feat(profiling): P4 JSON/CSV baseline export + metadata (LOC-69)#73
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Export the report to the app documents dir as query_profile_<ts>.json/.csv with flush, one PROFILE log line per run, and the export dir/filenames logged for off-device pull (physical-device steps: Xcode Download Container / devicectl). Wire export into the measurement test + add os/os_version metadata. PR-P4.md + README. baseline = embed(ONNX) dominates warm; activate dominates cold.
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P4 — JSON/CSV export + 로그 + 메타 (baseline 산출, LOC-69)
feat/loc-68-profiler-timing(P3). P3을 main에 먼저 머지한 뒤 이 PR을 main으로 retarget 후 머지하세요(아니면 orphan).P3 위에 export를 얹어 baseline을 박제(1차 목표 달성 지점).
변경
example/lib/profiling/profile_export.dart— 리포트를 앱 documents dir에query_profile_<ts>.json/.csvflush + 실행당PROFILE로그 + dir/파일명 로그. 물리기기 추출: Xcode Download Container 또는xcrun devicectl(simctl은 시뮬전용).baseline (iPhone iOS 26.5, profile, 500 docs/collection, topK=10)
지배 세그먼트 → P5(LOC-70) 게이트
embed(ONNX) 지배 (~27ms) → 다음 타깃은 ONNX 추론(크레이트 외부), Rust 벡터 작업 불필요.activate(HNSW build/load) 지배 (247ms) → Phase-2에서bm25_rebuildvshnsw_load분해.상세: PR-P4.md · Linear LOC-69.
flutter analyze클린, iPhone profile 런 green. 머지는 본인.