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.
486 lines
18 KiB
Python
486 lines
18 KiB
Python
#!/usr/bin/env python3
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"""Real-Postgres G7 smoke using synthetic WAV metadata and the store boundary.
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Run only against an expendable database. The script creates fixture users/session
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and intentionally leaves them for the caller to discard with the database.
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"""
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from __future__ import annotations
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# smoke 러너는 앱 패키지를 불러오기 전에 apps/api를 sys.path에 추가해야 한다.
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# ruff: noqa: E402
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import asyncio
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import hashlib
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import io
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import math
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import struct
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import sys
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import wave
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from datetime import UTC, datetime, timedelta
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from pathlib import Path
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from uuid import UUID
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API_ROOT = Path(__file__).resolve().parents[1] / "apps" / "api"
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if str(API_ROOT) not in sys.path:
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sys.path.insert(0, str(API_ROOT))
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from app import db
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from app.contracts.multimodal_alliance import (
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FusionCalibration,
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ModalityAxisMeasurement,
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)
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from app.deps import Principal, Role
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from app.services import multimodal_alliance_store
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from app.services.multimodal_alliance import align_voice_timeline
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LEARNER_ID = UUID("77000000-0000-4000-8000-000000000001")
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TEACHER_ID = UUID("77000000-0000-4000-8000-000000000002")
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OUTSIDER_ID = UUID("77000000-0000-4000-8000-000000000003")
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ADMIN_ID = UUID("77000000-0000-4000-8000-000000000004")
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SESSION_ID = UUID("77000000-0000-4000-8000-000000000010")
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CONSENT_SUBMISSION_ID = UUID("77000000-0000-4000-8000-000000000011")
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TIMELINE_SUBMISSION_ID = UUID("77000000-0000-4000-8000-000000000012")
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TEXT_ONLY_SUBMISSION_ID = UUID("77000000-0000-4000-8000-000000000013")
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FUSION_SUBMISSION_ID = UUID("77000000-0000-4000-8000-000000000014")
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WITHDRAW_SUBMISSION_ID = UUID("77000000-0000-4000-8000-000000000015")
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COMPLETION_SUBMISSION_ID = UUID("77000000-0000-4000-8000-000000000016")
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def principal(user_id: UUID, role: Role, cohort: str) -> Principal:
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return Principal(
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user_id=str(user_id),
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role=role,
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cohort_ids=[cohort],
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consent_at=1.0,
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profile_completed_at=1.0,
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)
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def synthetic_wav() -> bytes:
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sample_rate = 16_000
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duration_ms = 1200
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frames = sample_rate * duration_ms // 1000
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output = io.BytesIO()
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with wave.open(output, "wb") as writer:
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writer.setnchannels(1)
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writer.setsampwidth(2)
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writer.setframerate(sample_rate)
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payload = bytearray()
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for index in range(frames):
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value = int(3200 * math.sin(2 * math.pi * 220 * index / sample_rate))
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payload.extend(struct.pack("<h", value))
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writer.writeframes(bytes(payload))
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return output.getvalue()
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def timeline():
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return align_voice_timeline(
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audio_duration_ms=1200,
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words=(
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{
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"word_index": 0,
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"start_ms": 40,
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"end_ms": 220,
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"speaker": "learner",
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"token_hash": "a" * 64,
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},
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{
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"word_index": 1,
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"start_ms": 500,
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"end_ms": 710,
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"speaker": "client",
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"token_hash": "b" * 64,
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},
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),
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events=(
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{
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"event_id": "oas-g7-event-smoke-silence",
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"event_type": "silence",
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"start_ms": 220,
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"end_ms": 500,
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"actor": "both",
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"observed_feature": "280ms turn transition silence",
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"uncertainty": 0.05,
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"source": "stt_word_timestamps",
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},
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{
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"event_id": "oas-g7-event-smoke-overlap",
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"event_type": "overlap",
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"start_ms": 710,
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"end_ms": 770,
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"actor": "both",
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"observed_feature": "60ms simultaneous speech segment",
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"uncertainty": 0.1,
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"source": "observed_audio_runtime",
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},
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{
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"event_id": "oas-g7-event-smoke-interruption",
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"event_type": "interruption",
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"start_ms": 770,
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"end_ms": 840,
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"actor": "learner",
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"observed_feature": "learner segment began before client segment ended",
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"uncertainty": 0.12,
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"source": "observed_audio_runtime",
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},
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{
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"event_id": "oas-g7-event-smoke-prosody",
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"event_type": "prosody",
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"start_ms": 840,
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"end_ms": 1080,
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"actor": "learner",
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"observed_feature": "median intensity decreased by 3dB",
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"uncertainty": 0.2,
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"source": "observed_audio_runtime",
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},
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),
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)
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async def seed_fixture() -> None:
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pool = db.get_pool()
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async with pool.acquire() as conn:
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for user_id, role, cohort in (
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(LEARNER_ID, "learner", "g7-cohort"),
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(TEACHER_ID, "instructor", "g7-cohort"),
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(OUTSIDER_ID, "instructor", "other-cohort"),
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(ADMIN_ID, "admin", "admin-cohort"),
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):
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await conn.execute(
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"""
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INSERT INTO app.app_user (
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user_id, external_id, email, display_name, role, cohort,
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consent_at, profile_completed_at
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) VALUES ($1,$2,$3,$4,$5,$6,now(),now())
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ON CONFLICT (user_id) DO NOTHING
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""",
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user_id,
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f"g7-smoke:{user_id}",
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f"{user_id}@g7-smoke.invalid",
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"G7 Smoke",
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role,
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cohort,
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)
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async with db.acquire(
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role="learner",
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user_id=str(LEARNER_ID),
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cohort_ids=["g7-cohort"],
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) as conn:
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await conn.execute(
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"""
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INSERT INTO app.sessions (id, learner_id, theory_mode)
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VALUES ($1,$2,'integrative')
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ON CONFLICT (id) DO NOTHING
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""",
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SESSION_ID,
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LEARNER_ID,
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)
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def measurement(
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*,
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measurement_id: str,
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modality: str,
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value: float,
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evidence_ref: str,
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model_run_id: UUID,
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) -> ModalityAxisMeasurement:
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return ModalityAxisMeasurement(
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measurement_id=measurement_id,
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axis="bond",
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modality=modality,
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status="ready",
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value=value,
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confidence=0.82,
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uncertainty=0.18,
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evidence_refs=(evidence_ref,),
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model_run_id=model_run_id,
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)
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async def main() -> None:
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await db.init_pool()
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try:
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await seed_fixture()
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learner = principal(LEARNER_ID, Role.LEARNER, "g7-cohort")
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teacher = principal(TEACHER_ID, Role.TEACHER, "g7-cohort")
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outsider = principal(OUTSIDER_ID, Role.TEACHER, "other-cohort")
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admin = principal(ADMIN_ID, Role.ADMIN, "admin-cohort")
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consent = await multimodal_alliance_store.append_consent_snapshot(
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principal=learner,
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session_id=SESSION_ID,
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submission_id=CONSENT_SUBMISSION_ID,
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consent_status="granted",
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retain_audio=True,
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retain_derived_features=True,
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transcript_retained=True,
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retention_days=30,
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policy_version="g7-smoke-v1",
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reason_code=None,
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)
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replay = await multimodal_alliance_store.append_consent_snapshot(
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principal=learner,
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session_id=SESSION_ID,
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submission_id=CONSENT_SUBMISSION_ID,
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consent_status="granted",
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retain_audio=True,
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retain_derived_features=True,
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transcript_retained=True,
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retention_days=30,
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policy_version="g7-smoke-v1",
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reason_code=None,
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)
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assert replay["idempotent_replay"]
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assert consent["consent_snapshot_id"] == replay["consent_snapshot_id"]
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audio = synthetic_wav()
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aligned = timeline()
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audio_asset = {
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"audio_ref": "g7-smoke://synthetic/one.wav",
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"audio_sha256": hashlib.sha256(audio).hexdigest(),
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"media_type": "audio/wav",
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"byte_size": len(audio),
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}
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async with db.acquire(ai_view="evaluator", ai_context=True) as conn:
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first = await multimodal_alliance_store.append_timeline(
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conn=conn,
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session_id=SESSION_ID,
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submission_id=TIMELINE_SUBMISSION_ID,
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timeline=aligned,
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audio_asset=audio_asset,
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)
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async with db.acquire(ai_view="evaluator", ai_context=True) as conn:
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second = await multimodal_alliance_store.append_timeline(
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conn=conn,
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session_id=SESSION_ID,
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submission_id=TIMELINE_SUBMISSION_ID,
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timeline=aligned,
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audio_asset=audio_asset,
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)
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assert second["idempotent_replay"]
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assert first["timeline_id"] == second["timeline_id"]
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changed = aligned.model_copy(update={"audio_duration_ms": 1300})
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try:
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async with db.acquire(ai_view="evaluator", ai_context=True) as conn:
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await multimodal_alliance_store.append_timeline(
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conn=conn,
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session_id=SESSION_ID,
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submission_id=TIMELINE_SUBMISSION_ID,
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timeline=changed,
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audio_asset=audio_asset,
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)
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except multimodal_alliance_store.MultimodalAllianceConflictError:
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pass
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else:
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raise AssertionError("changed idempotency payload was not rejected")
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provenance = {
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"instrument_id": "alliance-axis-observer",
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"instrument_version": "g7-smoke-v1",
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"model_name": "synthetic-observer",
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"prompt_version": "g7-smoke-v1",
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}
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text = measurement(
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measurement_id="oas-g7-measurement-smoke-text-no-gain",
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modality="text",
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value=0.62,
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evidence_ref="turn:learner:1",
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model_run_id=UUID("77000000-0000-4000-8000-000000000020"),
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)
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voice = measurement(
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measurement_id="oas-g7-measurement-smoke-voice-no-gain",
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modality="voice",
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value=0.7,
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evidence_ref="timeline:oas-g7-event-smoke-prosody",
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model_run_id=UUID("77000000-0000-4000-8000-000000000021"),
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)
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no_gain = FusionCalibration(
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calibration_id="oas-g7-fusion-smoke-no-gain",
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axis="bond",
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text_weight=0.75,
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voice_weight=0.25,
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text_only_accuracy=0.81,
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fused_accuracy=0.815,
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benchmark_version="g7-benchmark-smoke-v1",
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minimum_incremental_gain=0.01,
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)
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async with db.acquire(ai_view="evaluator", ai_context=True) as conn:
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text_only = await multimodal_alliance_store.append_measurement_fusion(
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conn=conn,
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session_id=SESSION_ID,
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submission_id=TEXT_ONLY_SUBMISSION_ID,
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text=text,
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voice=voice,
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calibration=no_gain,
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text_provenance=provenance,
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voice_provenance=provenance,
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)
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assert text_only["result"]["modalities_used"] == ["text"]
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assert not text_only["result"]["fusion_applied"]
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text_gain = text.model_copy(
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update={"measurement_id": "oas-g7-measurement-smoke-text-gain"}
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)
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voice_gain = voice.model_copy(
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update={"measurement_id": "oas-g7-measurement-smoke-voice-gain"}
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)
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gain = no_gain.model_copy(
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update={
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"calibration_id": "oas-g7-fusion-smoke-gain",
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"fused_accuracy": 0.84,
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}
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)
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async with db.acquire(ai_view="evaluator", ai_context=True) as conn:
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fused = await multimodal_alliance_store.append_measurement_fusion(
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conn=conn,
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session_id=SESSION_ID,
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submission_id=FUSION_SUBMISSION_ID,
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text=text_gain,
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voice=voice_gain,
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calibration=gain,
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text_provenance=provenance,
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voice_provenance=provenance,
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)
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assert fused["result"]["fusion_applied"]
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assert set(fused["result"]["modalities_used"]) == {"text", "voice"}
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learner_view = await multimodal_alliance_store.read_session_metadata(
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principal=learner, session_id=SESSION_ID
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)
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teacher_view = await multimodal_alliance_store.read_session_metadata(
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principal=teacher, session_id=SESSION_ID
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)
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assert len(learner_view["voice_events"]) == 4
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assert len(teacher_view["voice_events"]) == 4
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assert await multimodal_alliance_store.read_raw_audio_access(
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principal=learner, session_id=SESSION_ID
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)
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try:
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await multimodal_alliance_store.read_raw_audio_access(
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principal=teacher, session_id=SESSION_ID
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)
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except multimodal_alliance_store.MultimodalAllianceStateError:
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pass
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else:
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raise AssertionError("teacher unexpectedly received raw audio access")
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try:
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await multimodal_alliance_store.read_session_metadata(
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principal=outsider, session_id=SESSION_ID
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)
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except multimodal_alliance_store.MultimodalAllianceNotFoundError:
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pass
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else:
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raise AssertionError("out-of-cohort teacher unexpectedly saw G7 metadata")
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assert await multimodal_alliance_store.read_raw_audio_access(
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principal=admin, session_id=SESSION_ID
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)
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async with db.acquire(ai_view="evaluator", ai_context=True) as conn:
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expiry_work = (
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await multimodal_alliance_store.request_expired_retention_deletions(
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conn=conn,
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as_of=datetime.now(UTC) + timedelta(days=31),
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limit=10,
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)
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)
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assert len(expiry_work) == 1
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assert not expiry_work[0]["idempotent_replay"]
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async with db.acquire(ai_view="evaluator", ai_context=True) as conn:
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expiry_replay = (
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await multimodal_alliance_store.request_expired_retention_deletions(
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conn=conn,
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as_of=datetime.now(UTC) + timedelta(days=31),
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limit=10,
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)
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)
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assert expiry_replay[0]["idempotent_replay"]
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assert (
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expiry_work[0]["deletion_request_id"]
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== expiry_replay[0]["deletion_request_id"]
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)
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pool = db.get_pool()
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async with pool.acquire() as conn:
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async with conn.transaction():
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await conn.execute("SET LOCAL ROLE vignette")
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public_count = await conn.fetchval(
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"SELECT count(*) FROM app.multimodal_session_metadata_v"
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)
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assert public_count == 0
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withdrawal = await multimodal_alliance_store.append_consent_snapshot(
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principal=learner,
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session_id=SESSION_ID,
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submission_id=WITHDRAW_SUBMISSION_ID,
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consent_status="withdrawn",
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retain_audio=False,
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retain_derived_features=False,
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transcript_retained=True,
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retention_days=None,
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policy_version="g7-smoke-v1",
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reason_code="learner_withdrawal",
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)
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assert withdrawal["deletion_request_id"] is not None
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try:
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await multimodal_alliance_store.assert_voice_processing_allowed(
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principal=learner,
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session_id=str(SESSION_ID),
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)
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except multimodal_alliance_store.MultimodalConsentWithdrawnError:
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pass
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else:
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raise AssertionError("withdrawal did not block new voice processing")
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target_hash = hashlib.sha256(
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audio_asset["audio_ref"].encode("utf-8")
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).hexdigest()
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async with db.acquire(ai_view="evaluator", ai_context=True) as conn:
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completed = await multimodal_alliance_store.complete_deletion(
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conn=conn,
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deletion_request_id=withdrawal["deletion_request_id"],
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submission_id=COMPLETION_SUBMISSION_ID,
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tombstones=(
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{
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"scope": "audio",
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"target_ref_hash": target_hash,
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"deletion_proof": "synthetic object-store delete acknowledged",
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"deleted_at": datetime.now(UTC),
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},
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{
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"scope": "derived_features",
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"target_ref_hash": hashlib.sha256(
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str(first["timeline_id"]).encode("utf-8")
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).hexdigest(),
|
|
"deletion_proof": "derived feature access tombstoned",
|
|
"deleted_at": datetime.now(UTC),
|
|
},
|
|
),
|
|
actor_uid=None,
|
|
actor_kind="retention_worker",
|
|
)
|
|
assert len(completed["tombstone_ids"]) == 2
|
|
assert not await multimodal_alliance_store.read_raw_audio_access(
|
|
principal=learner, session_id=SESSION_ID
|
|
)
|
|
after = await multimodal_alliance_store.read_session_metadata(
|
|
principal=learner, session_id=SESSION_ID
|
|
)
|
|
assert not after["word_timestamps"] and not after["voice_events"]
|
|
assert all(item["modality"] == "text" for item in after["measurements"])
|
|
assert all(
|
|
"voice" not in item["modalities_used"] for item in after["fusion_decisions"]
|
|
)
|
|
|
|
print(
|
|
"G7 real DB smoke passed: consent, clock alignment, idempotency, "
|
|
"fusion gate, RLS, retention sweep, withdrawal, and tombstones"
|
|
)
|
|
finally:
|
|
await db.close_pool()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|