G0~G8 성과·동맹 측정 OS 작업 일괄 고정
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.
This commit is contained in:
Yun Chan
parent
93dd8f82d7
commit
16e791e044
390 changed files with 243188 additions and 499 deletions
272
apps/api/app/test_session_learning_producer.py
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272
apps/api/app/test_session_learning_producer.py
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from __future__ import annotations
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import unittest
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from typing import Any
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from unittest.mock import AsyncMock, patch
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from uuid import UUID, uuid4
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from .routes import calibration_transfer, sessions
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from .services import evaluator, session_learning_producer, state_machine
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from .services.persona import P1
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from .store import InProcSession, TurnRecord
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SESSION_ID = UUID("00000000-0000-0000-0000-00000000a401")
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LEARNER_ID = UUID("00000000-0000-0000-0000-00000000a402")
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COUNSELOR_TURN_ID = UUID("00000000-0000-0000-0000-00000000a403")
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CLIENT_TURN_ID = UUID("00000000-0000-0000-0000-00000000a404")
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class FakeProducerConnection:
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def __init__(self, *, locked_history: bool = False) -> None:
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self.locked_history = locked_history
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self.executed: list[tuple[str, tuple[Any, ...]]] = []
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self.evaluation = {
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"status": "ready",
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"scope": "session_end",
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"learner_id": LEARNER_ID,
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"payload": {
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"loop": "deep",
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"scope": "session_end",
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"intent_deviations": [
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{
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"dimension": "reflection",
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"expected": "정서를 반영하고 이해를 확인한다",
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"actual": "바로 다음 질문으로 이동했다",
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"severity": "moderate",
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}
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],
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},
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}
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async def fetchrow(self, query: str, *args: Any) -> dict[str, Any] | None:
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if "FROM app.session_evaluation" in query:
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return self.evaluation
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if "FROM app.competency_graph_snapshot" in query:
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return None
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raise AssertionError(f"unexpected fetchrow: {query}")
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async def fetch(self, query: str, *args: Any) -> list[dict[str, Any]]:
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if "FROM app.turns t" in query:
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return [
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{
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"turn_id": COUNSELOR_TURN_ID,
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"turn_seq": 3,
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"response_turn_id": CLIENT_TURN_ID,
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"response_turn_seq": 4,
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"intent_deviation": {
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"dimension": "공감적 반영",
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"expected": "정서를 반영하고 이해를 확인한다",
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"actual": "바로 다음 질문으로 이동했다",
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"severity": "moderate",
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},
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}
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]
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if "FROM app.calibration_prediction_history h" in query:
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if not self.locked_history:
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return []
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return [
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{
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"history_id": UUID("00000000-0000-0000-0000-00000000a405"),
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"competency_id": "competency.empathic_reflection",
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"locked_sequence": 2,
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}
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]
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raise AssertionError(f"unexpected fetch: {query}")
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async def execute(self, query: str, *args: Any) -> str:
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self.executed.append((query, args))
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return "INSERT 0 1"
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class SessionLearningProducerTests(unittest.IsolatedAsyncioTestCase):
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async def test_g4_uses_durable_turns_and_replays_stable_submission(self) -> None:
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conn = FakeProducerConnection()
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append = AsyncMock(
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side_effect=[
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{"submission_id": uuid4(), "idempotent_replay": False},
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{"submission_id": uuid4(), "idempotent_replay": True},
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]
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)
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with patch.object(
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session_learning_producer.deliberate_practice_store,
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"append_prescription_submission",
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append,
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):
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first = await session_learning_producer._produce_g4(
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conn, session_id=SESSION_ID
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)
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second = await session_learning_producer._produce_g4(
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conn, session_id=SESSION_ID
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)
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self.assertEqual(first["status"], "ready")
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self.assertEqual(second["status"], "ready")
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self.assertEqual(append.await_count, 2)
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first_call = append.await_args_list[0].kwargs
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second_call = append.await_args_list[1].kwargs
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self.assertEqual(first_call["submission_id"], second_call["submission_id"])
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self.assertEqual(first_call["coaching_cards"], second_call["coaching_cards"])
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self.assertEqual(
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tuple(first_call["evidence_turn_ids"]),
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(COUNSELOR_TURN_ID, CLIENT_TURN_ID),
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)
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card = first_call["coaching_cards"][0]
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self.assertEqual(
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tuple(item.ref_id for item in card.evidence_refs),
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(str(COUNSELOR_TURN_ID), str(CLIENT_TURN_ID)),
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)
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graph = first_call["graph"]
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self.assertEqual(len(graph.states), 4)
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self.assertTrue(all(state.band == "unassessed" for state in graph.states))
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self.assertTrue(all(state.attempt_count == 0 for state in graph.states))
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self.assertTrue(
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all(state.unseen_transfer_demonstrations == 0 for state in graph.states)
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)
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async def test_g5_does_not_reveal_before_prediction_lock(self) -> None:
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conn = FakeProducerConnection(locked_history=False)
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append = AsyncMock()
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with patch.object(
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session_learning_producer.calibration_transfer_store,
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"append_performance_observation",
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append,
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):
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result = await session_learning_producer._produce_g5(
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conn, session_id=SESSION_ID
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)
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self.assertEqual(
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result, {"status": "skipped", "reason": "locked_prediction_missing"}
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)
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append.assert_not_awaited()
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self.assertFalse(
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any("INSERT INTO audit.model_run" in query for query, _ in conn.executed)
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)
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async def test_g5_locked_history_gets_failed_observation_with_real_provenance(
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self,
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) -> None:
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conn = FakeProducerConnection(locked_history=True)
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append = AsyncMock(
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return_value={"observation_id": uuid4(), "idempotent_replay": False}
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)
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with patch.object(
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session_learning_producer.calibration_transfer_store,
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"append_performance_observation",
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append,
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):
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result = await session_learning_producer._produce_g5(
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conn, session_id=SESSION_ID
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)
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self.assertEqual(result["status"], "ready")
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self.assertTrue(
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any("INSERT INTO audit.model_run" in query for query, _ in conn.executed)
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)
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kwargs = append.await_args.kwargs
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self.assertEqual(kwargs["status"], "failed")
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self.assertEqual(kwargs["source_kind"], "model_inferred")
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self.assertEqual(kwargs["perspective"], "independent_observer")
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self.assertIsInstance(kwargs["model_run_id"], UUID)
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self.assertEqual(
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tuple(kwargs["evidence_turn_ids"]),
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(COUNSELOR_TURN_ID, CLIENT_TURN_ID),
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)
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self.assertEqual(kwargs["revealed_sequence"], 3)
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self.assertNotIn("master", repr(kwargs).lower())
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self.assertNotIn("transfer_verified", repr(kwargs))
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async def test_ready_evaluation_is_not_rolled_back_when_producer_fails(
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self,
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) -> None:
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state = state_machine.init_state(params=P1.openness_params())
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sess = InProcSession(
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session_id=str(SESSION_ID),
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case_id=str(uuid4()),
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learner_id=str(LEARNER_ID),
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persona_code=P1.code,
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theory_mode="humanistic",
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persona=P1,
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state=state,
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turns=[
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TurnRecord(
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turn_seq=1,
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speaker="counselor",
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stage=state.stage.value,
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text="상담자 발화",
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text_masked="상담자 발화",
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)
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],
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ended=True,
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)
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ready = evaluator.SessionEvaluation(
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session_id=str(SESSION_ID),
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stage=state.stage.value,
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scope="session_end",
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improvements=["정서를 반영한 뒤 이해를 확인한다."],
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)
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save = AsyncMock(return_value=True)
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notify = AsyncMock()
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with (
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patch.object(
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sessions.evaluator, "evaluate_session", AsyncMock(return_value=ready)
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),
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patch.object(sessions.session_persistence, "save_session_evaluation", save),
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patch.object(
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sessions.session_learning_producer,
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"produce_session_learning_artifacts",
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AsyncMock(side_effect=RuntimeError("producer offline")),
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),
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patch.object(
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sessions, "_enqueue_session_review_ready_notification", notify
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),
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):
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await sessions._generate_and_save_session_evaluation(sess)
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save.assert_awaited_once()
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self.assertEqual(save.await_args.args[0].status, "ready")
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notify.assert_awaited_once_with(str(SESSION_ID))
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async def test_prediction_lock_invokes_same_session_worker(self) -> None:
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history_id = uuid4()
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body = calibration_transfer.PredictionLockRequest(
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submission_id=uuid4(),
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lock_id=uuid4(),
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prediction_revision_id=uuid4(),
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locked_sequence=2,
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)
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payload = {
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"submission_id": body.submission_id,
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"history_id": history_id,
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"lock_id": body.lock_id,
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"idempotent_replay": False,
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}
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principal = calibration_transfer.Principal(
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user_id=str(LEARNER_ID),
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role=calibration_transfer.Role.LEARNER,
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cohort_ids=["g5-test"],
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)
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worker = AsyncMock(return_value={"g5": {"status": "ready"}})
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with (
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patch.object(
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calibration_transfer.calibration_transfer_store,
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"append_prediction_lock",
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AsyncMock(return_value=payload),
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),
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patch.object(
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calibration_transfer.session_learning_producer,
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"produce_locked_prediction_history",
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worker,
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),
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):
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response = await calibration_transfer.lock_prediction_history(
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history_id, body, principal
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)
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self.assertEqual(response.history_id, history_id)
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worker.assert_awaited_once_with(history_id)
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if __name__ == "__main__":
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unittest.main()
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