feat: Phase 1 handover - schema migration, correction mechanism, API fixes
Schema changes: dev.chunks->dev.chunk, remove old_chunk_id/chunk_index Correction: asr-1.json format, generate/apply scripts API: 37/37 endpoints fixed and tested Docs: HANDOVER_V2.0.md for M4
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Accusys
@@ -1,6 +1,6 @@
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# Visual Speaker Diarization 選型評估報告
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**日期**：2026-05-07
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**日期**：2026-05-07（初版）、2026-05-09（8Hz 實測）
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**作者**：M5
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**目的**：評估從視覺（嘴型）辨識誰在說話的技術方案
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@@ -319,3 +319,87 @@ else:
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| MediaPipe 478 點 3D landmarks | 更精確的嘴型 + 頭部轉向 | 安裝 MediaPipe（~30min） |
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| Per-trace lip motion history | 不只是 ASR 開始，追蹤整段說話的 lip 變化 | 已可行 |
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| VSP-LLM 完整部署 | 誰+說什麼 | 需 LLaMA2 授權 + AV-HuBERT |
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---
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## 6. 8Hz 實測（2026-05-09）
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### 6.1 測試目標
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驗證 Apple Vision（ANE）+ `sample_interval=3`（8Hz）對 lip motion 分析的可行性。
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### 6.2 測試參數
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| 項目 | 數值 |
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|------|------|
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| 影片 | Charade (1963)，前 10 分鐘 |
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| 解析度 | 1920×1080 |
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| FPS | 25 |
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| 測試時長 | 600s（0~600s） |
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| 總幀數 | 15,000 |
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| sample_interval | 3（8Hz ≈ 每幀 ~0.12s） |
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| 處理幀數 | ~5,000 |
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| 臉部分析 | Apple Vision（ANE）+ CoreML FaceNet |
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### 6.3 測試流程
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```
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1. 用 face_processor.py 以 interval=3 跑前 10 分鐘
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→ 輸出 {uuid}.face_test.json
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2. 從 face_test.json 提取 outer_lips → 計算 lip_openness
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lip_openness = max(outer_lips.y) - min(outer_lips.y)
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3. 讀 asrx.json speaker segments → 比對時間重疊
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4. 對每個 ASR segment 計算說話幀比例
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```
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### 6.4 執行
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```bash
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# 建立獨立測試目錄
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mkdir -p output_dev/lip_test
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# 跑 face detection @ 8Hz（僅前 600s）
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python3 scripts/face_processor.py \
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"var/sftpgo/data/demo/Charade (1963).mp4" \
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output_dev/lip_test/aeed71342a899fe4b4c57b7d41bcb692.face_test.json \
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--uuid aeed71342a899fe4b4c57b7d41bcb692 \
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--sample-interval 3 \
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--max-frames 15000
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# Lip openness 計算 + ASRX 對照
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python3 scripts/lip_analyzer.py \
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--face output_dev/lip_test/aeed71342a899fe4b4c57b7d41bcb692.face_test.json \
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--asrx output_dev/aeed71342a899fe4b4c57b7d41bcb692.asrx.json \
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--output output_dev/lip_test/aeed71342a899fe4b4c57b7d41bcb692.lip_test.json
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```
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### 6.5 結果
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> 測試執行於 2026-05-09 19:14。
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| 項目 | 結果 |
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|------|------|
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| 處理時間（Vision ANE） | **37 秒** |
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| 處理時間（CoreML ANE） | **356 秒**（~6 分鐘） |
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| 處理幀數 | 2,734（sample_interval=3，~8Hz） |
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| 偵測到臉的幀數 | 2,734（100%） |
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| outer_lips 有效幀 | 2,734（**100%**） |
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| ASRX 區段（0-600s） | 114 |
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| 有 face 資料區段 | 112（**98%**） |
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| 可判定 lip motion | 55（**49%** of face-present） |
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**關鍵發現：**
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- Apple Vision ANE 在 interval=3 時非常快（37 秒 / 10 分鐘影片），但 CoreML embedding 是瓶頸（356 秒），因為每張臉都要跑一次 FaceNet
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- outer_lips 覆蓋率 100% — 只要有臉就有 lips data
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- 98% 的 ASR 區段有對應的臉部資料（僅 2% 為畫外音）
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- 49% 的區段顯示明確 lip motion（>5% threshold），比之前 26% 大幅改善
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- 8Hz 連續取樣讓 baseline/during 比較可行 — 之前 sample_interval=30 時無法可靠計算
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**比起原始測試（sample_interval=30）的改善：**
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| 指標 | interval=30 | interval=3（8Hz） |
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|------|-------------|-------------------|
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| 每秒取樣數 | ~0.8 | **~8** |
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| lip 可分析幀 | 稀疏，無連續性 | **連續，可計算 baseline** |
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| 可判定 speaker | ~26% | **~49%** |
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@@ -0,0 +1,87 @@
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# 場景分類缺口分析
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## 現狀
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Places365（ResNet18, CoreML ANE）已被棄用 — 對 Charade 只偵測到 1 個 scene class（"door"），無實用價值。
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## 缺口
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CUT processor 產出 1130 個 scene boundary，但沒有任何 metadata 描述場景性質：
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- 室內/室外？
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- 白天/夜晚？
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- 靜態對話/動作場面？
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- 近景/遠景？
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- 情緒（緊張/輕鬆）？
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## 填補方案比較
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### A. 5W1H+ prompt 延伸（最快）
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在目前的 5W1H+ prompt 中加入場景分類，LLM 直接輸出。
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```json
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{
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"scene_summary": "...",
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"scene_type": "dialogue_interior",
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"setting": "restaurant",
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"lighting": "low_key",
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"mood": "tense",
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"shot_scale": "medium",
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...
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}
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```
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| 面向 | 評估 |
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|------|------|
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| 開發量 | 🟢 改 prompt 即可 |
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| 正確性 | ⚠️ 仰賴 LLM 對場景的理解 |
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| 成本 | 🟢 不增加額外 LLM call（已包含在 5W1H+） |
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| 可擴展 | ✅ 可任意增加分類維度 |
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### B. ffmpeg 物理特徵（M4 實驗方向）
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用 ffmpeg 內建 filter 對每個 scene 提取訊號：
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| 特徵 | ffmpeg filter | 可推論 |
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|------|-------------|--------|
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| Y 亮度均值 | signalstats | 白天/夜晚/室內 |
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| 運動量 | flow/mestimate | 動作/靜態 |
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| 音量 | volumedetect | 安靜/吵鬧 |
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| 對話/靜音 | silencedetect | 對話/過場 |
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| 色彩 | signalstats U/V | 色調 |
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| 面向 | 評估 |
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|------|------|
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| 開發量 | 🟡 需實作 scene-level 批次分析 |
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| 正確性 | ✅ 客觀數據 |
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| 成本 | 🟢 ffmpeg 內建 |
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| 限制 | ❌ 無法分辨場景類型（餐廳/辦公室/街頭） |
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### C. YOLO 物件統計
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從現有 YOLO pre_chunks 分析每個 scene 的物件分布：
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| 物件 | 推論場景 |
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|------|---------|
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| car, truck, traffic light | 街頭/戶外 |
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| bed, sofa, TV | 室內/居家 |
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| dining table, bottle, wine glass | 餐廳/酒吧 |
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| person × 1 | 獨白/近景 |
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| person × 3+ | 群戲 |
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| 面向 | 評估 |
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|------|------|
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| 開發量 | 🟢 查 pre_chunks 即可 |
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| 正確性 | ⚠️ 僅物件層次 |
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| 成本 | 🟢 已存在 |
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## 建議：A + B + C 三層次
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| 層次 | 方法 | 產出 | 優先級 |
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|------|------|------|--------|
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| 1 | 5W1H+ prompt 延伸（A） | 場景類型、設定、情緒 | 🥇 立即 |
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| 2 | YOLO 物件統計（C） | 物件分布、人數 | 🥈 短期 |
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| 3 | ffmpeg 物理特徵（B） | 亮度、運動、音量曲線 | 🥉 中期 |
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Layer 1 最簡單：5W1H+ 已經每 scene 呼叫 LLM，多加幾個 JSON field 零成本。
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240
docs_v1.0/M5_workspace/RELEASE_PHASES.md
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240
docs_v1.0/M5_workspace/RELEASE_PHASES.md
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# Momentry Model — 分階段交付
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## 核心架構
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```
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Pipeline (training)
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│ 每個 processor 產出 .json
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│ Rule 1/3 Ingestion → chunks + embeddings
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▼
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momentry model for {video} ← 每部影片 = 一個 model
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│ release/phase1/latest/
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│ release/phase2/latest/
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▼
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momentry core (inference engine) ← Rust API server
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│ momentry_playground (dev)
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│ momentry (production)
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▼
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Search / Query / Identity APIs
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```
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- **Pipeline** = training phase：影片 → processor output → chunks → embeddings
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- **Model** = 每部影片的產出 package（output_json + chunks + vectors）
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- **Engine** = momentry core，吃 model 提供 API（search, trace, identity）
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每個影片可有多個 model 版本，命名保留升級空間：
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| Model 版本 | Qdrant Collection | 內容 | 觸發時機 |
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|-----------|------------------|------|---------|
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| `{uuid}_v1` | `momentry_dev_v1` | sentence chunk embedding（base） | ASR + ASRX + Rule 1 完成 |
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| `{uuid}_v2` | `momentry_dev_v2` | 完整 pipeline + 5W1H | 全部完成 |
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| `{uuid}_v3` | `momentry_dev_v3` | object identity + custom detector | v2 + object instance matching 完成 |
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各版本共存不覆蓋。
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## 階段劃分
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### Phase 1：Sentence Chunk Embedding（base model）
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**觸發時機**: ASR + ASRX 完成 + Rule 1 Ingestion + vectorize 完成
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**交付內容**:
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- `{uuid}.asr.json`
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- `{uuid}.asrx.json`
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- chunks（chunk_type = 'sentence'）
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- chunk_vectors（sentence embedding）
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**用途**: 終端使用者可進行語意搜尋
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### Phase 2：完整 Pipeline（v2 model）
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**觸發時機**: 全部 processor 完成 + Rule 3 Ingestion + 5W1H Agent
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**交付內容**:
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- Phase 1 全部內容
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- 所有 `{uuid}.*.json`（cut, yolo, face, pose, ocr, ...）
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- chunks（chunk_type = 'cut', 'visual', 'trace', 'story'）
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- chunk_vectors（summary embedding）
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- identities / identity_bindings / face_detections
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**用途**: 完整搜尋 + 摘要 + 人物識別
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---
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## Worker Pipeline
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```
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ASR 完成 → ASRX 完成
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↓
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Rule 1 Ingestion (sentence chunks)
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↓
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vectorize_chunks (sentence embedding)
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↓
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📦 Phase 1 release ───→ release/phase1/latest/ (base model)
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↓
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其他 processors 繼續 (yolo, face, pose, ocr, ...)
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↓
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Rule 3 Ingestion + 5W1H Agent
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↓
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📦 Phase 2 release ───→ release/phase2/latest/ (full model)
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```
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## 產出目錄結構
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```
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release/
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├── phase1/
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│ ├── {version}_{timestamp}/
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│ │ ├── output_json/ ← 所有已完成的 .json
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│ │ ├── chunks.csv ← sentence chunks
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│ │ ├── vectors.csv ← sentence embeddings
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│ │ ├── schema.sql ← chunks table DDL
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│ │ └── RELEASE_INFO.txt
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│ └── latest → {version}_{timestamp}
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│
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└── phase2/
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├── {version}_{timestamp}/
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│ ├── output_json/ ← 所有 .json
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│ ├── chunks.csv ← 所有 chunks
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│ ├── vectors.csv ← 所有 embeddings
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│ ├── identities.csv ← 人物身分
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│ ├── schema.sql ← 完整 schema
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│ └── RELEASE_INFO.txt
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└── latest → {version}_{timestamp}
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```
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## momentry model vs momentry core
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| | momentry model | momentry core |
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|---|---|---|
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| 類比 | 訓練好的 weights | inference engine |
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| 內容 | `.json` + chunks + vectors | Rust binary |
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| 生命週期 | 每部影片產出一個 | 一個 binary 服務所有影片 |
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| 版本 | `{uuid}_v1`（base） / `{uuid}_v2` / `{uuid}_v3` | `momentry_playground` / `momentry` |
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| 交付對象 | 終端使用者 | 部署工程師 |
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---
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## Wiki 機制：每個 model 都可被調整
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每個 momentry model（`{uuid}_v1` / `v2` / `v3`）不只是唯讀的產出，而是可透過 wiki 機制持續改善。
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### 與傳統 RAG 的區別
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| | 傳統 RAG | momentry wiki |
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|---|---|---|
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| 知識儲存 | vector DB（ephemeral） | model package（permanent） |
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| 修正方式 | query 時 LLM 決定是否採用 | 使用者/Agent 直接編輯 |
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| 修正持久性 | ❌ 下次 query 就消失 | ✅ 寫入 model，版本化保存 |
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| 模型改進 | 無（僅改變 prompt） | 下次 version bump 時合併為 ground truth |
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| 協作方式 | 單向（retrieve → generate） | 雙向（編輯 → 合併 → 改進） |
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| 離線可用 | ❌ 需 vector DB + LLM | ✅ 離線查閱 wiki 目錄 |
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**momentry wiki 不是 RAG 的替代品，而是 model 的生命週期管理機制。**
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### 概念
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```
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momentry model (release package)
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├── output_json/ ← 唯讀，processor 產出
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├── chunks.csv ← 唯讀，ingestion 產出
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├── vectors.csv ← 唯讀，embedding 產出
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└── wiki/ ← 可編輯，使用者貢獻知識
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├── identities.json ← "trace 5 = Audrey Hepburn"
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├── objects.json ← "object 42 = 郵票 #1"
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├── corrections.json ← "ASR 'Hello' → 'Halo'"
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└── changelog.json ← 編輯歷史
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```
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### 資料流向
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```
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使用者/Agent 編輯 wiki
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↓
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DB wiki_entries + wiki_revisions 寫入
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↓
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下次 release 打包時 merge 進 model
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↓
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TKG label 更新 (tkg_nodes.label)
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↓
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新版 model version bump
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```
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### 與 TKG 的關係
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wiki 的 identity 和 object 標註會回寫到 TKG node label：
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```
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(face_trace:5) label="Audrey Hepburn" ← wiki 編輯
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(object_instance:42) label="郵票 #1" ← wiki 編輯
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```
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這些編輯累積後，可做為下一版 model training 的 ground truth。
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### 實作方向
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**DB 層** — 新 table `wiki_entries` + `wiki_revisions`：
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```sql
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wiki_entries (target_type, target_id, title, body, summary, status, version, file_uuid)
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wiki_revisions (entry_id, version, title, body, summary, change_summary, edited_by)
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```
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**API 層** — CRUD + 版本歷史：
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```
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GET /api/v1/wiki/{target_type}/{target_id}
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PUT /api/v1/wiki/{target_type}/{target_id}
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GET /api/v1/wiki/{target_type}/{target_id}/revisions
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POST /api/v1/wiki/search
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```
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**打包層** — `release_pack.py` 加入 wiki 匯出，與 model 共存
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|
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---
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## Phase 3：Object Identity（v3 model）
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### 目標
|
||||
|
||||
從影片中提取關鍵物體（郵票、手槍、信封、放大鏡...），對同類物體做 instance-level 的跨畫面追蹤與辨識，達到類似 face trace 的效果 — 不只是 detect class，還能區分「這一張郵票」vs「那一張郵票」。
|
||||
|
||||
### 現狀問題
|
||||
|
||||
1. **COCO 80 類不包含關鍵物體** — 郵票、手槍、信封、放大鏡等不在 COCO 資料集中
|
||||
2. **YOLOv5nano 偵測率低** — 即使是 COCO 類別（knife, cell phone）在 nano 模型上 recall 不足
|
||||
3. **無 object instance matching** — 目前只有 frame-level detection，沒有跨 frame 的物體追蹤
|
||||
|
||||
### 技術方向
|
||||
|
||||
```
|
||||
YOLOv8m/OWL-ViT → 改善 detection coverage
|
||||
↓
|
||||
Object Tracker (IoU + embedding，類似 face tracker)
|
||||
↓
|
||||
object_trace → TKG CO_OCCURS_WITH edges
|
||||
↓
|
||||
object identity → 同物體跨場景辨識
|
||||
```
|
||||
|
||||
| 方向 | 方法 | 效果 |
|
||||
|------|------|------|
|
||||
| Model upgrade | `yolov5nu` → `yolov8s.pt` / `yolov8m.pt` | COCO recall 提升 |
|
||||
| Custom fine-tune | 收集 stamps/guns 資料 fine-tune YOLO | 可偵測非 COCO 物件 |
|
||||
| Zero-shot | OWL-ViT / Grounding DINO by text prompt | 不用 training，但速度慢 |
|
||||
| Object trace | IoU + embedding 跨 frame 匹配 | instance-level 追蹤 |
|
||||
| Object identity | clustering 跨場景辨識同一物體 | 可在全片搜尋「這把槍」 |
|
||||
|
||||
### 與 TKG 整合
|
||||
|
||||
```
|
||||
face_trace -[:CO_OCCURS_WITH]-> object_instance:5 (這把槍)
|
||||
face_trace -[:CO_OCCURS_WITH]-> object_instance:42 (這張郵票)
|
||||
|
||||
查詢: "Audrey Hepburn 拿這把槍的畫面"
|
||||
→ face_trace:5 -[:SPEAKS_AS]-> SPEAKER_0
|
||||
→ face_trace:5 -[:CO_OCCURS_WITH]-> object_instance:5
|
||||
```
|
||||
|
||||
### 交付順序
|
||||
|
||||
1. YOLO model upgrade（低難度，立即見效）
|
||||
2. Object tracker（中難度，參考 face tracker 實作）
|
||||
3. Custom fine-tune / zero-shot（高難度，需資料或新模型）
|
||||
244
docs_v1.0/M5_workspace/patch_chunk.diff
Normal file
244
docs_v1.0/M5_workspace/patch_chunk.diff
Normal file
@@ -0,0 +1,244 @@
|
||||
diff --git a/src/core/chunk/mod.rs b/src/core/chunk/mod.rs
|
||||
index 14226fd..75e4d80 100644
|
||||
--- a/src/core/chunk/mod.rs
|
||||
+++ b/src/core/chunk/mod.rs
|
||||
@@ -1,9 +1,11 @@
|
||||
pub mod rule1_ingest;
|
||||
pub mod rule3_ingest;
|
||||
pub mod splitter;
|
||||
+pub mod trace_ingest;
|
||||
pub mod types;
|
||||
|
||||
pub use rule1_ingest::execute_rule1;
|
||||
pub use rule3_ingest::ingest_rule3;
|
||||
+pub use trace_ingest::ingest_traces;
|
||||
pub use splitter::{AsrSegment, ChunkSplitter};
|
||||
pub use types::{Chunk, ChunkType};
|
||||
diff --git a/src/core/chunk/trace_ingest.rs b/src/core/chunk/trace_ingest.rs
|
||||
new file mode 100644
|
||||
index 0000000..3821cc7
|
||||
--- /dev/null
|
||||
+++ b/src/core/chunk/trace_ingest.rs
|
||||
@@ -0,0 +1,222 @@
|
||||
+use crate::core::chunk::types::{Chunk, ChunkRule, ChunkType};
|
||||
+use crate::core::db::schema;
|
||||
+use crate::core::db::PostgresDb;
|
||||
+use anyhow::{Context, Result};
|
||||
+use sqlx::Row;
|
||||
+use tracing::{error, info};
|
||||
+
|
||||
+pub async fn ingest_traces(db: &PostgresDb, file_uuid: &str) -> Result<usize> {
|
||||
+ let pool = db.pool();
|
||||
+ let face_table = schema::table_name("face_detections");
|
||||
+ let pre_table = schema::table_name("pre_chunks");
|
||||
+
|
||||
+ let video = db
|
||||
+ .get_video_by_uuid(file_uuid)
|
||||
+ .await?
|
||||
+ .context("Video not found")?;
|
||||
+ let file_id = video.id as i32;
|
||||
+ let fps = video.fps;
|
||||
+
|
||||
+ let traces = sqlx::query_as::<_, TraceAgg>(&format!(
|
||||
+ r#"
|
||||
+ SELECT trace_id,
|
||||
+ MIN(frame_number) AS first_frame,
|
||||
+ MAX(frame_number) AS last_frame,
|
||||
+ MIN(timestamp_secs) AS first_time,
|
||||
+ MAX(timestamp_secs) AS last_time,
|
||||
+ COUNT(*) AS face_count,
|
||||
+ AVG(x)::float8 AS avg_x,
|
||||
+ AVG(y)::float8 AS avg_y,
|
||||
+ AVG(width)::float8 AS avg_w,
|
||||
+ AVG(height)::float8 AS avg_h
|
||||
+ FROM {}
|
||||
+ WHERE file_uuid = $1 AND trace_id IS NOT NULL
|
||||
+ GROUP BY trace_id
|
||||
+ ORDER BY trace_id
|
||||
+ "#,
|
||||
+ face_table
|
||||
+ ))
|
||||
+ .bind(file_uuid)
|
||||
+ .fetch_all(pool)
|
||||
+ .await?;
|
||||
+
|
||||
+ if traces.is_empty() {
|
||||
+ info!("No traces found for {}", file_uuid);
|
||||
+ return Ok(0);
|
||||
+ }
|
||||
+
|
||||
+ let asr_segments = sqlx::query_as::<_, AsrSegment>(&format!(
|
||||
+ r#"
|
||||
+ SELECT start_frame, end_frame, start_time, end_time, data
|
||||
+ FROM {}
|
||||
+ WHERE file_uuid = $1 AND processor_type = 'asr'
|
||||
+ ORDER BY start_frame
|
||||
+ "#,
|
||||
+ pre_table
|
||||
+ ))
|
||||
+ .bind(file_uuid)
|
||||
+ .fetch_all(pool)
|
||||
+ .await?;
|
||||
+
|
||||
+ // 計算 pairwise trace 重疊關係
|
||||
+ let overlaps = compute_overlaps(&traces);
|
||||
+
|
||||
+ let mut count = 0;
|
||||
+ for trace in &traces {
|
||||
+ let text = collect_overlapping_text(&asr_segments, trace.first_time, trace.last_time);
|
||||
+
|
||||
+ let bbox = serde_json::json!({
|
||||
+ "x": trace.avg_x,
|
||||
+ "y": trace.avg_y,
|
||||
+ "width": trace.avg_w,
|
||||
+ "height": trace.avg_h,
|
||||
+ });
|
||||
+
|
||||
+ // 與此 trace 同框的其他 trace
|
||||
+ let co_appearances: Vec<serde_json::Value> = overlaps
|
||||
+ .iter()
|
||||
+ .filter(|o| o.trace_id == trace.trace_id)
|
||||
+ .map(|o| {
|
||||
+ serde_json::json!({
|
||||
+ "trace_id": o.other_trace_id,
|
||||
+ "overlap_frames": o.overlap_frames,
|
||||
+ "overlap_secs": (o.overlap_frames as f64 / fps * 100.0).round() / 100.0,
|
||||
+ })
|
||||
+ })
|
||||
+ .collect();
|
||||
+
|
||||
+ let metadata = serde_json::json!({
|
||||
+ "trace_id": trace.trace_id,
|
||||
+ "face_count": trace.face_count,
|
||||
+ "bbox": bbox,
|
||||
+ "co_appearances": co_appearances,
|
||||
+ });
|
||||
+
|
||||
+ let chunk = Chunk::new(
|
||||
+ file_id,
|
||||
+ file_uuid.to_string(),
|
||||
+ (count + 1) as u32,
|
||||
+ ChunkType::Trace,
|
||||
+ ChunkRule::Rule1,
|
||||
+ trace.first_frame as i64,
|
||||
+ trace.last_frame as i64,
|
||||
+ fps,
|
||||
+ metadata.clone(),
|
||||
+ )
|
||||
+ .with_text_content(text)
|
||||
+ .with_metadata(metadata)
|
||||
+ .with_frame_count(trace.face_count as i32);
|
||||
+
|
||||
+ if let Err(e) = db.store_chunk(&chunk).await {
|
||||
+ error!("Failed to store trace chunk {}: {}", trace.trace_id, e);
|
||||
+ } else {
|
||||
+ let preview = chunk.text_content.as_deref().unwrap_or("").chars().take(60).collect::<String>();
|
||||
+ let co = chunk.metadata.as_ref()
|
||||
+ .and_then(|m| m.get("co_appearances"))
|
||||
+ .and_then(|c| c.as_array())
|
||||
+ .map(|a| a.len())
|
||||
+ .unwrap_or(0);
|
||||
+ info!(
|
||||
+ "Trace chunk {}: trace_id={} frames={}-{} faces={} co_appear={} text={}",
|
||||
+ chunk.chunk_id, trace.trace_id,
|
||||
+ trace.first_frame, trace.last_frame,
|
||||
+ trace.face_count, co, preview,
|
||||
+ );
|
||||
+ count += 1;
|
||||
+ }
|
||||
+ }
|
||||
+
|
||||
+ info!("Ingested {} trace chunks for {}", count, file_uuid);
|
||||
+ Ok(count)
|
||||
+}
|
||||
+
|
||||
+/// 計算所有 trace pair 之間在時間上的重疊 frame 數
|
||||
+struct TraceOverlap {
|
||||
+ trace_id: i32,
|
||||
+ other_trace_id: i32,
|
||||
+ overlap_frames: i64,
|
||||
+}
|
||||
+
|
||||
+fn compute_overlaps(traces: &[TraceAgg]) -> Vec<TraceOverlap> {
|
||||
+ let mut result = Vec::new();
|
||||
+ for (i, a) in traces.iter().enumerate() {
|
||||
+ for b in traces.iter().skip(i + 1) {
|
||||
+ let overlap_start = a.first_frame.max(b.first_frame);
|
||||
+ let overlap_end = a.last_frame.min(b.last_frame);
|
||||
+ let frames = overlap_end - overlap_start;
|
||||
+ if frames > 0 {
|
||||
+ result.push(TraceOverlap {
|
||||
+ trace_id: a.trace_id,
|
||||
+ other_trace_id: b.trace_id,
|
||||
+ overlap_frames: frames,
|
||||
+ });
|
||||
+ result.push(TraceOverlap {
|
||||
+ trace_id: b.trace_id,
|
||||
+ other_trace_id: a.trace_id,
|
||||
+ overlap_frames: frames,
|
||||
+ });
|
||||
+ }
|
||||
+ }
|
||||
+ }
|
||||
+ result
|
||||
+}
|
||||
+
|
||||
+fn collect_overlapping_text(segments: &[AsrSegment], start_time: f64, end_time: f64) -> String {
|
||||
+ let mut texts: Vec<&str> = Vec::new();
|
||||
+ for seg in segments {
|
||||
+ if seg.end_time >= start_time && seg.start_time <= end_time {
|
||||
+ if let Some(t) = seg.text() {
|
||||
+ texts.push(t);
|
||||
+ }
|
||||
+ }
|
||||
+ }
|
||||
+ texts.join(" ")
|
||||
+}
|
||||
+
|
||||
+#[derive(Debug, sqlx::FromRow)]
|
||||
+struct TraceAgg {
|
||||
+ trace_id: i32,
|
||||
+ first_frame: i64,
|
||||
+ last_frame: i64,
|
||||
+ first_time: f64,
|
||||
+ last_time: f64,
|
||||
+ face_count: i64,
|
||||
+ avg_x: f64,
|
||||
+ avg_y: f64,
|
||||
+ avg_w: f64,
|
||||
+ avg_h: f64,
|
||||
+}
|
||||
+
|
||||
+struct AsrSegment {
|
||||
+ start_frame: i64,
|
||||
+ end_frame: i64,
|
||||
+ start_time: f64,
|
||||
+ end_time: f64,
|
||||
+ data: serde_json::Value,
|
||||
+}
|
||||
+
|
||||
+impl<'r> sqlx::FromRow<'r, sqlx::postgres::PgRow> for AsrSegment {
|
||||
+ fn from_row(row: &'r sqlx::postgres::PgRow) -> Result<Self, sqlx::Error> {
|
||||
+ Ok(Self {
|
||||
+ start_frame: row.try_get("start_frame")?,
|
||||
+ end_frame: row.try_get("end_frame")?,
|
||||
+ start_time: row.try_get("start_time")?,
|
||||
+ end_time: row.try_get("end_time")?,
|
||||
+ data: row.try_get("data")?,
|
||||
+ })
|
||||
+ }
|
||||
+}
|
||||
+
|
||||
+impl AsrSegment {
|
||||
+ fn text(&self) -> Option<&str> {
|
||||
+ self.data
|
||||
+ .get("text")
|
||||
+ .and_then(|v| v.as_str())
|
||||
+ .or_else(|| {
|
||||
+ self.data
|
||||
+ .get("data")
|
||||
+ .and_then(|d| d.get("text"))
|
||||
+ .and_then(|v| v.as_str())
|
||||
+ })
|
||||
+ }
|
||||
+}
|
||||
17
docs_v1.0/M5_workspace/patch_executor.diff
Normal file
17
docs_v1.0/M5_workspace/patch_executor.diff
Normal file
@@ -0,0 +1,17 @@
|
||||
diff --git a/src/core/processor/executor.rs b/src/core/processor/executor.rs
|
||||
index 494ee2b..fc604bc 100644
|
||||
--- a/src/core/processor/executor.rs
|
||||
+++ b/src/core/processor/executor.rs
|
||||
@@ -244,8 +244,10 @@ impl PythonExecutor {
|
||||
.and_then(|c| serde_json::from_str::<serde_json::Value>(&c).ok())
|
||||
.is_some();
|
||||
if is_valid {
|
||||
- let _ = std::fs::rename(tmp, out);
|
||||
- tracing::warn!("[Executor] Partial output preserved: {:?}", out);
|
||||
+ let mut partial_path = out.to_path_buf();
|
||||
+ partial_path.set_extension("json.partial");
|
||||
+ let _ = std::fs::rename(tmp, &partial_path);
|
||||
+ tracing::warn!("[Executor] Partial output preserved: {:?}", partial_path);
|
||||
} else {
|
||||
let mut err_path = out.to_path_buf();
|
||||
err_path.set_extension("json.err");
|
||||
52
docs_v1.0/M5_workspace/patch_release_phases.diff
Normal file
52
docs_v1.0/M5_workspace/patch_release_phases.diff
Normal file
@@ -0,0 +1,52 @@
|
||||
diff --git a/src/worker/job_worker.rs b/src/worker/job_worker.rs
|
||||
index dceb674..4accd3e 100644
|
||||
--- a/src/worker/job_worker.rs
|
||||
+++ b/src/worker/job_worker.rs
|
||||
@@ -681,6 +681,21 @@ impl JobWorker {
|
||||
error!("❌ Auto-vectorize failed for {}: {}", uuid_clone, e);
|
||||
}
|
||||
}
|
||||
+ // Phase 1 release: sentence chunk embedding 交付
|
||||
+ info!("📦 Phase 1 release packaging...");
|
||||
+ let executor = match crate::core::processor::PythonExecutor::new() {
|
||||
+ Ok(ex) => ex,
|
||||
+ Err(e) => { error!("Failed PythonExecutor for release pack: {}", e); return; }
|
||||
+ };
|
||||
+ match executor.run(
|
||||
+ "release_pack.py",
|
||||
+ &["--phase", "1", "--file-uuid", &uuid_clone],
|
||||
+ None, "RELEASE_P1",
|
||||
+ Some(std::time::Duration::from_secs(120)),
|
||||
+ ).await {
|
||||
+ Ok(()) => info!("✅ Phase 1 release packaged for {}", uuid_clone),
|
||||
+ Err(e) => error!("❌ Phase 1 release pack failed: {}", e),
|
||||
+ }
|
||||
}
|
||||
Err(e) => error!("❌ Rule 1 Ingestion failed: {}", e),
|
||||
}
|
||||
@@ -830,7 +845,24 @@ impl JobWorker {
|
||||
tokio::spawn(async move {
|
||||
tokio::time::sleep(tokio::time::Duration::from_secs(30)).await;
|
||||
match run_5w1h_agent(&db_clone, &uuid_clone).await {
|
||||
- Ok(()) => info!("✅ 5W1H Agent completed for {}", uuid_clone),
|
||||
+ Ok(()) => {
|
||||
+ info!("✅ 5W1H Agent completed for {}", uuid_clone);
|
||||
+ // Phase 2 release: full pipeline 交付
|
||||
+ info!("📦 Phase 2 release packaging...");
|
||||
+ let executor = match crate::core::processor::PythonExecutor::new() {
|
||||
+ Ok(ex) => ex,
|
||||
+ Err(e) => { error!("Failed PythonExecutor for release pack: {}", e); return; }
|
||||
+ };
|
||||
+ match executor.run(
|
||||
+ "release_pack.py",
|
||||
+ &["--phase", "2", "--file-uuid", &uuid_clone],
|
||||
+ None, "RELEASE_P2",
|
||||
+ Some(std::time::Duration::from_secs(120)),
|
||||
+ ).await {
|
||||
+ Ok(()) => info!("✅ Phase 2 release packaged for {}", uuid_clone),
|
||||
+ Err(e) => error!("❌ Phase 2 release pack failed: {}", e),
|
||||
+ }
|
||||
+ }
|
||||
Err(e) => error!("❌ 5W1H Agent failed for {}: {}", uuid_clone, e),
|
||||
}
|
||||
});
|
||||
111
docs_v1.0/M5_workspace/patch_search.diff
Normal file
111
docs_v1.0/M5_workspace/patch_search.diff
Normal file
@@ -0,0 +1,111 @@
|
||||
diff --git a/src/api/universal_search.rs b/src/api/universal_search.rs
|
||||
index 054a1f4..2fc9520 100644
|
||||
--- a/src/api/universal_search.rs
|
||||
+++ b/src/api/universal_search.rs
|
||||
@@ -20,6 +20,8 @@ pub struct UniversalSearchRequest {
|
||||
pub types: Vec<String>, // chunk, frame, person
|
||||
pub time_range: Option<[f64; 2]>,
|
||||
pub filters: Option<SearchFilters>,
|
||||
+ pub page: Option<usize>,
|
||||
+ pub page_size: Option<usize>,
|
||||
pub limit: Option<usize>,
|
||||
pub offset: Option<usize>,
|
||||
}
|
||||
@@ -31,6 +33,10 @@ pub struct SearchFilters {
|
||||
pub ocr_text: Option<String>,
|
||||
pub has_face: Option<bool>,
|
||||
pub speaker_id: Option<String>,
|
||||
+ /// 指定 chunk_type：如 "sentence", "cut", "trace", "visual"
|
||||
+ pub chunk_type: Option<String>,
|
||||
+ /// 搜尋與指定 trace_id 有時間重疊的 trace chunk
|
||||
+ pub co_appears_with_trace_id: Option<i32>,
|
||||
// Visual chunk filters
|
||||
pub min_confidence: Option<f32>,
|
||||
pub min_unique_classes: Option<u32>,
|
||||
@@ -44,6 +50,8 @@ pub struct UniversalSearchResponse {
|
||||
pub query: String,
|
||||
pub results: Vec<SearchResult>,
|
||||
pub total: usize,
|
||||
+ pub page: usize,
|
||||
+ pub page_size: usize,
|
||||
pub took_ms: u64,
|
||||
}
|
||||
|
||||
@@ -108,8 +116,14 @@ pub async fn universal_search(
|
||||
)
|
||||
})?;
|
||||
|
||||
- let limit = req.limit.unwrap_or(20);
|
||||
- let offset = req.offset.unwrap_or(0);
|
||||
+ let page = req.page.unwrap_or(1).max(1);
|
||||
+ let page_size = req.page_size.unwrap_or(20).max(1).min(200);
|
||||
+ // Backward compat: if old `offset` is used without `page`, derive from offset
|
||||
+ let offset = if req.page.is_none() && req.offset.is_some() {
|
||||
+ req.offset.unwrap()
|
||||
+ } else {
|
||||
+ (page - 1) * page_size
|
||||
+ };
|
||||
let types = if req.types.is_empty() {
|
||||
vec![
|
||||
"chunk".to_string(),
|
||||
@@ -163,7 +177,8 @@ pub async fn universal_search(
|
||||
});
|
||||
|
||||
let total = results.len();
|
||||
- let end = std::cmp::min(offset + limit, results.len());
|
||||
+ let effective_limit = req.limit.unwrap_or(usize::MAX);
|
||||
+ let end = std::cmp::min(offset + page_size, results.len()).min(effective_limit);
|
||||
let paginated = if offset < results.len() {
|
||||
results[offset..end].to_vec()
|
||||
} else {
|
||||
@@ -176,6 +191,8 @@ pub async fn universal_search(
|
||||
query: req.query,
|
||||
results: paginated,
|
||||
total,
|
||||
+ page,
|
||||
+ page_size,
|
||||
took_ms: took,
|
||||
}))
|
||||
}
|
||||
@@ -378,10 +395,22 @@ async fn search_chunks(
|
||||
sql.push_str(&format!(" AND ({})", class_conditions.join(" OR ")));
|
||||
}
|
||||
}
|
||||
+ if let Some(ref chunk_type) = filters.chunk_type {
|
||||
+ sql.push_str(&format!(
|
||||
+ " AND chunk_type = '{}'",
|
||||
+ chunk_type.replace('\'', "''")
|
||||
+ ));
|
||||
+ }
|
||||
+ if let Some(trace_id) = filters.co_appears_with_trace_id {
|
||||
+ sql.push_str(&format!(
|
||||
+ " AND metadata->'co_appearances' @> '[{{ \"trace_id\": {} }}]'",
|
||||
+ trace_id
|
||||
+ ));
|
||||
+ }
|
||||
}
|
||||
|
||||
sql.push_str(" ORDER BY start_time ASC");
|
||||
- sql.push_str(&format!(" LIMIT {}", req.limit.unwrap_or(20)));
|
||||
+ sql.push_str(&format!(" LIMIT {}", req.page_size.unwrap_or(20)));
|
||||
|
||||
let rows: Vec<(
|
||||
String,
|
||||
@@ -495,7 +524,7 @@ async fn search_frames_internal(
|
||||
}
|
||||
|
||||
sql.push_str(" ORDER BY f.timestamp ASC");
|
||||
- sql.push_str(&format!(" LIMIT {}", req.limit.unwrap_or(20)));
|
||||
+ sql.push_str(&format!(" LIMIT {}", req.page_size.unwrap_or(20)));
|
||||
|
||||
let rows: Vec<(
|
||||
i64,
|
||||
@@ -575,7 +604,7 @@ async fn search_persons_internal(
|
||||
}
|
||||
|
||||
sql.push_str(" ORDER BY appearance_count DESC");
|
||||
- sql.push_str(&format!(" LIMIT {}", req.limit.unwrap_or(20)));
|
||||
+ sql.push_str(&format!(" LIMIT {}", req.page_size.unwrap_or(20)));
|
||||
|
||||
let rows: Vec<(
|
||||
String,
|
||||
153
docs_v1.0/M5_workspace/patch_worker_tkg.diff
Normal file
153
docs_v1.0/M5_workspace/patch_worker_tkg.diff
Normal file
@@ -0,0 +1,153 @@
|
||||
diff --git a/scripts/tkg_builder.py b/scripts/tkg_builder.py
|
||||
index 31ccf8a..8941d7f 100644
|
||||
--- a/scripts/tkg_builder.py
|
||||
+++ b/scripts/tkg_builder.py
|
||||
@@ -365,6 +365,73 @@ def build_speaker_face_edges(cur, schema, file_uuid):
|
||||
return edge_count
|
||||
|
||||
|
||||
+def build_face_face_edges(cur, schema, file_uuid):
|
||||
+ """Build CO_OCCURS_WITH edges: face_trace ↔ face_trace in same frame"""
|
||||
+ print("[TKG] Building face-face co-occurrence edges...")
|
||||
+
|
||||
+ cur.execute(
|
||||
+ f"""
|
||||
+ SELECT a.trace_id AS tid_a, b.trace_id AS tid_b,
|
||||
+ a.frame_number, a.timestamp_secs,
|
||||
+ a.x AS ax, a.y AS ay, a.width AS aw, a.height AS ah,
|
||||
+ b.x AS bx, b.y AS by, b.width AS bw, b.height AS bh
|
||||
+ FROM {schema}.face_detections a
|
||||
+ JOIN {schema}.face_detections b
|
||||
+ ON a.file_uuid = b.file_uuid
|
||||
+ AND a.frame_number = b.frame_number
|
||||
+ AND a.trace_id < b.trace_id
|
||||
+ WHERE a.file_uuid = %s
|
||||
+ AND a.trace_id IS NOT NULL
|
||||
+ AND b.trace_id IS NOT NULL
|
||||
+ ORDER BY a.frame_number
|
||||
+ """,
|
||||
+ (file_uuid,),
|
||||
+ )
|
||||
+ rows = cur.fetchall()
|
||||
+ if not rows:
|
||||
+ print("[TKG] No face-face co-occurrences found")
|
||||
+ return 0
|
||||
+
|
||||
+ # Deduplicate by pair (group all frames where same two traces co-occur)
|
||||
+ pair_first = {}
|
||||
+ pair_frames = {}
|
||||
+ for tid_a, tid_b, frame, ts, ax, ay, aw, ah, bx, by, bw, bh in rows:
|
||||
+ key = (min(tid_a, tid_b), max(tid_a, tid_b))
|
||||
+ if key not in pair_first:
|
||||
+ pair_first[key] = frame
|
||||
+ pair_frames.setdefault(key, []).append(frame)
|
||||
+
|
||||
+ edge_count = 0
|
||||
+ for (tid_a, tid_b), frames in pair_frames.items():
|
||||
+ cur.execute(
|
||||
+ f"SELECT id FROM {schema}.tkg_nodes WHERE file_uuid=%s AND node_type='face_trace' AND external_id=%s",
|
||||
+ (file_uuid, f"trace_{tid_a}"),
|
||||
+ )
|
||||
+ n_a = cur.fetchone()
|
||||
+ cur.execute(
|
||||
+ f"SELECT id FROM {schema}.tkg_nodes WHERE file_uuid=%s AND node_type='face_trace' AND external_id=%s",
|
||||
+ (file_uuid, f"trace_{tid_b}"),
|
||||
+ )
|
||||
+ n_b = cur.fetchone()
|
||||
+ if not n_a or not n_b:
|
||||
+ continue
|
||||
+
|
||||
+ distance_px = ((frames[0] - frames[0]) ** 2) ** 0.5 # placeholder
|
||||
+ ensure_edge(
|
||||
+ cur, schema, file_uuid,
|
||||
+ "CO_OCCURS_WITH",
|
||||
+ n_a[0], n_b[0],
|
||||
+ {
|
||||
+ "first_frame": int(frames[0]),
|
||||
+ "frame_count": len(frames),
|
||||
+ },
|
||||
+ )
|
||||
+ edge_count += 1
|
||||
+
|
||||
+ print(f"[TKG] {edge_count} face-face co-occurrence edges created")
|
||||
+ return edge_count
|
||||
+
|
||||
+
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Build Temporal Knowledge Graph")
|
||||
parser.add_argument("--file-uuid", required=True)
|
||||
@@ -382,17 +449,19 @@ def main():
|
||||
|
||||
e1 = build_co_occurrence_edges(cur, args.schema, args.file_uuid)
|
||||
e2 = build_speaker_face_edges(cur, args.schema, args.file_uuid)
|
||||
+ e3 = build_face_face_edges(cur, args.schema, args.file_uuid)
|
||||
|
||||
conn.commit()
|
||||
cur.close()
|
||||
conn.close()
|
||||
|
||||
- print(f"\n[TKG] Complete: {n1+n2+n3} nodes, {e1+e2} edges")
|
||||
+ print(f"\n[TKG] Complete: {n1+n2+n3} nodes, {e1+e2+e3} edges")
|
||||
print(f" Face traces: {n1}")
|
||||
print(f" Objects: {n2}")
|
||||
print(f" Speakers: {n3}")
|
||||
print(f" Co-occur: {e1}")
|
||||
print(f" Speaker-face:{e2}")
|
||||
+ print(f" Face-face: {e3}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
diff --git a/src/worker/job_worker.rs b/src/worker/job_worker.rs
|
||||
index 0f0ea1e..dceb674 100644
|
||||
--- a/src/worker/job_worker.rs
|
||||
+++ b/src/worker/job_worker.rs
|
||||
@@ -713,6 +713,7 @@ impl JobWorker {
|
||||
// Runs face_tracker.py (IoU+embedding tracking), stores trace_id + position in DB
|
||||
if has_face {
|
||||
info!("📝 Face completed, triggering face trace + DB store...");
|
||||
+ let db_clone = self.db.clone();
|
||||
let uuid_clone = uuid.to_string();
|
||||
tokio::spawn(async move {
|
||||
let executor = match crate::core::processor::PythonExecutor::new() {
|
||||
@@ -744,6 +745,41 @@ impl JobWorker {
|
||||
} else {
|
||||
info!("✅ Qdrant face sync completed for {}", uuid_clone);
|
||||
}
|
||||
+
|
||||
+ // Generate trace chunks from face_detections + ASR text
|
||||
+ info!("📝 Generating trace chunks...");
|
||||
+ match crate::core::chunk::trace_ingest::ingest_traces(
|
||||
+ &db_clone,
|
||||
+ &uuid_clone,
|
||||
+ )
|
||||
+ .await
|
||||
+ {
|
||||
+ Ok(n) => info!("✅ {} trace chunks created for {}", n, uuid_clone),
|
||||
+ Err(e) => error!("❌ Trace chunk ingestion failed: {}", e),
|
||||
+ }
|
||||
+
|
||||
+ // Build Temporal Knowledge Graph (TKG)
|
||||
+ info!("📝 Building TKG graph...");
|
||||
+ let executor = match crate::core::processor::PythonExecutor::new() {
|
||||
+ Ok(ex) => ex,
|
||||
+ Err(e) => {
|
||||
+ error!("Failed to create PythonExecutor for TKG: {}", e);
|
||||
+ return;
|
||||
+ }
|
||||
+ };
|
||||
+ match executor
|
||||
+ .run(
|
||||
+ "tkg_builder.py",
|
||||
+ &["--file-uuid", &uuid_clone],
|
||||
+ Some(&uuid_clone),
|
||||
+ "TKG_BUILDER",
|
||||
+ Some(std::time::Duration::from_secs(300)),
|
||||
+ )
|
||||
+ .await
|
||||
+ {
|
||||
+ Ok(()) => info!("✅ TKG built for {}", uuid_clone),
|
||||
+ Err(e) => error!("❌ TKG build failed for {}: {}", uuid_clone, e),
|
||||
+ }
|
||||
}
|
||||
Err(e) => {
|
||||
error!("❌ Face trace + DB store failed for {}: {}", uuid_clone, e)
|
||||
150
docs_v1.0/M5_workspace/release_pack.py
Normal file
150
docs_v1.0/M5_workspace/release_pack.py
Normal file
@@ -0,0 +1,150 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Release packaging — two non-overlapping phases.
|
||||
|
||||
Phase 1: ASR + ASRX + Rule 1 sentence chunks complete
|
||||
Phase 2: Full pipeline + Rule 3 + 5W1H complete
|
||||
|
||||
Output: release/phase{N}/v{VERSION}_{TIMESTAMP}/
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
|
||||
PROJECT = Path(__file__).resolve().parent.parent
|
||||
OUTPUT_DIR = Path(os.environ.get("MOMENTRY_OUTPUT_DIR", PROJECT / "output_dev"))
|
||||
RELEASE_DIR = PROJECT / "release"
|
||||
VERSION = "v1.0.0"
|
||||
|
||||
DB_USER = os.environ.get("USER", "accusys")
|
||||
DB_NAME = "momentry"
|
||||
QDRANT_URL = os.environ.get("QDRANT_URL", "http://localhost:6333")
|
||||
QDRANT_COLLECTION = os.environ.get("QDRANT_COLLECTION", "momentry_dev_rule1_v2")
|
||||
|
||||
|
||||
def ts():
|
||||
return datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
|
||||
|
||||
|
||||
def run_sql(sql: str) -> str:
|
||||
r = subprocess.run(
|
||||
["psql", "-U", DB_USER, "-d", DB_NAME, "-t", "-A", "-c", sql],
|
||||
capture_output=True, text=True, timeout=30,
|
||||
)
|
||||
return r.stdout.strip()
|
||||
|
||||
|
||||
def pack_phase(file_uuid: str, phase: int) -> Path:
|
||||
"""Package deliverables for phase 1 or 2."""
|
||||
phase_dir = RELEASE_DIR / f"phase{phase}"
|
||||
stamp = ts()
|
||||
pkg_dir = phase_dir / f"{VERSION}_{stamp}"
|
||||
out_dir = pkg_dir / "output_json"
|
||||
out_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# 收集 processor output .json 檔
|
||||
for f in OUTPUT_DIR.glob(f"{file_uuid}.*.json"):
|
||||
if f.is_file():
|
||||
shutil.copy2(f, out_dir / f.name)
|
||||
|
||||
# 收集 schema
|
||||
schema_path = pkg_dir / "schema.sql"
|
||||
with open(schema_path, "w") as fh:
|
||||
subprocess.run(
|
||||
["pg_dump", "-U", DB_USER, "-d", DB_NAME, "--schema=dev", "--schema-only",
|
||||
"-T", "dev.monitor_jobs", "-T", "dev.processor_results"],
|
||||
stdout=fh, text=True, timeout=60,
|
||||
)
|
||||
|
||||
# 收集 chunks
|
||||
chunks_csv = pkg_dir / "chunks.csv"
|
||||
run_sql(f"\\COPY (SELECT * FROM dev.chunks WHERE file_uuid='{file_uuid}') TO '{chunks_csv}' CSV HEADER")
|
||||
|
||||
# 收集 vectors
|
||||
vecs_csv = pkg_dir / "vectors.csv"
|
||||
run_sql(f"\\COPY (SELECT * FROM dev.chunk_vectors WHERE uuid='{file_uuid}') TO '{vecs_csv}' CSV HEADER")
|
||||
|
||||
if phase >= 2:
|
||||
faces_csv = pkg_dir / "face_detections.csv"
|
||||
run_sql(f"\\COPY (SELECT * FROM dev.face_detections WHERE file_uuid='{file_uuid}') TO '{faces_csv}' CSV HEADER")
|
||||
idents_csv = pkg_dir / "identities.csv"
|
||||
run_sql(f"\\COPY (SELECT * FROM dev.identities) TO '{idents_csv}' CSV HEADER")
|
||||
|
||||
# 匯出 Qdrant collection 快照
|
||||
import urllib.request
|
||||
qdrant_path = pkg_dir / "qdrant_points.jsonl"
|
||||
try:
|
||||
offset = None
|
||||
with open(qdrant_path, "w") as qf:
|
||||
while True:
|
||||
params = f"limit=1000&with_payload=true&with_vectors=true"
|
||||
if offset is not None:
|
||||
params += f"&offset={offset}"
|
||||
url = f"{QDRANT_URL}/collections/{QDRANT_COLLECTION}/points/scroll?{params}"
|
||||
req = urllib.request.Request(url)
|
||||
with urllib.request.urlopen(req, timeout=30) as resp:
|
||||
data = json.loads(resp.read())
|
||||
pts = data.get("result", {}).get("points", [])
|
||||
if not pts:
|
||||
break
|
||||
for p in pts:
|
||||
qf.write(json.dumps(p, ensure_ascii=False) + "\n")
|
||||
# 從回傳的 next_page_offset 取得下一頁偏移量
|
||||
offset = data.get("result", {}).get("next_page_offset")
|
||||
if offset is None:
|
||||
break
|
||||
n_points = sum(1 for _ in open(qdrant_path) if _.strip())
|
||||
print(f"[RELEASE] Qdrant: {n_points} points exported from '{QDRANT_COLLECTION}'")
|
||||
except Exception as e:
|
||||
print(f"[RELEASE] Qdrant export skipped: {e}")
|
||||
if qdrant_path.exists():
|
||||
qdrant_path.unlink()
|
||||
|
||||
# RELEASE_INFO
|
||||
git_commit = subprocess.run(
|
||||
["git", "-C", str(PROJECT), "rev-parse", "HEAD"],
|
||||
capture_output=True, text=True, timeout=10,
|
||||
).stdout.strip()
|
||||
|
||||
model_name = f"{file_uuid}_v1" if phase == 1 else f"{file_uuid}_v2"
|
||||
info = pkg_dir / "RELEASE_INFO.txt"
|
||||
with open(info, "w") as fh:
|
||||
fh.write(f"Model: {model_name}\n")
|
||||
fh.write(f"Phase: {phase}\n")
|
||||
fh.write(f"Version: {VERSION}\n")
|
||||
fh.write(f"Timestamp: {stamp}\n")
|
||||
fh.write(f"File UUID: {file_uuid}\n")
|
||||
fh.write(f"Qdrant Collection: {QDRANT_COLLECTION}\n")
|
||||
fh.write(f"Git Commit: {git_commit}\n")
|
||||
fh.write(f"Packaged at: {datetime.now(timezone.utc).isoformat()}\n")
|
||||
|
||||
# latest symlink
|
||||
latest = phase_dir / "latest"
|
||||
if latest.is_symlink():
|
||||
latest.unlink()
|
||||
if not latest.exists():
|
||||
latest.symlink_to(pkg_dir.name, target_is_directory=True)
|
||||
|
||||
size = sum(f.stat().st_size for f in pkg_dir.rglob("*") if f.is_file())
|
||||
print(f"[RELEASE] Phase {phase} packaged: {pkg_dir} ({size / 1024:.0f} KB)")
|
||||
return pkg_dir
|
||||
|
||||
|
||||
def main():
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--phase", type=int, required=True, choices=[1, 2])
|
||||
parser.add_argument("--file-uuid", required=True)
|
||||
args = parser.parse_args()
|
||||
|
||||
pack_phase(args.file_uuid, args.phase)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user