_id),
         "history_id": str(history_id),
@@ -318,6 +304,11 @@ async def append_prediction_revision(
             asyncpg.ForeignKeyViolationError,
             asyncpg.ObjectNotInPrerequisiteStateError,
         ) as exc:
+            message = str(exc)
+            if "lock" in message.lower() or "reveal" in message.lower():
+                raise CalibrationTransferStateError(
+                    f"self-prediction history is locked or revealed: {message}"
+                ) from exc
             raise CalibrationTransferStateError(
                 "prediction revision violated provenance or history invariants"
             ) from exc
@@ -880,21 +871,9 @@ async def append_transfer_suite(
 
 
 def _actual_target_techniques(competency_id: str) -> frozenset[str]:
-    key = competency_id.lower()
-    if any(token in key for token in ("empathy", "empathic", "reflection")):
-        return frozenset({"empathy", "reflection", "validation", "restatement"})
-    if any(token in key for token in ("open_question", "open-question")):
-        return frozenset({"facilitative_question", "exploration", "clarification"})
-    if any(token in key for token in ("rupture", "repair", "impact")):
-        return frozenset(
-            {"opinion_check", "validation", "reflection", "here_and_now_focus"}
-        )
-    if any(token in key for token in ("goal", "collaborative", "reagreement")):
-        return frozenset(
-            {"consent_motivation_check", "opinion_check", "restatement"}
-        )
-    if any(token in key for token in ("presence", "response-space")):
-        return frozenset({"holding", "reflection", "here_and_now_focus"})
+    targets = target_techniques(competency_id)
+    if targets is not None:
+        return targets
     raise CalibrationTransferStateError(
         f"unsupported actual transfer competency: {competency_id}"
     )

apps/api/app/services/continuous_improvement_store.py

@@ -2,8 +2,6 @@
 
 from __future__ import annotations
 
-import hashlib
-import json
 from collections.abc import Mapping, Sequence
 from typing import Any, Literal, Protocol
 from uuid import UUID, uuid5
@@ -26,6 +24,10 @@ from .continuous_improvement import (
     promote_content_to_catalog,
     release_allowed,
 )
+from .outcome_repository_values import (
+    canonical_hash as _canonical_hash,
+    value as _value,
+)
 
 
 DATA_CLASSIFICATION = "synthetic_replay_red_team_coverage_drift"
@@ -101,24 +103,6 @@ class RollbackExecutor(Protocol):
     ) -> RollbackExecutionReceipt: ...
 
 
-def _value(row: Mapping[str, Any], key: str, default: Any = None) -> Any:
-    try:
-        return row[key]
-    except (KeyError, TypeError):
-        return default
-
-
-def _canonical_hash(payload: Any) -> str:
-    serialized = json.dumps(
-        payload,
-        ensure_ascii=False,
-        separators=(",", ":"),
-        sort_keys=True,
-        default=str,
-    )
-    return hashlib.sha256(serialized.encode("utf-8")).hexdigest()
-
-
 async def _begin_submission(
     conn: asyncpg.Connection,
     *,

apps/api/app/services/deliberate_practice_store.py

@@ -7,8 +7,6 @@ separate superseding events and never mutate the original evidence or graph.
 
 from __future__ import annotations
 
-import hashlib
-import json
 from collections.abc import Mapping, Sequence
 from typing import Any
 from uuid import UUID, uuid5
@@ -39,6 +37,12 @@ from .practice_runtime_observer import (
     derive_runtime_episode,
     observation_model_run_id,
 )
+from .outcome_repository_values import (
+    canonical_hash as _canonical_hash,
+    created_role as _created_role,
+    public_row as _public_row,
+    value as _value,
+)
 
 
 _RUNTIME_ATTEMPT_NAMESPACE = UUID("52e24f06-34be-54cb-9092-5122e384c814")
@@ -60,36 +64,6 @@ class DeliberatePracticeFeedbackDisabledError(PermissionError):
     """처방/연습 원천 회기의 학습자 피드백 스냅샷이 비활성이다."""
 
 
-def _value(row: Mapping[str, Any], key: str, default: Any = None) -> Any:
-    try:
-        return row[key]
-    except (KeyError, TypeError):
-        return default
-
-
-def _public_row(row: Mapping[str, Any]) -> dict[str, Any]:
-    """API 응답에서 정책 판정 전용 내부 열을 제거한다."""
-
-    payload = dict(row)
-    payload.pop("source_learner_feedback_enabled", None)
-    return payload
-
-
-def _canonical_hash(payload: Mapping[str, Any]) -> str:
-    serialized = json.dumps(
-        payload,
-        ensure_ascii=False,
-        separators=(",", ":"),
-        sort_keys=True,
-        default=str,
-    )
-    return hashlib.sha256(serialized.encode("utf-8")).hexdigest()
-
-
-def _created_role(principal: Principal) -> str:
-    return "instructor" if principal.role == Role.TEACHER else principal.role.value
-
-
 def _ensure_unique_evidence(
     evidence_turn_ids: Sequence[UUID], *, required: bool = True
 ) -> tuple[UUID, ...]:

apps/api/app/services/outcome_repository_values.py

@@ -0,0 +1,40 @@
+"""결과 저장소가 공유하는 순수 값 정규화 도우미."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from collections.abc import Mapping
+from typing import Any
+
+from ..deps import Principal, Role
+
+
+def value(row: Mapping[str, Any], key: str, default: Any = None) -> Any:
+    try:
+        return row[key]
+    except (KeyError, TypeError):
+        return default
+
+
+def canonical_hash(payload: Any) -> str:
+    serialized = json.dumps(
+        payload,
+        ensure_ascii=False,
+        separators=(",", ":"),
+        sort_keys=True,
+        default=str,
+    )
+    return hashlib.sha256(serialized.encode("utf-8")).hexdigest()
+
+
+def public_row(row: Mapping[str, Any]) -> dict[str, Any]:
+    """API 응답에서 학습자 피드백 정책 판정 전용 열을 제거한다."""
+
+    payload = dict(row)
+    payload.pop("source_learner_feedback_enabled", None)
+    return payload
+
+
+def created_role(principal: Principal) -> str:
+    return "instructor" if principal.role == Role.TEACHER else principal.role.value

apps/api/app/services/practice_competency.py

@@ -0,0 +1,22 @@
+"""실제 연습과 전이 평가가 공유하는 competency 분류."""
+
+from __future__ import annotations
+
+
+def target_techniques(competency_id: str) -> frozenset[str] | None:
+    key = competency_id.lower()
+    if any(token in key for token in ("empathy", "empathic", "reflection")):
+        return frozenset({"empathy", "reflection", "validation", "restatement"})
+    if any(token in key for token in ("open_question", "open-question")):
+        return frozenset({"facilitative_question", "exploration", "clarification"})
+    if any(token in key for token in ("rupture", "repair", "impact")):
+        return frozenset(
+            {"opinion_check", "validation", "reflection", "here_and_now_focus"}
+        )
+    if any(token in key for token in ("goal", "collaborative", "reagreement")):
+        return frozenset(
+            {"consent_motivation_check", "opinion_check", "restatement"}
+        )
+    if any(token in key for token in ("presence", "response-space")):
+        return frozenset({"holding", "reflection", "here_and_now_focus"})
+    return None

apps/api/app/services/practice_runtime_observer.py

@@ -19,6 +19,7 @@ from ..contracts.deliberate_practice import (
     PracticePrescription,
     ScenarioNovelty,
 )
+from .practice_competency import target_techniques
 
 
 _OBSERVER_NAMESPACE = UUID("2d2df938-056c-56cc-86d2-99ad53bd3507")
@@ -67,21 +68,9 @@ def observation_model_run_id(
 
 
 def _target_techniques(competency_id: str) -> frozenset[str]:
-    key = competency_id.lower()
-    if any(token in key for token in ("empathy", "empathic", "reflection")):
-        return frozenset({"empathy", "reflection", "validation", "restatement"})
-    if any(token in key for token in ("open_question", "open-question")):
-        return frozenset({"facilitative_question", "exploration", "clarification"})
-    if any(token in key for token in ("rupture", "repair", "impact")):
-        return frozenset(
-            {"opinion_check", "validation", "reflection", "here_and_now_focus"}
-        )
-    if any(token in key for token in ("goal", "collaborative", "reagreement")):
-        return frozenset(
-            {"consent_motivation_check", "opinion_check", "restatement"}
-        )
-    if any(token in key for token in ("presence", "response-space")):
-        return frozenset({"holding", "reflection", "here_and_now_focus"})
+    targets = target_techniques(competency_id)
+    if targets is not None:
+        return targets
     raise RuntimePracticeObservationError(
         f"unsupported runtime practice competency: {competency_id}"
     )

apps/api/app/services/session_metrics.py

@@ -213,7 +213,12 @@ def build_learner_growth(
                 learner_id=learner_id,
                 learner_label=learner_label(learner_id),
                 sessions=len(ordered),
-                ended_sessions=sum(1 for sess in ordered if sess.ended),
+                ended_sessions=sum(
+                    1
+                    for sess in ordered
+                    if sess.ended
+                    and any(t.speaker in ("counselor", "learner") for t in sess.turns)
+                ),
                 latest_at=iso_datetime(session_activity_time(latest_session)) or "",
                 first_score=first_score,
                 latest_score=latest_score,

apps/api/app/services/supervision_research_store.py

@@ -2,8 +2,6 @@
 
 from __future__ import annotations
 
-import hashlib
-import json
 from collections.abc import Mapping, Sequence
 from typing import Any
 from uuid import UUID, uuid5
@@ -23,6 +21,10 @@ from .supervision_research import (
     build_phase3_outcome_manifest,
     compare_evaluation_versions,
 )
+from .outcome_repository_values import (
+    canonical_hash as _canonical_hash,
+    value as _value,
+)
 
 
 _POINTER_NAMESPACE = UUID("f99f95ba-365e-46ea-a613-2239275f8a2d")
@@ -40,24 +42,6 @@ class SupervisionResearchNotFoundError(SupervisionResearchError):
     """Required source evidence or a visible aggregate does not exist."""
 
 
-def _value(row: Mapping[str, Any], key: str, default: Any = None) -> Any:
-    try:
-        return row[key]
-    except (KeyError, TypeError):
-        return default
-
-
-def _canonical_hash(payload: Any) -> str:
-    serialized = json.dumps(
-        payload,
-        ensure_ascii=False,
-        separators=(",", ":"),
-        sort_keys=True,
-        default=str,
-    )
-    return hashlib.sha256(serialized.encode("utf-8")).hexdigest()
-
-
 async def _existing_submission(
     conn: asyncpg.Connection,
     *,

apps/api/app/services/voice.py

@@ -193,6 +193,29 @@ class StreamingTranscriptEvent:
     confidence: float | None = None
 
 
+def _streaming_provider_event(
+    provider: str,
+    transcript_event: StreamingTranscriptEvent,
+    *,
+    start: float,
+    duration: float,
+) -> dict[str, object]:
+    event_type = "speech_final" if transcript_event.speech_final else (
+        "speech_end" if transcript_event.final else "voice_activity"
+    )
+    provider_event: dict[str, object] = {
+        "type": event_type,
+        "provider": provider,
+        "source": "streaming_stt",
+        "start_ms": round(start * 1000),
+        "duration_ms": round(duration * 1000),
+        "is_final": transcript_event.final,
+    }
+    if transcript_event.confidence is not None:
+        provider_event["confidence"] = transcript_event.confidence
+    return provider_event
+
+
 class DeepgramStreamingSession:
     """One Deepgram Listen WebSocket, scoped to exactly one learner utterance."""
 
@@ -364,29 +387,21 @@ class DeepgramStreamingSession:
         if not display_text and not speech_final:
             return
 
-        event_type = "speech_final" if speech_final else (
-            "speech_end" if is_final else "voice_activity"
+        transcript_event = StreamingTranscriptEvent(
+            text=display_text,
+            final=is_final,
+            speech_final=speech_final,
+            confidence=confidence,
+        )
+        provider_event = _streaming_provider_event(
+            "deepgram",
+            transcript_event,
+            start=start,
+            duration=duration,
         )
-        provider_event: dict[str, object] = {
-            "type": event_type,
-            "provider": "deepgram",
-            "source": "streaming_stt",
-            "start_ms": round(start * 1000),
-            "duration_ms": round(duration * 1000),
-            "is_final": is_final,
-        }
-        if confidence is not None:
-            provider_event["confidence"] = confidence
         if is_final or speech_final:
             self._provider_events.append(provider_event)
-        await self._on_event(
-            StreamingTranscriptEvent(
-                text=display_text,
-                final=is_final,
-                speech_final=speech_final,
-                confidence=confidence,
-            )
-        )
+        await self._on_event(transcript_event)
 
     def _consume_final_words(self, value: object) -> None:
         if not isinstance(value, list):
@@ -620,29 +635,21 @@ class LocalWhisperStreamingSession:
         if not display_text and not speech_final:
             return
 
-        event_type = "speech_final" if speech_final else (
-            "speech_end" if is_final else "voice_activity"
+        transcript_event = StreamingTranscriptEvent(
+            text=display_text,
+            final=is_final,
+            speech_final=speech_final,
+            confidence=confidence,
+        )
+        provider_event = _streaming_provider_event(
+            "local_whisper",
+            transcript_event,
+            start=start,
+            duration=duration,
         )
-        provider_event: dict[str, object] = {
-            "type": event_type,
-            "provider": "local_whisper",
-            "source": "streaming_stt",
-            "start_ms": round(start * 1000),
-            "duration_ms": round(duration * 1000),
-            "is_final": is_final,
-        }
-        if confidence is not None:
-            provider_event["confidence"] = confidence
         if is_final or speech_final:
             self._provider_events.append(provider_event)
-        await self._on_event(
-            StreamingTranscriptEvent(
-                text=display_text,
-                final=is_final,
-                speech_final=speech_final,
-                confidence=confidence,
-            )
-        )
+        await self._on_event(transcript_event)
 
     def _consume_final_words(self, value: object, *, offset: float) -> None:
         if not isinstance(value, list):