현재 작업 상태 저장
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Yun Chan
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674 changed files with 8726 additions and 298 deletions
247
apps/api/app/test_evaluation_persistence.py
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247
apps/api/app/test_evaluation_persistence.py
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"""Regression tests for turn-evaluation persistence mapping."""
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from __future__ import annotations
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from pathlib import Path
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import unittest
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from unittest.mock import patch
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from .deps import Principal, Role
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from . import session_persistence
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from .routes import sessions
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from .services import persona as persona_service
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from .services import state_machine
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from .store import InProcSession
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class FakeEvaluationConn:
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def __init__(self) -> None:
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self.executed: list[tuple[str, tuple[object, ...]]] = []
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self.fetchvals: list[tuple[str, tuple[object, ...]]] = []
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async def execute(self, query: str, *args: object) -> str:
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self.executed.append((query, args))
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return "INSERT 0 1"
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async def fetchval(self, query: str, *args: object) -> int:
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self.fetchvals.append((query, args))
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if "app.technique_label_def" in query:
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return 101
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if "app.client_state_def" in query:
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return 202
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raise AssertionError(f"unexpected fetchval query: {query}")
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class EvaluationPersistenceMappingTest(unittest.TestCase):
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def test_feedback_rows_preserve_review_scalar_contract(self) -> None:
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evaluation = {
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"loop": "fast",
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"turn_seq": 2,
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"stage": "탐색",
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"appropriateness": "pos",
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"appropriateness_note": "정서를 먼저 반영했다.",
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"rapport_signal": 0.75,
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"theory_mode": "humanistic",
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"techniques": [
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{
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"code": "empathy",
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"label_ko": "공감",
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"category": "relational",
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"rationale": "감정을 명시적으로 반영했다.",
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}
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],
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"client_state_read": [
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{
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"code": "affect_contact",
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"label_ko": "정서 접촉/표현",
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"rationale": "내담자가 감정을 언급했다.",
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}
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],
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}
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rows = {
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row["dimension"]: row
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for row in session_persistence._evaluation_feedback_rows(evaluation)
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}
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self.assertEqual(rows["appropriateness"]["score"], 5.0)
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self.assertEqual(rows["appropriateness"]["rationale"], "정서를 먼저 반영했다.")
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self.assertEqual(rows["rapport_signal"]["score"], 0.75)
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self.assertEqual(rows["theory_mode"]["rationale"], "humanistic")
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self.assertEqual(rows["technique:empathy"]["rationale"], "감정을 명시적으로 반영했다.")
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self.assertEqual(rows["client_state:affect_contact"]["rationale"], "내담자가 감정을 언급했다.")
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def test_rebuild_turn_evaluation_restores_review_shape(self) -> None:
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rebuilt = session_persistence._rebuild_turn_evaluations(
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[("11111111-1111-1111-1111-111111111111", 2, "탐색")],
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feedback_rows=[
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{
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"turn_id": "11111111-1111-1111-1111-111111111111",
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"dimension": "appropriateness",
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"score": 1.0,
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"rationale": "조언이 너무 빨랐다.",
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"top1_score": None,
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"loop": "fast",
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},
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{
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"turn_id": "11111111-1111-1111-1111-111111111111",
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"dimension": "technique:empathy",
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"score": None,
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"rationale": "정서 반영이 포함됐다.",
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"top1_score": None,
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"loop": "fast",
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},
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{
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"turn_id": "11111111-1111-1111-1111-111111111111",
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"dimension": "rapport_signal",
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"score": -0.4,
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"rationale": None,
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"top1_score": None,
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"loop": "fast",
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},
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],
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technique_rows=[
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{
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"turn_id": "11111111-1111-1111-1111-111111111111",
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"code": "empathy",
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"label_ko": "공감",
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"category": "relational",
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}
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],
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client_state_rows=[
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{
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"turn_id": "11111111-1111-1111-1111-111111111111",
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"code": "defensive",
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"label_ko": "방어",
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}
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],
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comment_rows=[
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{
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"turn_id": "11111111-1111-1111-1111-111111111111",
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"intent_deviation": {
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"dimension": "pacing",
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"expected": "감정 탐색",
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"actual": "해결 조언",
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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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ev = rebuilt["11111111-1111-1111-1111-111111111111"]
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self.assertEqual(ev["turn_seq"], 2)
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self.assertEqual(ev["stage"], "탐색")
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self.assertEqual(ev["appropriateness"], "warn")
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self.assertEqual(ev["appropriateness_note"], "조언이 너무 빨랐다.")
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self.assertEqual(ev["rapport_signal"], -0.4)
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self.assertEqual(ev["techniques"][0]["rationale"], "정서 반영이 포함됐다.")
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self.assertEqual(ev["client_state_read"][0]["label_ko"], "방어")
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self.assertEqual(ev["intent_deviation"]["dimension"], "pacing")
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def test_evaluation_rls_blocks_raw_learner_writes(self) -> None:
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root = Path(__file__).resolve().parents[3]
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sql = (root / "infra/db/init/04_audit_eval_rls.sql").read_text(encoding="utf-8")
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self.assertIn("ALTER TABLE app.feedback_scores ENABLE ROW LEVEL SECURITY", sql)
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self.assertIn("ALTER TABLE app.turn_technique ENABLE ROW LEVEL SECURITY", sql)
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self.assertIn("ALTER TABLE app.turn_client_state ENABLE ROW LEVEL SECURITY", sql)
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self.assertIn("ALTER TABLE app.supervisor_comment ENABLE ROW LEVEL SECURITY", sql)
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self.assertIn("ALTER TABLE app.alternative_utterance ENABLE ROW LEVEL SECURITY", sql)
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feedback_insert = sql.split("CREATE POLICY p_feedback_insert", 1)[1].split(");", 1)[0]
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self.assertNotIn("learner_id = app.current_uid()", feedback_insert)
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def test_append_turn_requires_inserted_turn_id(self) -> None:
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source = Path(session_persistence.__file__).read_text(encoding="utf-8")
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self.assertIn("RETURNING id", source)
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self.assertIn("if inserted_turn_id is None:", source)
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class EvaluationPersistenceIOTest(unittest.IsolatedAsyncioTestCase):
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async def test_persist_turn_evaluation_uses_evaluator_context_and_real_fast_tables(self) -> None:
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conn = FakeEvaluationConn()
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evaluation = {
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"loop": "fast",
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"turn_seq": 3,
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"stage": "탐색",
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"appropriateness": "warn",
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"appropriateness_note": "해결 제안이 빨랐다.",
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"techniques": [
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{
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"code": "empathy",
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"label_ko": "공감",
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"category": "relational",
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"rationale": "정서 반영.",
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}
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],
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"client_state_read": [
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{
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"code": "defensive",
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"label_ko": "방어",
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"rationale": "짧은 회피 반응.",
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}
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],
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"intent_deviation": {
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"dimension": "pacing",
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"expected": "탐색",
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"actual": "조언",
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"severity": "minor",
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},
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}
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await session_persistence._persist_turn_evaluation(
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conn,
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"11111111-1111-1111-1111-111111111111",
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evaluation,
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)
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executed_sql = "\n".join(query for query, _ in conn.executed)
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self.assertIn("set_config('app.ai_context', '1', true)", executed_sql)
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self.assertIn("set_config('app.current_ai_view', 'evaluator', true)", executed_sql)
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self.assertIn("INSERT INTO app.feedback_scores", executed_sql)
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self.assertIn("INSERT INTO app.turn_technique", executed_sql)
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self.assertIn("INSERT INTO app.turn_client_state", executed_sql)
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self.assertIn("INSERT INTO app.supervisor_comment", executed_sql)
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self.assertNotIn("app.alternative_utterance", executed_sql)
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async def test_route_loader_only_hydrates_when_requested(self) -> None:
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principal = Principal(
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user_id="00000000-0000-0000-0000-000000000101",
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role=Role.LEARNER,
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cohort_ids=[],
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email="eval-map@hs.ac.kr",
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display_name="Eval Map",
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)
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card = persona_service.P1
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sess = InProcSession(
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session_id="eval-map-session",
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case_id="eval-map-case",
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learner_id=principal.user_id,
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persona_code=card.code,
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theory_mode="humanistic",
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persona=card,
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state=state_machine.SessionState(
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resistance=card.base_resistance(),
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ideation_stage=card.ideation_baseline(),
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),
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)
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calls: list[bool] = []
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async def fake_load_session(*args, **kwargs):
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calls.append(bool(kwargs.get("include_turn_evaluation")))
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return sess
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with patch.object(sessions.session_persistence, "load_session", fake_load_session):
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await sessions._load_session_or_404(sess.session_id, principal)
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await sessions._load_session_or_404(
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sess.session_id,
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principal,
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allow_ended=True,
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include_turn_evaluation=True,
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)
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self.assertEqual(calls, [False, True])
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if __name__ == "__main__":
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unittest.main()
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