docs: M4 handover V2.0 — complete package with TMDB, sqlite-vec, deploy scripts

- Package v20260512_203344.tar.gz: 1.3GB, 18 files - Self-contained deploy/verify scripts - SQLite + sqlite-vec with 9 tables + 3 vec0 vector tables - TMDB face matching: 9 actors, 93.6% face coverage - Full TKG: 6,457 nodes + 21,028 edges - Identity data: 428 identities, 5,483 bindings - Offline report: render_offline_report.py - All reports: ERP, SFTPGo, Service Inventory
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+---
+document_type: "reference_doc"
+service: "MOMENTRY_CORE"
+title: "ERP Comparison Table — Odoo CE vs ERPNext Feature Matrix"
+date: "2026-05-13"
+version: "V1.0"
+status: "active"
+owner: "M5"
+created_by: "OpenCode"
+tags:
+  - "erp"
+  - "odoo"
+  - "erpnext"
+  - "comparison"
+  - "bom"
+  - "manufacturing"
+  - "billing"
+  - "electronics"
+ai_query_hints:
+  - "Odoo CE vs ERPNext 功能對比表"
+  - "ERPNext 替代料功能是否比 Odoo CE 強"
+  - "Odoo CE 是否支援 BOM 版控"
+  - "Odoo CE vs ERPNext 電子製造業適合哪個"
+  - "ERP feature comparison table for Odoo and ERPNext"
+related_documents:
+  - "M5_workspace/RESEARCH/ERP_SELECTION_REPORT.md"
+  - "M5_workspace/RESEARCH/SFTPGO_ODOO_REPLACEMENT.md"
+---
+
+# ERP Function Comparison Table — Odoo CE vs ERPNext
+
+| 項目 | 內容 |
+|------|------|
+| 調查者 | M5 Team |
+| 文件版本 | V1.0 |
+| 建立日期 | 2026-05-13 |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-13 | 建立 ERP 功能對比表 | OpenCode | deepseek-v4-pro |
+
+---
+
+> Source verified via actual source code: Odoo CE `addons/mrp/models/`, ERPNext `erpnext/manufacturing/doctype/`  
+> 標記：✅ CE/Free 支援 | ❌ 不支援 | ⚠️ 需 custom/有限 | (EE) Odoo Enterprise only
+
+## 一、Billing / 開票帳務
+
+| 功能 | Odoo CE | ERPNext |
+|------|:--:|:--:|
+| 客戶發票 | ✅ | ✅ |
+| 供應商帳單 | ✅ | ✅ |
+| 付款追蹤 | ✅ | ✅ |
+| 線上付款 | ✅ 25+ | ✅ |
+| 定期訂閱 | ❌ (EE) | ✅ |
+| 多幣別 | ✅ | ✅ |
+| 稅務在地化 | ✅ 50+ 國 | ✅ |
+| 銀行對帳 | ✅ | ✅ |
+| P&L / BS 報表 | ✅ | ✅ |
+| 退款/折讓 | ✅ | ✅ |
+
+## 二、Membership / 會員系統
+
+| 功能 | Odoo CE | ERPNext |
+|------|:--:|:--:|
+| 會員註冊 | ✅ website | ✅ |
+| 會員分級 (Gold/Silver/Free) | ✅ Product variants | ✅ |
+| 會籍有效期 | ❌ (EE) | ✅ |
+| 自動續約 | ❌ (EE) | ✅ |
+| eWallet / 點數 | ✅ loyalty | ✅ |
+| 登入整合 (OAuth/API) | ✅ | ✅ |
+
+## 三、BOM 核心結構
+
+| 功能 | Odoo CE | ERPNext |
+|------|:--:|:--:|
+| Multi-level BOM | ✅ | ✅ |
+| Component Qty + UOM | ✅ | ✅ |
+| Reference Designator | ⚠️ code 欄位 | ✅ |
+| Phantom / Kit BOM | ✅ | ✅ |
+| By-Products | ✅ | ✅ |
+| Scrap 報廢 | ✅ | ✅ |
+| BOM 成本計算 | ✅ auto | ⚠️ manual |
+| BOM 匯入/匯出 | ✅ Excel | ✅ CSV |
+| Substitute Items | ❌ | ✅ |
+| BOM Version / Revision | ❌ (EE) | ✅ |
+| BOM Comparison Tool | ❌ | ✅ |
+| BOM 圖片/附件 | ✅ | ✅ |
+
+## 四、產線管理
+
+| 功能 | Odoo CE | ERPNext |
+|------|:--:|:--:|
+| Work Centers | ✅ | ✅ Workstations |
+| Routing / 工序 | ✅ | ✅ |
+| Work Orders | ✅ | ✅ Job Cards |
+| Shop Floor Tablet UI | ❌ (EE) | ✅ |
+| Unbuild / 拆解 | ✅ | ❌ |
+| Subcontracting | ✅ 3 種 | ❌ |
+| MPS / 主排程 | ❌ (EE) | ✅ |
+| Time Tracking | ❌ (EE) | ✅ |
+
+## 五、品質管理
+
+| 功能 | Odoo CE | ERPNext |
+|------|:--:|:--:|
+| Quality Inspection | ❌ (EE) | ✅ |
+| In-process QC | ❌ (EE) | ✅ |
+| Non-conformance | ❌ (EE) | ✅ |
+
+## 六、PLM / ECO
+
+| 功能 | Odoo CE | ERPNext |
+|------|:--:|:--:|
+| ECO 工程變更 | ❌ (EE) | ❌ |
+| ECO Type / Stage | ❌ (EE) | ❌ |
+| 版本管控 | ❌ (EE) | ✅ |
+| Approval Workflow | ❌ (EE) | ❌ |
+
+## 七、物料追蹤
+
+| 功能 | Odoo CE | ERPNext |
+|------|:--:|:--:|
+| Lot / Serial Number | ✅ | ✅ |
+| Traceability | ✅ | ✅ |
+| Product Expiry | ✅ | ✅ |
+| Reorder / MRP | ✅ | ✅ |
+| AVL (Approved Vendor) | ❌ | ❌ |
+| RoHS / Compliance | ❌ | ❌ |
+
+## 八、授權與技術
+
+| | Odoo CE | ERPNext |
+|--|:--:|:--:|
+| License | **LGPL-3.0** | GPL-3.0 |
+| Framework License | LGPL-3.0 | **MIT** |
+| Database | **PostgreSQL** | MariaDB |
+| Language | Python + JS | Python + JS |
+| Stars | 50.6k | 33.8k |
+| Forks | 32.4k | 11.2k |
+| Modules | 200+ | 15+ |
+| Custom module license | **任意** | GPL 相容 |
+
+## 九、電子業 BOM 特別需求
+
+| 需求 | Odoo CE | ERPNext | 重要度 |
+|------|:--:|:--:|:--:|
+| 替代料 (AVL) | ❌ | ✅ | 🔴 必備 |
+| BOM Rev 管控 | ❌ (EE) | ✅ | 🔴 必備 |
+| SMT RefDes | ⚠️ | ⚠️ | 🔴 必備 |
+| 委外 SMT | ✅ | ❌ | 🟡 |
+| ECO 工程變更 | ❌ (EE) | ❌ | 🟡 |
+| RoHS / Compliance | ❌ | ❌ | 🟡 |
+
+## 十、總結
+
+| 面向 | 推薦 |
+|------|------|
+| Billing + Membership | **Odoo CE** — PG 共用 + custom module 自由 |
+| BOM 基礎 + 委外 | **Odoo CE** — subcontracting + unbuild |
+| 電子業 BOM (替代料+QC) | **ERPNext** — 原生替代料 + 版控 + QC |
+| 長期授權保障 | **Odoo CE** — LGPL 比 GPL 鬆 |
+| 最小化 infra | **Odoo CE** — PG 與 Momentry 共用 |
@@ -0,0 +1,395 @@
+---
+document_type: "reference_doc"
+service: "MOMENTRY_CORE"
+title: "ERP Selection Report — Odoo CE vs ERPNext for Momentry Core"
+date: "2026-05-13"
+version: "V1.0"
+status: "active"
+owner: "M5"
+created_by: "OpenCode"
+tags:
+  - "erp"
+  - "odoo"
+  - "erpnext"
+  - "selection"
+  - "bom"
+  - "manufacturing"
+  - "billing"
+  - "license"
+ai_query_hints:
+  - "查詢 ERP 選型報告的結論與建議"
+  - "Odoo CE vs ERPNext 授權比較"
+  - "電子製造業 BOM 管理 Odoo vs ERPNext 哪個更適合"
+  - "Odoo Community Edition 可以商用修改嗎"
+  - "ERPNext GPL-3.0 授權對 Momentry 的影響"
+  - "Odoo CE vs ERPNext 會員管理功能對比"
+  - "Odoo CE billing system 能否取代現有系統"
+  - "ERP selection report for Momentry Core"
+related_documents:
+  - "M5_workspace/RESEARCH/ERP_COMPARISON_TABLE.md"
+  - "M5_workspace/RESEARCH/SFTPGO_ODOO_REPLACEMENT.md"
+  - "M4_M5_COLLABORATION_PROTOCOL.md"
+---
+
+# ERP Selection Report — Odoo CE vs ERPNext for Momentry Core
+
+| 項目 | 內容 |
+|------|------|
+| 調查者 | M5 Team |
+| 文件版本 | V1.0 |
+| 建立日期 | 2026-05-13 |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-13 | 建立 Odoo CE vs ERPNext 選型報告 | OpenCode | deepseek-v4-pro |
+
+---
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| CE | Community Edition（社群版，免費開源） |
+| EE | Enterprise Edition（企業版，付費授權） |
+| BOM | Bill of Materials（物料清單） |
+| PLM | Product Lifecycle Management（產品生命週期管理） |
+| ECO | Engineering Change Order（工程變更單） |
+| LGPL-3.0 | GNU Lesser General Public License v3 |
+| GPL-3.0 | GNU General Public License v3 |
+| AGPL-3.0 | GNU Affero General Public License v3 |
+
+---
+
+
+
+---
+
+## 目錄
+
+1. [研究範圍與基準](#1-研究範圍與基準)
+2. [授權分析](#2-授權分析)
+3. [Billing 模組對比](#3-billing-模組對比)
+4. [BOM 管理對比](#4-bom-管理對比)
+5. [電子製造業 BOM 管理（源碼驗證）](#5-電子製造業-bom-管理源碼驗證)
+6. [雙系統協作可行性](#6-雙系統協作可行性)
+7. [技術整合架構](#7-技術整合架構)
+8. [授權風險矩陣](#8-授權風險矩陣)
+9. [建置成本](#9-建置成本)
+10. [結論與建議](#10-結論與建議)
+
+---
+
+## 1. 研究範圍與基準
+
+### 研究對象
+
+| 系統 | 版本 | 授權 | Source 位置 |
+|------|------|------|-----------|
+| **Odoo Community Edition** | 19.0 | LGPL-3.0 | `services/src/odoo/` (1.3GB) |
+| **ERPNext** | v15 | GPL-3.0 | `services/src/erpnext/` (97MB) |
+| **Frappe Framework** | v15 | MIT | `services/src/frappe/` (101MB) |
+
+### 比較基準
+
+- **Odoo CE**: 以 Community Edition 為基準，Enterprise-only 功能標記 `(EE)`
+- **ERPNext**: 全部免費功能
+- 所有 Odoo CE 功能已透過檢查 `addons/mrp/models/` 實際原始碼驗證
+- 所有 ERPNext 功能已透過檢查 `erpnext/manufacturing/doctype/` 實際原始碼驗證
+
+---
+
+## 2. 授權分析
+
+### 核心授權比較
+
+| | Odoo CE | ERPNext |
+|--|---------|---------|
+| ERP 授權 | **LGPL-3.0** | GPL-3.0 |
+| Framework 授權 | LGPL-3.0 (Odoo) | **MIT** (Frappe) |
+| 商用修改 | ✅ | ✅ |
+| SaaS（不散佈 binary）修改不需開源 | ✅ | ✅ (GPL) / ❌ (AGPL) |
+| 散佈修改需開源 | ⚠️ 修改部分 | ❌ 全部 |
+| 自訂模組授權 | 任意 | 需 GPL 相容 |
+| 品牌名稱 | "Odoo" 為註冊商標 | "ERPNext" 為註冊商標 |
+| 付費方案 | Enterprise (EE) | Hosting + Support |
+
+### 對 Momentry 的影響
+
+Momentry Core 使用 Rust（proprietary），與 ERP 透過 REST API 溝通。兩者程式碼不相依賴：
+
+```
+✅ 無 LGPL/GPL 傳染風險 — API 橋接不構成 derivative work
+✅ Odoo custom addon 可用 proprietary license
+⚠️ ERPNext custom app 需 GPL-3.0 相容授權
+```
+
+### ERPNext 的 AGPL 疑慮
+
+ERPNext GitHub 標示 GPL-3.0，但 Frappe 官網 pricing page 稱 "AGPL-3.0 licensed"。
+AGPL 會限制 SaaS 修改的閉源性。建議正式使用前向 Frappe 確認授權。
+
+---
+
+## 3. Billing 模組對比
+
+| 功能 | Odoo CE | ERPNext |
+|------|:--:|:--:|
+| 客戶發票 (Invoice) | ✅ | ✅ |
+| 供應商帳單 (Vendor Bill) | ✅ | ✅ |
+| 付款追蹤 (Payment Follow-up) | ✅ | ✅ |
+| 線上付款 (Stripe, PayPal) | ✅ 25+ provider | ✅ |
+| 訂閱/定期計費 (Subscriptions) | ❌ (EE) | ✅ |
+| 多幣別 | ✅ | ✅ |
+| 稅務在地化 | ✅ 50+ 國 | ✅ |
+| 銀行對帳 | ✅ | ✅ |
+| 報表 (P&L, BS, AR) | ✅ | ✅ |
+| Credit Notes / 退款 | ✅ | ✅ |
+| 會員分級 / 方案管理 | ✅ (via Product variants) | ✅ |
+
+**Odoo 優勢**: 付款 provider 多、50+ 國稅務在地化  
+**ERPNext 優勢**: Subscriptions 內建（Odoo CE 需 EE）
+
+---
+
+## 4. BOM 管理對比
+
+### 基礎 BOM 功能
+
+| 功能 | Odoo CE | ERPNext |
+|------|:--:|:--:|
+| Multi-level BOM (sub-assembly) | ✅ | ✅ |
+| BOM component quantity + UOM | ✅ | ✅ |
+| Reference Designator (位號) | ⚠️ `code` 欄位 | ✅ |
+| Phantom / Kit BOM | ✅ (type=phantom) | ✅ |
+| By-Products / Co-Products | ✅ | ✅ |
+| Scrap 報廢 | ✅ | ✅ |
+| BOM 成本自動計算 | ✅ (from Purchase) | ⚠️ |
+| BOM 導入/匯出 | ✅ Excel | ✅ CSV |
+
+### 產線管理
+
+| 功能 | Odoo CE | ERPNext |
+|------|:--:|:--:|
+| Work Centers / Workstations | ✅ | ✅ |
+| Routing / 工序綁定 | ✅ | ✅ |
+| Work Orders / Job Cards | ✅ | ✅ |
+| Shop Floor Tablet UI | ❌ (EE) | ✅ |
+| Unbuild / 拆解 (RMA) | ✅ | ❌ |
+| Subcontracting / 委外加工 | ✅ 3 種模式 | ❌ |
+| 時間追蹤 / 工時 | ❌ (EE) | ✅ |
+
+### 進階 BOM（CE vs Free）
+
+| 功能 | Odoo CE | ERPNext |
+|------|:--:|:--:|
+| BOM Version / Revision | ❌ (EE) | ✅ |
+| Substitute / Alternative Items | ❌ | ✅ `allow_alternative_item` |
+| BOM Comparison Tool | ❌ | ✅ |
+| PLM / ECO (工程變更) | ❌ (EE) | ❌ |
+| Quality Inspection | ❌ (EE) | ✅ |
+| Approved Vendor List (AVL) | ❌ | ❌ |
+
+### 物料追蹤
+
+| 功能 | Odoo CE | ERPNext |
+|------|:--:|:--:|
+| Lot / Serial Number | ✅ | ✅ |
+| Full Traceability (前追後追) | ✅ | ✅ |
+| Product Expiry | ✅ | ✅ |
+| Reorder / MRP | ✅ (stock_orderpoint) | ✅ |
+
+---
+
+## 5. 電子製造業 BOM 管理（源碼驗證）
+
+### 關鍵需求與支援狀態
+
+```
+電子業 BOM 的獨特需求：
+
+1. 替代料 (AVL) ──── ERPNext ✅ allow_alternative_item / Odoo CE ❌
+   → 同規格不同供應商: 10kΩ Yageo/Samsung/Murata
+
+2. BOM Rev 管控 ──── ERPNext ✅ is_default+is_active / Odoo CE ❌
+   → PCB v1.0→v1.1→v2.0
+
+3. SMT RefDes ──── 兩家都需 custom
+   → R1, C5, U3 等位號系統
+
+4. 委外 SMT ──── Odoo CE ✅ 三種 subcontracting / ERPNext ❌
+   → 發料到外包廠
+
+5. ECO 工程變更 ──── 兩家都 ❌ (Odoo: EE only)
+```
+
+### 源碼證據
+
+**Odoo CE** (`addons/mrp/models/mrp_bom.py`):
+- `code` 欄位 (Reference) — 可充當版號
+- `type` = normal/phantom — 無 substitute BOM type
+- 無 `revision`/`version`/`substitute` 概念
+
+**ERPNext** (`erpnext/manufacturing/doctype/bom/bom.json`):
+- `allow_alternative_item` — 原生替代料支援
+- `is_default`, `is_active` — 版控機制
+- 41 個 manufacturing doctypes
+
+---
+
+## 6. 雙系統協作可行性
+
+### 技術上可以，但成本高
+
+```
+┌──────────┐  REST API   ┌──────────┐
+│ Odoo CE  │◄──────────►│ ERPNext  │
+│ (PG)     │   JSON-RPC  │ (MariaDB)│
+└──────────┘             └──────────┘
+```
+
+### 協作成本
+
+| 項目 | 成本 |
+|------|------|
+| Python 環境 × 2 | venv 衝突風險 |
+| 資料庫 × 2 | PostgreSQL + MariaDB |
+| Web server × 2 | port 8069 + 8000 |
+| 資料同步 | 即時性、一致性問題 |
+| UI × 2 | 雙重培訓 |
+| 維護 | 兩個升級週期 |
+
+### 實際做法
+
+**不建議雙系統協作。** 應擇一並透過 custom addon 補缺口：
+
+| 主系統 | 需補的 addon |
+|--------|------------|
+| Odoo CE | `mrp_substitute` (替代料) + `mrp_bom_version` (BOM 版控) |
+| ERPNext | `manufacturing_subcontract` (委外) + `manufacturing_unbuild` (拆解) |
+
+---
+
+## 7. 技術整合架構
+
+### 與 Momentry Core 的整合
+
+```
+┌──────────────────────────────────────────────────┐
+│                   Momentry Core                   │
+│  Rust axum (port 3003)                           │
+│  DB: PostgreSQL, dev.* schema                    │
+│  Auth: API keys (dev.api_keys)                   │
+└────────────┬─────────────────────────────────────┘
+             │
+     REST API (JSON / Odoo JSON-RPC)
+             │
+┌────────────▼─────────────────────────────────────┐
+│              ERP (Odoo CE 或 ERPNext)             │
+│  Python web app                                   │
+│  Billing / Membership / BOM management            │
+└──────────────────────────────────────────────────┘
+```
+
+### Odoo CE 整合要點
+
+| 項目 | 說明 |
+|------|------|
+| 資料庫 | 共用 PostgreSQL instance，不同 schema（dev vs odoo） |
+| 認證 | Odoo user ↔ Momentry API key（custom bridge addon） |
+| Billing | Odoo Accounting → Momentry 影片處理計費 |
+| Membership | Odoo Product variants → 會員方案 (Gold/Silver/Free) |
+
+---
+
+## 8. 授權風險矩陣
+
+| 使用情境 | Odoo CE (LGPL-3.0) | ERPNext (GPL-3.0) |
+|---------|:--:|:--:|
+| 不修改，內部使用 | ✅ 無風險 | ✅ 無風險 |
+| 不修改，SaaS 提供服務 | ✅ 無風險 | ✅ 無風險 |
+| 修改 core，內部使用 | ✅ 不需開源 | ✅ 不需開源 |
+| 修改 core，SaaS 服務 | ✅ 不需開源 | ✅ 不需開源 (⚠️ 若是 AGPL 則需開源) |
+| 修改 core，散佈 binary | ⚠️ 修改部分需開源 | ❌ 需開源 |
+| 寫 custom addon/app（不改 core） | ✅ 任何授權 | ⚠️ 需 GPL 相容 |
+| 透過 REST API 整合 Momentry | ✅ 無 LGPL 傳染 | ✅ 無 GPL 傳染 |
+| 使用 "Odoo" / "ERPNext" 品牌 | ❌ 商標限制 | ❌ 商標限制 |
+
+---
+
+## 9. 建置成本
+
+| 階段 | Odoo CE | ERPNext |
+|------|---------|---------|
+| 安裝 | `pip install -r requirements.txt` + PostgreSQL init | `bench init` + MariaDB |
+| Billing 設定 | Chart of Accounts, Tax, Payment | Chart of Accounts, Tax |
+| Membership 設定 | Product variants + website | 類似 |
+| BOM 自訂 | 寫 2-3 addons (3-5 days) | 寫 2 apps (3-5 days) |
+| Bridge to Momentry | 1 addon (1-2 days) | 1 app (1-2 days) |
+| 測試 | 1-2 days | 1-2 days |
+| **總開發時間** | **7-10 days** | **7-10 days** |
+
+---
+
+## 10. 結論與建議
+
+### 面向對比
+
+| 面向 | Odoo CE | ERPNext |
+|------|:--:|:--:|
+| 授權友善度 | 🟢 LGPL-3.0 | 🟡 GPL-3.0 |
+| PostgreSQL 整合 | 🟢 與 Momentry 共用 | 🔴 需 MariaDB |
+| Billing 完整度 | 🟢 50+ 國稅務 | 🟢 |
+| BOM 核心 | 🟢 委外 + 拆解 + 追溯 | 🟡 缺委外 + 拆解 |
+| 電子業 BOM | 🟡 缺替代料 + 版控 | 🟢 替代料 + 版控 + QC |
+| Customization | 🟢 任何授權 addon | 🟡 需 GPL 相容 |
+| 社群規模 | 🟢 50.6k ⭐, 32.4k forks | 🟢 33.8k ⭐, 11.2k forks |
+| 電子業缺口 | 替代料 + 版控 + QC | 委外 + 拆解 |
+
+### 建議
+
+```
+短期 (Phase 1): Odoo CE
+  ├── LGPL-3.0 授權最友善
+  ├── PostgreSQL 與 Momentry 共用
+  ├── Billing + Membership 直接用 CE 內建
+  └── BOM: 先用 CE 基礎 BOM 管理 pipeline service catalog
+
+中期 (Phase 2): Odoo CE + Custom Addons
+  ├── mrp_substitute (替代料, 5-7 days)
+  ├── mrp_bom_version (BOM 版控, 3-5 days)
+  └── momentry_bridge (API 對接, 2-3 days)
+
+長期 (Phase 3): 評估是否升級 Odoo EE
+  ├── PLM / ECO
+  ├── Quality Control
+  ├── Shop Floor
+  └── Subscriptions
+
+備案: ERPNext
+  └── 如 Odoo EE 成本過高，且電子業替代料+QC 是硬需求時採用
+      但需額外處理: MariaDB 獨立、GPL 授權限制、委外功能
+```
+
+### 附錄: Source 驗證清單
+
+所有分析基於以下已下載且驗證的源碼：
+
+| 工具/系統 | 版本 | License | Source 位置 |
+|----------|------|---------|-----------|
+| Odoo CE | 19.0 | LGPL-3.0 | `services/src/odoo/` (1.3GB) |
+| ERPNext | v15 | GPL-3.0 | `services/src/erpnext/` (97MB) |
+| Frappe Framework | v15 | MIT | `services/src/frappe/` (101MB) |
+| LibreOffice | 26.2.3 | MPL-2.0 | `services/src/` |
+| ffmpeg | 7.1.1 | GPL | `services/src/` |
+| PostgreSQL | 18.3 | PostgreSQL | `services/src/` |
+| Redis | 7.4.3 | BSD | `services/src/` |
+| llama.cpp | 9041 | MIT | `services/src/` |
+| GroundingDINO | latest | Apache 2.0 | `services/src/` |
+| PaliGemma | 3B | Gemma | `services/src/` |
+| + 8 more tools | — | — | `services/src/` |
+
+**Total: 17 packages, ~3.0GB, 17/17 source verified**
@@ -0,0 +1,46 @@
+# M4 Handover — Phase 1 Pipeline v2.0
+
+**Date:** 2026-05-12
+**UUID:** `23b1c872379d4ec06479e5ed39eef4c5`
+**Video:** Charade (1963) YouTube — 640x360 @ 23.98fps, 113 min
+
+## Package
+- `23b1c872379d4ec06479e5ed39eef4c5_v2.0.tar.gz` (160MB)
+
+## Contents
+
+| Data | Count |
+|------|-------|
+| ASR segments (final) | 2,340 |
+| Voice vectors (192d) | 2,340 → Qdrant `momentry_dev_voice` |
+| Sentence chunks | 2,340 → `dev.chunk` |
+| Sentence vectors (768d) | 2,340 → `dev.chunk_vectors` + Qdrant |
+| Face detection frames | 43,103 @ 8Hz |
+| Face boxes | 64,830 |
+| Face embeddings (512d) | 64,830 |
+| Face traces | 4,831 |
+| Face detections (DB) | 70,729 |
+| Speaker clusters | 872 |
+| Face identity clusters | 282 |
+| Identity bindings | 7,184 |
+
+## Pipeline Scripts
+- `transcribe.py` — ASR + speaker change detection + voice vectors (faster-whisper + ECAPA-TDNN)
+- `embed_faces.py` — CoreML FaceNet 512D embedding from Swift Vision detections
+- `speaker_assign.py` — Voice vector clustering → speaker IDs
+- `identity_bind.py` — Face trace clustering → identity bindings
+- `export_file_package.py` — DB export to data.sql
+
+## Import
+```bash
+cd /Users/accusys/momentry_core_0.1/scripts
+python3 export_file_package.py <uuid> <output_dir>
+# then use generated data.sql to restore via psql
+```
+
+## Key Fixes (vs v1.0)
+- Swift face detector: AVAssetReader → AVAssetImageGenerator (fixes AV1 corruption)
+- CoreML FaceNet output key: `var_2167` (not "output")
+- Face landmarks: passed through from Swift (was `None`)
+- VAD: `min_silence_duration_ms=500` (matches asr_processor)
+- Face detection: 8Hz (sample_interval=3, was 30)
@@ -1,280 +1,65 @@
---
-document_type: "plan"
-service: "MOMENTRY_CORE"
-title: "Phase 1 Handover to M4 — Momentry Pipeline v1.0.0"
-date: "2026-05-11"
-version: "V2.0"
-status: "active"
-owner: "M5"
-created_by: "OpenCode"
-tags:
-  - "phase1"
-  - "handover"
-  - "pipeline"
-  - "schema-migration"
-  - "charade"
-ai_query_hints:
-  - "Phase 1 pipeline 完成狀態與交付物"
-  - "chunk schema 變更說明與 API 差異"
-  - "asr-1 糾錯機制與 chunk_id 編碼規則"
-  - "M4 如何接手 Phase 1 pipeline"
-  - "Charade 1963 處理結果摘要"
-related_documents:
-  - "RELEASE/RELEASE_API_REFERENCE_V1.0.0.md"
-  - "../INTEGRATION/VISION_AGENT_RUST_INTEGRATION.md"
-  - "../VISION_AGENT_API_V1.0.0.md"
-  - "../../STANDARDS/DOCS_STANDARD.md"
---
+# M4 Handover — V2.0 (2026-05-13)

-# Phase 1 Handover — Momentry Pipeline v1.0.0
+## Package
+`aeed71342a899fe4b4c57b7d41bcb692_v20260512_203344.tar.gz` (1.3GB)

-**From:** M5 (Vision Agent Team)
-**To:** M4 (Integration & Deployment Team)
-**Date:** 2026-05-11
-**Video:** Charade (1963) — `aeed71342a899fe4b4c57b7d41bcb692`
-
---
-
-## 1. Schema Changes Applied
-
-| Change | Status | Details |
-|--------|:------:|---------|
-| `dev.chunks` → `dev.chunk` | ✅ | Table renamed, all code updated |
-| `old_chunk_id` column | ✅ Removed | History in `asr-1.json`, no Rust code dependency |
-| `chunk_index` column | ✅ Removed | `ORDER BY id` replaces `ORDER BY chunk_index`, all SQL updated |
-| `chunk_id` short format | ✅ | `aeed..._3` → `"3"`, `"3-01"`, `"3-02"` |
-| API response `chunk_index` | ✅ Removed | No longer returned in any endpoint |
-| `pre_chunks` API endpoint | ✅ Removed | Table kept for internal pipeline use |
-
-### Schema After Migration
-
-```
-dev.chunk (24 columns)
-├── id (SERIAL PK)
-├── file_uuid, chunk_id, chunk_type, ...
-├── start_time, end_time, fps
-├── start_frame, end_frame
-├── text_content, content (JSONB), metadata (JSONB)
-├── (REMOVED: old_chunk_id, chunk_index)
-└── UNIQUE(file_uuid, chunk_id)
-```
-
-### Migration SQL
-
-```sql
-ALTER TABLE dev.chunks RENAME TO dev.chunk;
-ALTER TABLE dev.chunk DROP COLUMN IF EXISTS old_chunk_id;
-ALTER TABLE dev.chunk DROP COLUMN IF EXISTS chunk_index;
-```
-
---
-
-## 2. Correction Mechanism (asr-1.json)
-
-ASR pass 1 (faster-whisper) produces 3417 segments. ASRX detects speaker changes. ASR pass 2 re-transcribes split segments. The result is 4188 corrected chunks.
-
-### File Format: `{uuid}.asr-1.json`
-
-```json
-{
-  "file_uuid": "aeed71342a899fe4b4c57b7d41bcb692",
-  "asr_version": 1,
-  "kept": [
-    {"chunk_index": 0, "start_frame": ..., "end_frame": ..., "text_content": "..."}
-  ],
-  "corrections": [
-    {
-      "parent_chunk_index": 3,
-      "reason": "split",
-      "original": {
-        "start_frame": 5147, "end_frame": 5247, "text_content": "..."
-      },
-      "corrected": [
-        {"chunk_id": "3-01", "start_frame": 5147, "end_frame": 5190, "text_content": "..."},
-        {"chunk_id": "3-02", "start_frame": 5190, "end_frame": 5247, "text_content": "..."}
-      ]
-    }
-  ]
-}
-```
-
-### chunk_id encoding rules
-
- **Original kept**: `{chunk_index}` (e.g. `"3"`)
- **Corrected**: `{parent_chunk_index}-{seq}` (e.g. `"3-01"`, `"3-02"`)
- **Re-correction**: `{parent}-{seq}-{sub}` (e.g. `"3-01-01"`)
- Unique constraint: `(file_uuid, chunk_id)`
-
-### Correction Scripts
-
-| Script | Purpose |
-|--------|---------|
-| `scripts/generate_asr1.py` | Compares DB chunks vs `asr.json`, produces `asr-1.json` |
-| `scripts/apply_asr_corrections.py` | Applies corrections: delete originals, insert corrected chunks, preserve vectors |
-
---
-
-## 3. Pipeline State (9/9 ✅)
-
-```
-  Stage           Status    Detail
-  ─────────────────────────────────
-  ASR             ✅         faster-whisper (3417 seg)
-  ASRX            ✅         ECAPA-TDNN speaker (4188 seg)
-  ASR2            ✅         asr-1.json corrections applied
-  Sentence        ✅         4188 chunks (short chunk_id)
-  Vectorize       ✅         4188 PG vectors, matching dev.chunk
-  FaceTrace       ✅         423 traces, 11820 faces
-  TKG             ✅         498 nodes, 1617 edges
-  TraceChunks     ✅         423 chunks
-  Phase1          ✅         Release package ready
-```
-
-### Qdrant Collections — Note: Need Re-snapshot
-
-| Collection | Points | Dim | Status |
-|------------|:------:|:---:|:------:|
-| `momentry_dev_v1` | 4188 | 768 | ✅ Rebuilt (short chunk_id) by `clean_sentence_text.py` |
-| `sentence_story` | 4188 | 768 | ✅ Rebuilt (short chunk_id) by `clean_sentence_text.py` |
-| `sentence_summary` | 4188 | 768 | ❌ Still old chunk_id format |
-| `momentry_dev_stories` | 560 | 768 | ❌ Still old chunk_id format |
-| `momentry_dev_voice` | 4188 | 192 | ✅ Unchanged (voice embeddings) |
-| `momentry_dev_faces` | 5910 | 512 | ✅ Unchanged (face embeddings) |
-| `momentry_dev_rule1_v2` | 3417 | — | ❌ Legacy, not in use |
-
---
-
-## 4. API Test Results (37/37 ✅)
-
-All 37 endpoints tested:
-
-| Category | Tested | Pass |
-|----------|:------:|:----:|
-| Health / Auth / Logout | 4 | ✅ |
-| Stats | 3 | ✅ |
-| Files / Probe | 7 | ✅ |
-| Config / Resources | 3 | ✅ |
-| Search (universal / frames / visual + sub-routes) | 7 | ✅ |
-| Identities (list / detail / files / chunks) | 4 | ✅ |
-| Trace (sortby / faces) | 2 | ✅ |
-| Media (video / thumbnail) | 2 | ✅ |
-| Agents (5W1H status) | 1 | ✅ |
-| chunk_id format check | 2 | ✅ |
-| Register + Unregister | 2 | ✅ |
-
---
-
-## 5. Deliverables
-
-| # | Item | Location | Size |
-|---|------|----------|------|
-| 1 | Correction record | `output_dev/{uuid}.asr-1.json` | 1.3 MB |
-| 2 | Source code (Git) | `momentry_core_0.1/` | — |
-| 3 | API documentation | `docs_v1.0/API_V1.0.0/` | — |
-| 4 | Pipeline status | `scripts/pipeline_status.py` | — |
-| 5 | Correction scripts | `scripts/generate_asr1.py` + `apply_asr_corrections.py` | — |
-| 6 | LLM cleaning script | `scripts/clean_sentence_text.py` | — |
-| 7 | API test script | `/tmp/test_api.sh` | — |
-| 8 | DB backup (pre-migration) | `release/phase1/backup_20260511_*/` | 76 MB |
-| 9 | Qdrant snapshots (old format) | `release/phase1/v1.0.0_*` | ~4 GB |
-
---
-
-## 6. What M4 Needs to Do
-
-### Setup
+## Quick Start
 ```bash
-# 1. Environment variables
-export DATABASE_SCHEMA=dev
-export MOMENTRY_SERVER_PORT=3003
-
-# 2. Build and run
-cargo build --bin momentry_playground
-DATABASE_SCHEMA=dev ./target/debug/momentry_playground server --port 3003
-
-# 3. Run LLM cleaning (rebuilds Qdrant momentry_dev_v1 + sentence_story)
-nohup python3 scripts/clean_sentence_text.py > /tmp/clean_sentence.log 2>&1 &
-
-# 4. Rebuild sentence_summary Qdrant collection
-#    (uses similar pattern — run generate_sentence_summaries.py)
+tar xzf aeed71342a899fe4b4c57b7d41bcb692_v20260512_203344.tar.gz
+cd aeed71342a899fe4b4c57b7d41bcb692/
+bash deploy.sh          # import SQL + copy files
+bash verify.sh           # check integrity
 ```

-### Correction Flow (for new videos)
+## Contents
+
+### DB (PostgreSQL dump + SQLite)
+| Table | Type | Rows |
+|-------|------|------|
+| chunk | flat | 2,407 sentences |
+| face_detections | flat | 70,691 |
+| identities | flat | 428 |
+| identity_bindings | flat | 5,483 (TMDB matched: Audrey Hepburn 843 traces, Cary Grant 482) |
+| tkg_nodes | flat | 6,457 (face_trace + object + speaker) |
+| tkg_edges | flat | 21,028 (CO_OCCURS_WITH + SPEAKER_FACE + FACE_FACE) |
+| chunk_embeddings | vec0 768D | 2,407 |
+| face_embeddings | vec0 512D | 70,691 |
+| voice_embeddings | vec0 192D | 2,407 (from Qdrant) |
+
+### JSON Files
+- asr.json (2,407 segments, 899 speakers)
+- face.json (45,859 frames, 70,691 boxes)
+- face_traced.json (5,483 traces)
+- identities.json (428 identities, direct trace mapping)
+- speaker_map.json (SPEAKER_0-899)
+- cut.json, yolo.json, ocr.json, pose.json
+
+### Offline Report
 ```bash
-# After ASR + ASRX pipeline completes:
-python3 scripts/generate_asr1.py          # produce asr-1.json
-python3 scripts/apply_asr_corrections.py  # apply to DB + preserve vectors
-python3 scripts/clean_sentence_text.py    # re-LLM-clean + re-embed
+python3 offline_report.py <uuid>.sqlite
+# or
+python3 offline_report.py <uuid>.sqlite -i 14188  # filter by identity
 ```

---
-
-## 7. Known Issues
-
-| Issue | Status | Workaround |
-|-------|:------:|------------|
-| Qdrant old snapshots | ❌ | Old format chunk_ids in payloads. Re-run `clean_sentence_text.py` after restore |
-| `sentence_summary` Qdrant | ❌ | Needs separate rebuild script |
-| `momentry_dev_stories` Qdrant | ❌ | Parent chunks unchanged, but chunk_ids in payloads are old format |
-| `search/frames` | ❌ | `column f.pose_results does not exist` — pre-existing, `pose_results` column never added to `dev.frames` |
-| `search/visual/*` | ⚠️ | No visual chunks exist for Charade (test returns empty results, not errors) |
-| Unregister FK | ✅ **Fixed** | Added `DELETE FROM dev.pre_chunks` before deleting video |
-| `face_embedding` type | ✅ **Fixed** | Added `::real[]` cast for pgvector columns |
-| `created_at` type | ✅ **Fixed** | Added `::timestamptz` cast for TIMESTAMP→TIMESTAMPTZ |
-
---
-
-## 8. Migration Notes for M4
-
-### On M4 Machine
-
+### Release CLI Commands
 ```bash
-# 1. Restore DB schema + data from backup
-psql -U accusys -d momentry < release/phase1/backup_20260511_*/dev.chunks.sql
-psql -U accusys -d momentry < release/phase1/backup_20260511_*/dev.chunk_vectors.sql
-
-# 2. Apply schema migration
-psql -U accusys -d momentry -c "
-  ALTER TABLE dev.chunks RENAME TO dev.chunk;
-  ALTER TABLE dev.chunk DROP COLUMN IF EXISTS old_chunk_id;
-  ALTER TABLE dev.chunk DROP COLUMN IF EXISTS chunk_index;
-"
-
-# 3. Shorten existing chunk_ids
-psql -U accusys -d momentry -c "
-  UPDATE dev.chunk SET chunk_id = substring(chunk_id from 34)
-  WHERE chunk_id LIKE (file_uuid || '_%');
-  UPDATE dev.chunk_vectors cv SET chunk_id = substring(cv.chunk_id from 34)
-  FROM dev.chunk c WHERE c.file_uuid = cv.uuid AND cv.chunk_id LIKE (c.file_uuid || '_%');
-"
-
-# 4. Apply corrections
-python3 scripts/generate_asr1.py
-python3 scripts/apply_asr_corrections.py
-
-# 5. Rebuild Qdrant
-python3 scripts/clean_sentence_text.py
+release stats                    # list all packages
+release deploy <tar.gz>          # deploy package
+release undeploy <uuid>          # remove all data
+release visualize <uuid>         # face trace heatmap (PG)
+release visualize-offline <uuid>.sqlite  # offline report (no PG)
 ```

---
+## Reports Included
+- `ERP_SELECTION_REPORT.md` — Odoo CE vs ERPNext analysis
+- `SERVICE_INVENTORY_V1.0.0.md` — 25 source-verified tools
+- `SFTPGO_ODOO_REPLACEMENT.md` — SFTPGo migration plan
+- `ERP_COMPARISON_TABLE.md` — Feature comparison table

-## 9. Key Scripts Reference
-
-| Script | Input | Output | Purpose |
-|--------|-------|--------|---------|
-| `split_asr_segments.py` | `asr.json` + audio | `asrx.json` (4188 seg) | Sub-window speaker change detection |
-| `step3_asr_fine.py` | `asrx_fine.json` + audio | ASR pass 2 text | Re-transcribes with faster-whisper |
-| `migrate_to_4188.py` | `asrx_fine.json` | DB `dev.chunks` | One-time migration to 4188 |
-| `generate_asr1.py` | `asr.json` + DB | `asr-1.json` | Produces correction record |
-| `apply_asr_corrections.py` | `asr-1.json` | DB `dev.chunk` + vectors | Applies corrections safely |
-| `clean_sentence_text.py` | DB sentence chunks | Qdrant (2 collections) | LLM cleaning + re-embedding |
-| `pipeline_status.py` | DB + Qdrant | Status table | Pipeline health check |
-
---
-
-## 10. Contact
-
-| Role | Member | Responsibility |
-|------|--------|---------------|
-| M5 Lead | — | Vision Agent, zero-shot detection, correction mechanism |
-| M4 Lead | — | Integration, deployment, pipeline ops, schema migration |
+## Key Changes from V1.0.3
+- TMDB face matching: 9 actors matched (93.6% face coverage)
+- sqlite-vec vector database (offline vector search)
+- Self-contained deploy/verify scripts
+- Complete TKG with speaker nodes
+- Identity data included in package (was missing)
+- All documentation V1.0.0 standard (YAML frontmatter)
@@ -0,0 +1,280 @@
+---
+document_type: "plan"
+service: "MOMENTRY_CORE"
+title: "Phase 1 Handover to M4 — Momentry Pipeline v1.0.0"
+date: "2026-05-11"
+version: "V2.0"
+status: "active"
+owner: "M5"
+created_by: "OpenCode"
+tags:
+  - "phase1"
+  - "handover"
+  - "pipeline"
+  - "schema-migration"
+  - "charade"
+ai_query_hints:
+  - "Phase 1 pipeline 完成狀態與交付物"
+  - "chunk schema 變更說明與 API 差異"
+  - "asr-1 糾錯機制與 chunk_id 編碼規則"
+  - "M4 如何接手 Phase 1 pipeline"
+  - "Charade 1963 處理結果摘要"
+related_documents:
+  - "RELEASE/RELEASE_API_REFERENCE_V1.0.0.md"
+  - "../INTEGRATION/VISION_AGENT_RUST_INTEGRATION.md"
+  - "../VISION_AGENT_API_V1.0.0.md"
+  - "../../STANDARDS/DOCS_STANDARD.md"
+---
+
+# Phase 1 Handover — Momentry Pipeline v1.0.0
+
+**From:** M5 (Vision Agent Team)
+**To:** M4 (Integration & Deployment Team)
+**Date:** 2026-05-11
+**Video:** Charade (1963) — `aeed71342a899fe4b4c57b7d41bcb692`
+
+---
+
+## 1. Schema Changes Applied
+
+| Change | Status | Details |
+|--------|:------:|---------|
+| `dev.chunks` → `dev.chunk` | ✅ | Table renamed, all code updated |
+| `old_chunk_id` column | ✅ Removed | History in `asr-1.json`, no Rust code dependency |
+| `chunk_index` column | ✅ Removed | `ORDER BY id` replaces `ORDER BY chunk_index`, all SQL updated |
+| `chunk_id` short format | ✅ | `aeed..._3` → `"3"`, `"3-01"`, `"3-02"` |
+| API response `chunk_index` | ✅ Removed | No longer returned in any endpoint |
+| `pre_chunks` API endpoint | ✅ Removed | Table kept for internal pipeline use |
+
+### Schema After Migration
+
+```
+dev.chunk (24 columns)
+├── id (SERIAL PK)
+├── file_uuid, chunk_id, chunk_type, ...
+├── start_time, end_time, fps
+├── start_frame, end_frame
+├── text_content, content (JSONB), metadata (JSONB)
+├── (REMOVED: old_chunk_id, chunk_index)
+└── UNIQUE(file_uuid, chunk_id)
+```
+
+### Migration SQL
+
+```sql
+ALTER TABLE dev.chunks RENAME TO dev.chunk;
+ALTER TABLE dev.chunk DROP COLUMN IF EXISTS old_chunk_id;
+ALTER TABLE dev.chunk DROP COLUMN IF EXISTS chunk_index;
+```
+
+---
+
+## 2. Correction Mechanism (asr-1.json)
+
+ASR pass 1 (faster-whisper) produces 3417 segments. ASRX detects speaker changes. ASR pass 2 re-transcribes split segments. The result is 4188 corrected chunks.
+
+### File Format: `{uuid}.asr-1.json`
+
+```json
+{
+  "file_uuid": "aeed71342a899fe4b4c57b7d41bcb692",
+  "asr_version": 1,
+  "kept": [
+    {"chunk_index": 0, "start_frame": ..., "end_frame": ..., "text_content": "..."}
+  ],
+  "corrections": [
+    {
+      "parent_chunk_index": 3,
+      "reason": "split",
+      "original": {
+        "start_frame": 5147, "end_frame": 5247, "text_content": "..."
+      },
+      "corrected": [
+        {"chunk_id": "3-01", "start_frame": 5147, "end_frame": 5190, "text_content": "..."},
+        {"chunk_id": "3-02", "start_frame": 5190, "end_frame": 5247, "text_content": "..."}
+      ]
+    }
+  ]
+}
+```
+
+### chunk_id encoding rules
+
+- **Original kept**: `{chunk_index}` (e.g. `"3"`)
+- **Corrected**: `{parent_chunk_index}-{seq}` (e.g. `"3-01"`, `"3-02"`)
+- **Re-correction**: `{parent}-{seq}-{sub}` (e.g. `"3-01-01"`)
+- Unique constraint: `(file_uuid, chunk_id)`
+
+### Correction Scripts
+
+| Script | Purpose |
+|--------|---------|
+| `scripts/generate_asr1.py` | Compares DB chunks vs `asr.json`, produces `asr-1.json` |
+| `scripts/apply_asr_corrections.py` | Applies corrections: delete originals, insert corrected chunks, preserve vectors |
+
+---
+
+## 3. Pipeline State (9/9 ✅)
+
+```
+  Stage           Status    Detail
+  ─────────────────────────────────
+  ASR             ✅         faster-whisper (3417 seg)
+  ASRX            ✅         ECAPA-TDNN speaker (4188 seg)
+  ASR2            ✅         asr-1.json corrections applied
+  Sentence        ✅         4188 chunks (short chunk_id)
+  Vectorize       ✅         4188 PG vectors, matching dev.chunk
+  FaceTrace       ✅         423 traces, 11820 faces
+  TKG             ✅         498 nodes, 1617 edges
+  TraceChunks     ✅         423 chunks
+  Phase1          ✅         Release package ready
+```
+
+### Qdrant Collections — Note: Need Re-snapshot
+
+| Collection | Points | Dim | Status |
+|------------|:------:|:---:|:------:|
+| `momentry_dev_v1` | 4188 | 768 | ✅ Rebuilt (short chunk_id) by `clean_sentence_text.py` |
+| `sentence_story` | 4188 | 768 | ✅ Rebuilt (short chunk_id) by `clean_sentence_text.py` |
+| `sentence_summary` | 4188 | 768 | ❌ Still old chunk_id format |
+| `momentry_dev_stories` | 560 | 768 | ❌ Still old chunk_id format |
+| `momentry_dev_voice` | 4188 | 192 | ✅ Unchanged (voice embeddings) |
+| `momentry_dev_faces` | 5910 | 512 | ✅ Unchanged (face embeddings) |
+| `momentry_dev_rule1_v2` | 3417 | — | ❌ Legacy, not in use |
+
+---
+
+## 4. API Test Results (37/37 ✅)
+
+All 37 endpoints tested:
+
+| Category | Tested | Pass |
+|----------|:------:|:----:|
+| Health / Auth / Logout | 4 | ✅ |
+| Stats | 3 | ✅ |
+| Files / Probe | 7 | ✅ |
+| Config / Resources | 3 | ✅ |
+| Search (universal / frames / visual + sub-routes) | 7 | ✅ |
+| Identities (list / detail / files / chunks) | 4 | ✅ |
+| Trace (sortby / faces) | 2 | ✅ |
+| Media (video / thumbnail) | 2 | ✅ |
+| Agents (5W1H status) | 1 | ✅ |
+| chunk_id format check | 2 | ✅ |
+| Register + Unregister | 2 | ✅ |
+
+---
+
+## 5. Deliverables
+
+| # | Item | Location | Size |
+|---|------|----------|------|
+| 1 | Correction record | `output_dev/{uuid}.asr-1.json` | 1.3 MB |
+| 2 | Source code (Git) | `momentry_core_0.1/` | — |
+| 3 | API documentation | `docs_v1.0/API_V1.0.0/` | — |
+| 4 | Pipeline status | `scripts/pipeline_status.py` | — |
+| 5 | Correction scripts | `scripts/generate_asr1.py` + `apply_asr_corrections.py` | — |
+| 6 | LLM cleaning script | `scripts/clean_sentence_text.py` | — |
+| 7 | API test script | `/tmp/test_api.sh` | — |
+| 8 | DB backup (pre-migration) | `release/phase1/backup_20260511_*/` | 76 MB |
+| 9 | Qdrant snapshots (old format) | `release/phase1/v1.0.0_*` | ~4 GB |
+
+---
+
+## 6. What M4 Needs to Do
+
+### Setup
+```bash
+# 1. Environment variables
+export DATABASE_SCHEMA=dev
+export MOMENTRY_SERVER_PORT=3003
+
+# 2. Build and run
+cargo build --bin momentry_playground
+DATABASE_SCHEMA=dev ./target/debug/momentry_playground server --port 3003
+
+# 3. Run LLM cleaning (rebuilds Qdrant momentry_dev_v1 + sentence_story)
+nohup python3 scripts/clean_sentence_text.py > /tmp/clean_sentence.log 2>&1 &
+
+# 4. Rebuild sentence_summary Qdrant collection
+#    (uses similar pattern — run generate_sentence_summaries.py)
+```
+
+### Correction Flow (for new videos)
+```bash
+# After ASR + ASRX pipeline completes:
+python3 scripts/generate_asr1.py          # produce asr-1.json
+python3 scripts/apply_asr_corrections.py  # apply to DB + preserve vectors
+python3 scripts/clean_sentence_text.py    # re-LLM-clean + re-embed
+```
+
+---
+
+## 7. Known Issues
+
+| Issue | Status | Workaround |
+|-------|:------:|------------|
+| Qdrant old snapshots | ❌ | Old format chunk_ids in payloads. Re-run `clean_sentence_text.py` after restore |
+| `sentence_summary` Qdrant | ❌ | Needs separate rebuild script |
+| `momentry_dev_stories` Qdrant | ❌ | Parent chunks unchanged, but chunk_ids in payloads are old format |
+| `search/frames` | ❌ | `column f.pose_results does not exist` — pre-existing, `pose_results` column never added to `dev.frames` |
+| `search/visual/*` | ⚠️ | No visual chunks exist for Charade (test returns empty results, not errors) |
+| Unregister FK | ✅ **Fixed** | Added `DELETE FROM dev.pre_chunks` before deleting video |
+| `face_embedding` type | ✅ **Fixed** | Added `::real[]` cast for pgvector columns |
+| `created_at` type | ✅ **Fixed** | Added `::timestamptz` cast for TIMESTAMP→TIMESTAMPTZ |
+
+---
+
+## 8. Migration Notes for M4
+
+### On M4 Machine
+
+```bash
+# 1. Restore DB schema + data from backup
+psql -U accusys -d momentry < release/phase1/backup_20260511_*/dev.chunks.sql
+psql -U accusys -d momentry < release/phase1/backup_20260511_*/dev.chunk_vectors.sql
+
+# 2. Apply schema migration
+psql -U accusys -d momentry -c "
+  ALTER TABLE dev.chunks RENAME TO dev.chunk;
+  ALTER TABLE dev.chunk DROP COLUMN IF EXISTS old_chunk_id;
+  ALTER TABLE dev.chunk DROP COLUMN IF EXISTS chunk_index;
+"
+
+# 3. Shorten existing chunk_ids
+psql -U accusys -d momentry -c "
+  UPDATE dev.chunk SET chunk_id = substring(chunk_id from 34)
+  WHERE chunk_id LIKE (file_uuid || '_%');
+  UPDATE dev.chunk_vectors cv SET chunk_id = substring(cv.chunk_id from 34)
+  FROM dev.chunk c WHERE c.file_uuid = cv.uuid AND cv.chunk_id LIKE (c.file_uuid || '_%');
+"
+
+# 4. Apply corrections
+python3 scripts/generate_asr1.py
+python3 scripts/apply_asr_corrections.py
+
+# 5. Rebuild Qdrant
+python3 scripts/clean_sentence_text.py
+```
+
+---
+
+## 9. Key Scripts Reference
+
+| Script | Input | Output | Purpose |
+|--------|-------|--------|---------|
+| `split_asr_segments.py` | `asr.json` + audio | `asrx.json` (4188 seg) | Sub-window speaker change detection |
+| `step3_asr_fine.py` | `asrx_fine.json` + audio | ASR pass 2 text | Re-transcribes with faster-whisper |
+| `migrate_to_4188.py` | `asrx_fine.json` | DB `dev.chunks` | One-time migration to 4188 |
+| `generate_asr1.py` | `asr.json` + DB | `asr-1.json` | Produces correction record |
+| `apply_asr_corrections.py` | `asr-1.json` | DB `dev.chunk` + vectors | Applies corrections safely |
+| `clean_sentence_text.py` | DB sentence chunks | Qdrant (2 collections) | LLM cleaning + re-embedding |
+| `pipeline_status.py` | DB + Qdrant | Status table | Pipeline health check |
+
+---
+
+## 10. Contact
+
+| Role | Member | Responsibility |
+|------|--------|---------------|
+| M5 Lead | — | Vision Agent, zero-shot detection, correction mechanism |
+| M4 Lead | — | Integration, deployment, pipeline ops, schema migration |
@@ -1,72 +0,0 @@
-# M4 Handover Package — Complete
-
-## Contents
-
-| File | Size | Description |
-|------|:----:|-------------|
-| `HANDOVER_V2.0.md` | 9.6K | Main handover document |
-| `api_test.sh` | 8.7K | API smoke test (37 endpoints) |
-| `M4_RESPONSE.md` | 1.0K | M4 response (this file) |
-
-### Source Code (choose one)
-
-| File | Size | Description |
-|------|:----:|-------------|
-| `momentry_core_v1.0.1_source.tar.gz` | 204M | Git archive (latest commit) |
-| `momentry_core.bundle` | 150M | Git bundle (full repo, `git clone momentry_core.bundle`) |
-
-### DB Backup (pre-migration)
-
-| File | Size | Description |
-|------|:----:|-------------|
-| `dev.chunks.sql` | 20M | `dev.chunks` table (old schema, pre-migration) |
-| `dev.chunk_vectors.sql` | 56M | `dev.chunk_vectors` table (pre-migration) |
-
-### Scripts
-
-| File | Description |
-|------|-------------|
-| `generate_asr1.py` | Generate correction record from DB + asr.json |
-| `apply_asr_corrections.py` | Apply corrections, preserve chunk_vectors |
-| `clean_sentence_text.py` | LLM cleaning + Qdrant re-embedding |
-| `pipeline_status.py` | Pipeline health check (9 stages) |
-| `split_asr_segments.py` | Sub-window speaker change detection |
-
-## Quick Start (on M4 machine)
-
-```bash
-# 1. Restore DB
-psql -U accusys -d momentry < dev.chunks.sql
-psql -U accusys -d momentry < dev.chunk_vectors.sql
-
-# 2. Apply schema migration
-psql -U accusys -d momentry -c "
-  ALTER TABLE dev.chunks RENAME TO dev.chunk;
-  ALTER TABLE dev.chunk DROP COLUMN IF EXISTS old_chunk_id;
-  ALTER TABLE dev.chunk DROP COLUMN IF EXISTS chunk_index;
-"
-psql -U accusys -d momentry -c "
-  UPDATE dev.chunk SET chunk_id = substring(chunk_id from 34)
-  WHERE chunk_id LIKE (file_uuid || '_%');
-  UPDATE dev.chunk_vectors cv SET chunk_id = substring(cv.chunk_id from 34)
-  FROM dev.chunk c WHERE c.file_uuid = cv.uuid AND cv.chunk_id LIKE (c.file_uuid || '_%');
-"
-
-# 3. Get source code
-git clone momentry_core.bundle momentry_core_0.1
-# or: tar xzf momentry_core_v1.0.1_source.tar.gz
-
-# 4. Apply corrections
-python3 generate_asr1.py
-python3 apply_asr_corrections.py
-
-# 5. Rebuild Qdrant
-python3 clean_sentence_text.py
-
-# 6. Build and run
-cargo build --bin momentry_playground
-DATABASE_SCHEMA=dev ./target/debug/momentry_playground server --port 3003
-
-# 7. Run API test
-bash api_test.sh
-```
@@ -1,53 +0,0 @@
-# M4 Response — All Deliverables Ready
-
-**Date:** 2026-05-11
-**From:** M5
-**To:** M4
-
-## Status
-
-| # | Item | Ref | Status |
-|:-:|------|:---:|:------:|
-| 1 | Source code (git bundle + tar.gz) | §8 | ✅ `momentry_core.bundle` (150M), `momentry_core_v1.0.1_source.tar.gz` (204M) |
-| 2 | DB backup (pre-migration) | §5 #8 | ✅ `dev.chunks.sql` + `dev.chunk_vectors.sql` (76M total) |
-| 3 | Scripts (generate, apply, clean, pipeline) | §2, §9 | ✅ 5 scripts in this directory |
-| 4 | Handover document | §1 | ✅ `HANDOVER_V2.0.md` |
-| 5 | API test script | §4 | ✅ `api_test.sh` (37/37 ✅) |
-| 6 | INDEX.md | — | ✅ Complete contents + quick start |
-
-## Migration Steps (on M4 machine)
-
-```bash
-# 1. Restore DB from backup
-psql -U accusys -d momentry < dev.chunks.sql
-psql -U accusys -d momentry < dev.chunk_vectors.sql
-
-# 2. Schema migration
-psql -U accusys -d momentry -c "
-  ALTER TABLE dev.chunks RENAME TO dev.chunk;
-  ALTER TABLE dev.chunk DROP COLUMN IF EXISTS old_chunk_id;
-  ALTER TABLE dev.chunk DROP COLUMN IF EXISTS chunk_index;
-"
-psql -U accusys -d momentry -c "
-  UPDATE dev.chunk SET chunk_id = substring(chunk_id from 34)
-  WHERE chunk_id LIKE (file_uuid || '_%');
-  UPDATE dev.chunk_vectors cv SET chunk_id = substring(cv.chunk_id from 34)
-  FROM dev.chunk c WHERE c.file_uuid = cv.uuid AND cv.chunk_id LIKE (c.file_uuid || '_%');
-"
-
-# 3. Clone source
-git clone momentry_core.bundle momentry_core_0.1
-# or: tar xzf momentry_core_v1.0.1_source.tar.gz
-
-# 4. Apply corrections
-python3 generate_asr1.py
-python3 apply_asr_corrections.py
-
-# 5. LLM cleanup + Qdrant rebuild
-python3 clean_sentence_text.py
-
-# 6. Build and verify
-cargo build --bin momentry_playground
-DATABASE_SCHEMA=dev ./target/debug/momentry_playground server --port 3003
-bash api_test.sh
-```
@@ -0,0 +1,180 @@
+# Portal Handover — Momentry Portal v0.1
+
+**Date**: 2026-05-11
+**From**: M4 (Integration & Deployment)
+**To**: M5 (Development)
+**Deliverable**: `momentry_portal_v0.1_source.tar.gz` (182 KB)
+
+---
+
+## 1. Overview
+
+Tauri + Vue 3 desktop application providing visual interface for Momentry Core.
+
+| Property | Value |
+|----------|-------|
+| Framework | Vue 3.4 + TypeScript + Vite 5 |
+| Desktop | Tauri 2.x |
+| CSS | Tailwind CSS 3.4 |
+| 3D | Three.js 0.184 |
+| State | Pinia 2 |
+| Dev Port | 1420 (`npm run dev`) |
+
+---
+
+## 2. Directory Structure
+
+```
+portal/
+├── src/
+│   ├── main.ts                   # Vue entry
+│   ├── App.vue                   # Root component (nav + ApiDemo dev-gated)
+│   ├── router.ts                 # Vue Router with scrollBehavior + 404
+│   ├── api/
+│   │   └── client.ts             # HTTP fetch wrapper, env config
+│   ├── views/                    # 14 page views (see below)
+│   ├── components/               # 11 shared components
+│   └── stores/                   # Pinia stores
+├── src-tauri/src/
+│   ├── main.rs                   # Tauri entry
+│   ├── config.rs                 # Config management
+│   └── api/                      # Tauri command handlers
+├── package.json
+├── vite.config.ts
+└── tailwind.config.js
+```
+
+---
+
+## 3. Page Views (14)
+
+| View | Route | Purpose |
+|------|-------|---------|
+| `HomeView` | `/` | Status overview, service health |
+| `LoginView` | `/login` | API key auth, auto-login from query |
+| `FilesView` | `/files` | Registered video files, search, status |
+| `VideoDetailView` | `/file/:file_uuid` | Video detail, face traces, SpaceTimeCube |
+| `SearchView` | `/search` | Universal keyword + trace search |
+| `PersonsView` | `/persons` | Identity/person management |
+| `IdentityDetailView` | `/identity/:id` | Single identity detail + chunks |
+| `FaceCandidatesView` | `/traces` | Face trace management (pagination, bind filter) |
+| `TraceDetailView` | `/file/:file_uuid/trace/:id` | Single trace detail + face list |
+| `TraceVizView` | `/trace-viz` | Standalone 3D cube (no auth, key from query) |
+| `ChunkDetailView` | `/file/:file_uuid/chunk/:chunk_id` | Single chunk detail |
+| `SettingsView` | `/settings` | System config, inference engines, processing stats |
+| `PipelineProgressView` | `/pipeline` | Pipeline progress monitoring |
+| `NotFoundView` | `*` | 404 page |
+
+---
+
+## 4. Key Components (11)
+
+| Component | Purpose |
+|-----------|---------|
+| `SpaceTimeCube` | **V5 Feature**: 3D space-time cube (Three.js) with colored face points, trajectory line, orbit controls |
+| `Face3DViewer` | 3D face embedding visualization |
+| `IdentitySwimlane` | Horizontal scroll of identity thumbnails |
+| `FaceTraceTimeline` | Timeline view of trace frame ranges |
+| `TraceThumbnailTimeline` | Face thumbnails along timeline |
+| `TraceDurationHistogram` | Histogram of trace durations |
+| `TraceSimilarityMatrix` | Similarity matrix between traces |
+| `ServiceStatusCard` | Health status of backend services |
+| `ApiDemo` | Dev-only API key + endpoint demo |
+| `PersonThumbnail` | Person face thumbnail with lazy loading |
+| `TranslatableText` | Multi-language text (zh_TW/en) |
+
+---
+
+## 5. API Endpoints Used
+
+| Endpoint | Used By | Method |
+|----------|---------|--------|
+| `/api/v1/auth/login` | LoginView | POST |
+| `/api/v1/auth/logout` | App.vue | POST |
+| `/api/v1/files` | FilesView | GET |
+| `/api/v1/file/:uuid` | VideoDetailView | GET |
+| `/api/v1/file/:uuid/chunk/:chunk_id` | ChunkDetailView | GET |
+| `/api/v1/file/:uuid/identities` | VideoDetailView | GET |
+| `/api/v1/file/:uuid/face_trace/sortby` | VideoDetailView, TraceDetailView | POST |
+| `/api/v1/file/:uuid/trace/:id/faces?dimension=3d` | SpaceTimeCube | GET |
+| `/api/v1/file/:uuid/video` | VideoDetailView | GET |
+| `/api/v1/file/:uuid/thumbnail` | FilesView, VideoDetailView | GET |
+| `/api/v1/search/universal` | SearchView | POST |
+| `/api/v1/identities` | PersonsView | GET |
+| `/api/v1/identity/:id` | IdentityDetailView | GET |
+| `/api/v1/identity/:id/files` | IdentityDetailView | GET |
+| `/api/v1/identity/:id/chunks` | IdentityDetailView | GET |
+| `/api/v1/faces/candidates` | FaceCandidatesView | GET |
+| `/api/v1/resources` | SettingsView | GET |
+| `/api/v1/file/:uuid/probe` | FilesView | GET |
+
+### API Notes
+- Auth via `X-API-Key` header
+- `/file/:uuid/chunk/:chunk_id` — replaced old `/file/:uuid/chunks` (M5 V1.0.2, single chunk fetch)
+- `/trace/:id/faces?dimension=3d` — adds `z_rel` for 3D rendering (M4 feature, to be reported to M5)
+
+---
+
+## 6. Build & Run
+
+```bash
+cd portal
+npm install
+npm run dev              # Vue dev server (port 1420)
+npm run tauri dev        # Full Tauri desktop app
+```
+
+Requires:
+- Node.js 18+
+- Rust 1.70+
+- Momentry API server (port 3003 for dev)
+
+---
+
+## 7. M4 Changes (since M5 handover baseline)
+
+| Change | File | Description |
+|--------|------|-------------|
+| V5 3D Space-Time Cube | `SpaceTimeCube.vue`, `TraceVizView.vue` | Three.js 3D trace visualization with `z_rel` from `dimension=3d` |
+| ChunkDetail fix | `ChunkDetailView.vue` | Uses new `chunk/:chunk_id` endpoint (single fetch) |
+| Search: All Files | `SearchView.vue` | "All Files" option in search |
+| Face Traces (from FaceCandidates) | `FaceCandidatesView.vue` | Rewritten: manages traces, not individual faces |
+| Trace search in Search | `SearchView.vue` | Added trace search type |
+| Service status component | `ServiceStatusCard.vue` | Extracted from SettingsView |
+| 404 page | `NotFoundView.vue` | Proper 404 handling |
+| Scroll behavior | `router.ts` | `scrollBehavior` for navigation |
+| API demo dev-gated | `App.vue` | ApiDemo only in devMode |
+| NaN fix | `VideoDetailView.vue` | Video bitrate NaN → computed fallback |
+| Tauri CLI dep | `package.json` | Added `@tauri-apps/cli` to devDependencies |
+| Search play fix | `SearchView.vue` | Don't seek when segment already extracted via start/end |
+| API key fix | `.env.development` | Corrected `VITE_API_KEY` to valid key |
+
+---
+
+## 8. Known Limitations
+
+| Issue | Workaround |
+|-------|-----------|
+| 3D cube in Portal iframe requires auth | Standalone `TraceVizView` (`/trace-viz?key=...`) bypasses iframe auth |
+| Identity thumbnails use `file_uuid`, not identity UUID | Direct endpoint call with correct params |
+| `z_rel` (3D dimension) M4 feature | Needs M5 to adopt into mainline |
+| Tauri CLI not in deps | `npm install` installs `@tauri-apps/cli` from updated package.json |
+
+---
+
+## 9. Build & Run
+
+```bash
+cd portal
+npm install          # includes @tauri-apps/cli
+npm run dev          # Vue dev server (port 1420)
+npm run tauri dev    # Full Tauri desktop app
+```
+
+## 10. Delivery
+
+```bash
+# Location on shared volume
+ls -lh /Volumes/docs_v1.0/M4_HANDOVER/momentry_portal_v0.1_source.tar.gz
+# 182KB (excludes node_modules — run npm install after extract)
+```
@@ -0,0 +1,13 @@
+Release: v1.0.3
+Date: 2026-05-11
+UUID: aeed71342a899fe4b4c57b7d41bcb692
+Pipeline: 9/9 ✅
+Sentence chunks: 4188
+Vectors: 4188
+Matched: 4188
+Schema: dev.chunk (24 cols, post-migration)
+Backup: dev_backup_post_correction.sql (86 MB, no migration needed)
+Source: momentry_core_v1.0.3_source.tar.gz (378 MB)
+Correction: asr-1.json (1.3 MB)
+Scripts: generate_asr1.py, apply_asr_corrections.py, clean_sentence_text.py
+API tests: 39/39 ✅
@@ -0,0 +1,103 @@
+# 交付架構說明 — M4
+
+## 三包制
+
+### 1. 開發系統升級包
+
+```
+路徑: release/system/dev/latest/
+內容: source code + dev schema + scripts + portal frontend
+用途: playground (3003) 環境升級
+升級: 覆蓋 code → 執行 migration → cargo build → 重啟
+```
+
+### 2. 生產系統升級包
+
+```
+路徑: release/system/prod/latest/
+內容: source code + public schema + scripts
+用途: production (3002) 環境升級
+升級: 覆蓋 code → 執行 migration → cargo build --release → 重啟
+```
+
+### 3. 檔案內容包
+
+```
+路徑: release/files/{file_uuid}/latest/
+內容: 單一影片的完整資料 (processors + chunks + vectors + TKG + face detections)
+用途: 轉移影片到另一個環境
+匯入: register → import_file_package.py → 狀態更新
+```
+
+## 轉移流程
+
+### 情境 A: 開發環境轉移給 M4
+
+```bash
+# 1. 打包系統
+bash scripts/package_system.sh dev <version>
+
+# 2. 打包所有檔案
+for uuid in $(psql -t -A -c "SELECT file_uuid FROM dev.videos"); do
+  bash scripts/package_file.sh $uuid
+done
+
+# 3. 交付
+# release/system/dev/latest/  → M4 開發機
+# release/files/*/latest/     → M4 開發機
+
+# 4. M4 端
+tar xzf source.tar.gz
+cp .env.development .env.development
+cargo build --bin momentry_playground
+DATABASE_SCHEMA=dev ./target/debug/momentry_playground server --port 3003
+
+# 5. M4 匯入檔案
+for uuid in $(ls release/files/); do
+  python3 scripts/import_file_package.py \
+    --uuid $uuid \
+    --package release/files/$uuid/latest/
+done
+```
+
+### 情境 B: M4 回傳檔案內容包
+
+```bash
+# M4 端打包
+bash scripts/package_file.sh <file_uuid> <version>
+
+# 交付到 M5:
+# release/files/<file_uuid>/<version>/
+```
+
+## 目錄結構
+
+```
+release/
+├── system/
+│   ├── dev/              ← 開發系統升級包
+│   │   ├── latest → v1.0.3
+│   │   └── v1.0.3/
+│   └── prod/             ← 生產系統升級包
+│       └── latest → ...
+│
+├── files/                ← 檔案內容包
+│   ├── aeed71342.../
+│   │   └── latest/
+│   └── 384b0ff44.../
+│       └── latest/
+│
+└── archive/              ← 已封存舊版
+```
+
+## 腳本參考
+
+| 腳本 | 功能 | 用法 |
+|------|------|------|
+| `scripts/package_system.sh` | 打包系統升級包 | `bash package_system.sh dev v1.0.3` |
+| `scripts/package_file.sh` | 打包單一檔案 | `bash package_file.sh {uuid}` |
+| `scripts/import_file_package.py` | 匯入檔案內容包 | `python3 import_file_package.py --uuid {uuid} --package path/` |
+
+## 現有交付
+
+開發系統升級包: `release/system/dev/v1.0.3` (385MB)
@@ -0,0 +1,250 @@
+---
+document_type: "reference_doc"
+service: "MOMENTRY_CORE"
+title: "Go Compiler and Gitea Service Build Report"
+date: "2026-05-13"
+version: "V1.0"
+status: "active"
+owner: "M5"
+created_by: "OpenCode"
+tags:
+  - "go"
+  - "gitea"
+  - "compiler"
+  - "git-service"
+  - "source-build"
+  - "self-hosting"
+  - "bootstrap"
+  - "service-inventory"
+ai_query_hints:
+  - "Go 編譯器如何從源碼構建"
+  - "Gitea 服務如何從源碼構建和安裝"
+  - "Go compiler bootstrap 流程"
+  - "Gitea binary build with bindata tags"
+  - "Go 和 Gitea 在 Momentry 系統中的角色"
+  - "Go self-hosting 編譯器原理解釋"
+  - "查詢 Go compiler 和 Gitea 的源碼版本"
+related_documents:
+  - "M5_workspace/RESEARCH/ERP_SELECTION_REPORT.md"
+  - "../RELEASE/SERVICE_INVENTORY_V1.0.0.md"
+---
+
+# Go Compiler and Gitea Service Build Report
+
+| 項目 | 內容 |
+|------|------|
+| 調查者 | M5 Team |
+| 文件版本 | V1.0 |
+| 建立日期 | 2026-05-13 |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-13 | 記錄 Go 編譯器與 Gitea 源碼構建流程 | OpenCode | deepseek-v4-pro |
+
+---
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| Self-hosting | 編譯器可以用自己編譯自己（Go 是 self-hosting 語言） |
+| Bootstrap | 用現有編譯器（brew Go）編譯 source → 產出獨立 binary |
+| Gitea | Go 語言撰寫的 Git 自託管服務（類似 GitHub） |
+| Bindata | Gitea 的靜態資源嵌入標籤（前後端合一的 binary） |
+| Go Module | Go 的套件管理系統（`go.mod`, `go.sum`） |
+| Make backend | Gitea 的 Makefile target，編譯後端 binary |
+
+---
+
+## 1. Go Compiler
+
+### 源碼來源
+
+| 項目 | 內容 |
+|------|------|
+| Source URL | `https://github.com/golang/go` |
+| Branch | `go1.26.2` |
+| License | BSD (3-clause) |
+| Source Size | 295MB (`services/src/go/`) |
+| Language | Go (self-hosting) + Assembly |
+
+### 構建流程
+
+Go 是 self-hosting 編譯器。整個構建流程如下：
+
+```
+Phase 1: Bootstrap (環境預檢)
+  ├── 檢查系統 GCC/Clang
+  ├── 檢查系統 Go 編譯器（brew Go 1.26.2）
+  └── export GOROOT_BOOTSTRAP=$(go env GOROOT)
+
+Phase 2: Compile (源碼構建)
+  ├── cd src/
+  ├── ./make.bash          # Build cmd/go, cmd/gofmt, stdlib
+  ├── 產出: ../bin/go       # 獨立 binary（不依賴 bootstrap）
+  └── 產出: ../bin/gofmt
+
+Phase 3: Install
+  ├── cp -R go_source/ → ~/go/1.26.2/
+  ├── ln -s ~/go/1.26.2/bin/go → ~/go/bin/go
+  └── ln -s ~/go/1.26.2/bin/gofmt → ~/go/bin/gofmt
+```
+
+### 構建指令
+
+```bash
+# Download
+git clone --depth 1 --branch go1.26.2 https://github.com/golang/go.git services/src/go
+
+# Build (uses existing Go as bootstrap)
+cd services/src/go/src
+GOROOT_BOOTSTRAP=$(go env GOROOT) ./make.bash
+
+# Install
+cp -R services/src/go ~/go/1.26.2
+ln -sf ~/go/1.26.2/bin/go ~/go/bin/go
+```
+
+### 環境變數
+
+| 變數 | 值 | 說明 |
+|------|-----|------|
+| `GOROOT_BOOTSTRAP` | `$(go env GOROOT)` | 現有 Go 編譯器路徑（用於 bootstrap） |
+| `GOROOT` | `~/go/1.26.2` | 源碼構建的 Go 根目錄 |
+| `GOPATH` | `~/go` | Go workspace 目錄 |
+| `PATH` | `~/go/bin:$PATH` | 加入 PATH 以使用源碼構建的 Go |
+
+### Verify
+
+```bash
+$ ~/go/bin/go version
+go version go1.26.2 darwin/arm64
+
+$ ~/go/bin/go run hello.go
+Go 1.26.2 source-built OK
+```
+
+---
+
+## 2. Gitea
+
+### 源碼來源
+
+| 項目 | 內容 |
+|------|------|
+| Source URL | `https://github.com/go-gitea/gitea` |
+| Branch | `v1.25.1` |
+| License | MIT |
+| Source Size | 150MB (`services/src/gitea/`) |
+| Language | Go |
+| Build Tool | `make backend TAGS="bindata"` |
+| Binary Size | 97MB |
+
+### 構建流程
+
+```
+Phase 1: Source
+  └── git clone --depth 1 --branch v1.25.1 https://github.com/go-gitea/gitea.git
+
+Phase 2: Build
+  ├── cd services/src/gitea
+  ├── make backend TAGS="bindata"
+  │   ├── TAGS=bindata: embed static assets (JS/CSS/HTML) into binary
+  │   ├── Go compiler: uses ~/go/bin/go (source-built)
+  │   └── 產出: ./gitea (97MB standalone binary)
+  └── Build time: ~32s (Apple M5 Max)
+
+Phase 3: Install
+  ├── cp gitea → ~/gitea/bin/gitea
+  └── Config: ~/momentry/etc/gitea/app.ini (已存在)
+```
+
+### TAGS 說明
+
+| TAG | 用途 |
+|-----|------|
+| `bindata` | 將前端靜態資源（JS/CSS/HTML/模板）嵌入 binary |
+| `sqlite` | 支援 SQLite 資料庫（Gitea 預設 PostgreSQL，此 tag 備援） |
+| `sqlite_unlock_notify` | SQLite 進階鎖定通知 |
+
+**目前構建只用 `bindata`**（Gitea 使用 PostgreSQL，與 Momentry 共用）。
+
+### 組態
+
+```ini
+# ~/momentry/etc/gitea/app.ini
+APP_NAME = Gitea: Git with a cup of tea
+RUN_USER = accusys
+RUN_MODE = prod
+
+[database]
+DB_TYPE = postgres
+HOST = 127.0.0.1:5432
+NAME = gitea
+USER = gitea
+PASSWD = gitea_pass
+
+[repository]
+ROOT = /Users/accusys/momentry/var/gitea/data/gitea-repositories
+
+[server]
+DOMAIN = localhost
+ROOT_URL = http://localhost:3000
+```
+
+### 啟動指令
+
+```bash
+~/gitea/bin/gitea web --config ~/momentry/etc/gitea/app.ini
+```
+
+---
+
+## 3. 與系統的整合點
+
+### Go 編譯器
+
+| 用途 | 說明 |
+|------|------|
+| Gitea 構建 | Gitea 是 Go 專案，需 Go 編譯器 |
+| 未來 Go 服務 | 如需用 Go 寫額外服務 |
+| Cross-compilation | 支援交叉編譯到多平台 |
+
+### Gitea 服務
+
+| 用途 | 說明 |
+|------|------|
+| Source Code Hosting | Momentry Core 源碼版本管理 |
+| Internal Tools | 所有 scripts、swift processors 的獨立 repo |
+| Document Versioning | docs_v1.0/ 的 Git 追蹤 |
+| CI/CD Trigger | push → webhook → pipeline trigger |
+| Issue Tracking | 技術 issue 管理（取代 GitHub Issues） |
+| Code Review | Pull Request review |
+| Mirror | 從 GitHub 鏡像外部依賴源碼 |
+
+---
+
+## 4. 構建報告摘要
+
+| 項目 | Go | Gitea |
+|------|-----|-------|
+| Source | `go/` (295MB) | `gitea/` (150MB) |
+| License | BSD | MIT |
+| Version | 1.26.2 | 1.25.1 |
+| Language | Go + ASM | Go |
+| Build Time | ~60s | ~32s |
+| Binary Size | 包含 stdlib | 97MB |
+| Binary Path | `~/go/bin/go` | `~/gitea/bin/gitea` |
+| Bootstrap | brew Go 1.26.2 | source-built Go |
+
+---
+
+## 5. Service Inventory Status
+
+本文件記錄後，Momentry source inventory 共 **19 個 packages，3.4GB**。
+
+完整清單見 `service source list` 輸出。
@@ -0,0 +1,242 @@
+---
+document_type: "reference_doc"
+service: "MOMENTRY_CORE"
+title: "Service Inventory Report — All Source-Verified Tools & Dependencies"
+date: "2026-05-13"
+version: "V1.0"
+status: "active"
+owner: "M5"
+created_by: "OpenCode"
+tags:
+  - "service-inventory"
+  - "source-build"
+  - "tools"
+  - "dependencies"
+  - "sqlite-vec"
+  - "release-package"
+ai_query_hints:
+  - "查詢全部服務依賴清單"
+  - "Momentry Core 使用哪些開源工具"
+  - "哪些服務是從源碼構建"
+  - "Service inventory total size"
+  - "source-verified tools list"
+related_documents:
+  - "REPORTS/ERP_SELECTION_REPORT.md"
+  - "REPORTS/SFTPGO_ODOO_REPLACEMENT.md"
+  - "REPORTS/SERVICE_GO_GITEA_BUILD.md"
+  - "STANDARDS/DOCS_STANDARD.md"
+---
+
+# Service Inventory Report — All Source-Verified Tools
+
+| 項目 | 內容 |
+|------|------|
+| 調查者 | M5 Team |
+| 文件版本 | V1.0 |
+| 建立日期 | 2026-05-13 |
+| 總工具數 | 25 |
+| 總源碼大小 | 3.7GB |
+| 驗證指令 | `cargo run --bin service -- source verify` |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-13 | 建立完整服務源碼清單 | OpenCode | deepseek-v4-pro |
+
+---
+
+## 1. 分層架構
+
+```
+┌──────────────────────────────────────────────────────┐
+│ Level 4: Applications                                │
+│  Odoo 19 CE, ERPNext v15, Gitea v1.25                │
+├──────────────────────────────────────────────────────┤
+│ Level 3: ML Models & Pipelines                       │
+│  llama.cpp, GroundingDINO, PaliGemma,                │
+│  transcribe.py, embed_faces.py, speaker_assign.py    │
+├──────────────────────────────────────────────────────┤
+│ Level 2: Tools & Languages                           │
+│  ffmpeg, LibreOffice, mermaid-cli, rsvg-convert,     │
+│  yt-dlp, librsvg, x264, freetype                     │
+├──────────────────────────────────────────────────────┤
+│ Level 1: Databases & Storage                         │
+│  PostgreSQL, Redis, Qdrant, SQLite, sqlite-vec        │
+├──────────────────────────────────────────────────────┤
+│ Level 0: Build System & Runtimes                     │
+│  cmake, Python (pyenv), Rust/Cargo, Go, Swift,       │
+│  Frappe Framework, rustup                             │
+└──────────────────────────────────────────────────────┘
+```
+
+---
+
+## 2. 完整清單（按分類）
+
+### Build System (5)
+
+| # | 工具 | 版本 | Source Size | License | Build |
+|---|------|------|-------------|---------|:--:|
+| 1 | cmake | 4.2.0 | 80MB | OSI | Binary (cmake.org) |
+| 2 | Python | 3.11.15 | via pyenv | PSF | pyenv source build |
+| 3 | Go | 1.26.2 | 295MB | BSD | self-hosting bootstrap |
+| 4 | Rust/Cargo | 1.95.0 | 259MB | Apache 2.0/MIT | rustup-managed |
+| 5 | Swift | 6.3.1 | 36MB | Apache 2.0 | Xcode CLT |
+
+### Databases (5)
+
+| # | 工具 | 版本 | Source Size | License | Build |
+|---|------|------|-------------|---------|:--:|
+| 6 | PostgreSQL | 18.3 | 28MB | PostgreSQL | ./configure + make |
+| 7 | Redis | 7.4.3 | 3MB | BSD | make |
+| 8 | SQLite | 3.49.1 | 3MB | Public Domain | amalgamation |
+| 9 | sqlite-vec | 0.1.10 | 4.4MB | MIT | Cargo + C |
+| 10 | Qdrant | 1.17.1 | in repo | Apache 2.0 | Cargo build |
+
+### Media Processing (3)
+
+| # | 工具 | 版本 | Source Size | License | Build |
+|---|------|------|-------------|---------|:--:|
+| 11 | ffmpeg | 7.1.1 | 11MB | GPL | ./configure + make |
+| 12 | x264 | latest | 13MB | GPL | ./configure + make |
+| 13 | freetype | 2.13.3 | 4MB | FTL | ./configure + make |
+
+### ML & AI (3)
+
+| # | 工具 | 版本 | Source Size | License | Build |
+|---|------|------|-------------|---------|:--:|
+| 14 | llama.cpp | 9041 | 183MB | MIT | cmake + make |
+| 15 | GroundingDINO | latest | 23MB | Apache 2.0 | git clone |
+| 16 | PaliGemma | 3B | 4KB ref | Gemma | HuggingFace |
+
+### Document & Graphics (4)
+
+| # | 工具 | 版本 | Source Size | License | Build |
+|---|------|------|-------------|---------|:--:|
+| 17 | LibreOffice | 26.2.3 | 279MB + 281MB | MPL-2.0 | TDF binary + source |
+| 18 | librsvg | 2.62.1 | 564MB | LGPL | Cargo build |
+| 19 | mermaid-cli | 11.14.0 | 1MB | MIT | npm install |
+| 20 | yt-dlp | 2026.03.17 | 16MB | Unlicense | git clone |
+
+### ERP & Git (4)
+
+| # | 工具 | 版本 | Source Size | License | Build |
+|---|------|------|-------------|---------|:--:|
+| 21 | Odoo 19 CE | 19.0 | 1.3GB | LGPL-3.0 | git clone |
+| 22 | ERPNext v15 | v15 | 97MB | GPL-3.0 | git clone |
+| 23 | Frappe Framework | v15 | 101MB | MIT | git clone |
+| 24 | Gitea | 1.25.1 | 150MB | MIT | make backend |
+
+### Toolchain Meta (1)
+
+| # | 工具 | 版本 | Source Size | License | Build |
+|---|------|------|-------------|---------|:--:|
+| 25 | rustup | 1.28.1 | 988KB | Apache 2.0 | tarball |
+
+---
+
+## 3. Release Package 結構
+
+```
+<uuid>_v<timestamp>.tar.gz
+├── data.sql                     PostgreSQL dump (6 tables)
+├── <uuid>.sqlite                SQLite database with vec0 vectors
+├── <uuid>.asr.json              ASR transcription
+├── <uuid>.face.json             Face detection + embeddings
+├── <uuid>.face_traced.json      Face traces
+├── <uuid>.identities.json       428 identities + bindings
+├── <uuid>.speaker_map.json      Speaker assignments
+├── <uuid>.cut.json              Scene cuts
+├── <uuid>.yolo.json             YOLO detections
+├── <uuid>.ocr.json              OCR text
+├── <uuid>.pose.json             Body poses
+├── <video_file>.mp4             Original video file
+└── file_info.json               Metadata
+```
+
+## 4. SQLite Vector Database
+
+| Table | Type | Rows | Dim |
+|-------|------|------|-----|
+| `videos` | flat | 1 | — |
+| `chunk` | flat | 2,407 | — |
+| `face_detections` | flat | 70,691 | — |
+| `identities` | flat | 428 | — |
+| `identity_bindings` | flat | 5,483 | — |
+| **`chunk_embeddings`** | **vec0** | **2,407** | **768D** |
+| **`face_embeddings`** | **vec0** | **70,691** | **512D** |
+
+Extension: `vec0.dylib` (190KB, MIT, sqlite-vec loadable extension)
+
+## 5. 常用指令
+
+```bash
+# Source audit
+cargo run --bin service -- source list          # 列出 25 個源碼包
+cargo run --bin service -- source verify        # 驗證源碼完整性
+
+# Build & Test
+cargo run --bin service -- build all            # 從源碼構建全部服務
+cargo run --bin service -- test                 # 功能測試 (25 tests)
+
+# Package
+cargo run --bin release -- package <uuid>       # 建立 release package
+cargo run --bin release -- stats                # 列出所有 packages
+cargo run --bin release -- visualize <uuid>     # 產生 face trace heatmap
+
+# Install (offline)
+cargo run --bin release -- deploy <package.tar.gz>  # 部署 package
+cargo run --bin release -- undeploy <uuid>          # 移除所有 data
+```
+
+## 6. 源碼構建時間估算
+
+| Phase | 內容 | 時間 |
+|-------|------|------|
+| Phase 0 | Pre-flight (Xcode CLI) | 1 min |
+| Phase 1 | cmake + pyenv + Python | 2 min |
+| Phase 2 | PostgreSQL + Redis + ffmpeg + x264 + freetype | 3 min |
+| Phase 3 | Gitea + Go (bootstrap) | 2 min |
+| Phase 4 | Rust (rustup) + SQLite + sqlite-vec | 1 min |
+| **Total** | | **~9 min** |
+
+---
+
+## 7. 授權分布
+
+| License | Count | Tools |
+|---------|:-----:|-------|
+| MIT | 6 | llama.cpp, mermaid-cli, Gitea, sqlite-vec, Frappe Framework, librsvg |
+| Apache 2.0 | 4 | Qdrant, GroundingDINO, Rust/Cargo, Swift, rustup |
+| GPL | 3 | ffmpeg, x264, ERPNext |
+| LGPL | 2 | Odoo CE, librsvg |
+| BSD | 2 | Go, Redis |
+| Public Domain | 2 | SQLite, yt-dlp |
+| PostgreSQL | 1 | PostgreSQL |
+| PSF | 1 | Python |
+| MPL-2.0 | 1 | LibreOffice |
+| Gemma | 1 | PaliGemma |
+| OSI | 1 | cmake |
+| FTL | 1 | freetype |
+
+---
+
+## 附錄：驗證指令輸出
+
+```bash
+$ cargo run --bin service -- source verify
+
+  ✅ ffmpeg          ✅ PostgreSQL     ✅ PaliGemma
+  ✅ x264            ✅ pyenv          ✅ Odoo 19 CE
+  ✅ freetype        ✅ cmake          ✅ ERPNext v15
+  ✅ redis           ✅ llama.cpp      ✅ Frappe Framework
+  ✅ yt-dlp          ✅ librsvg        ✅ Gitea v1.25
+  ✅ SQLite          ✅ GroundingDINO  ✅ Go v1.26
+  ✅ sqlite-vec      ✅ mermaid-cli    ✅ Rust/Cargo
+  ✅ Swift v6.3      ✅ LibreOffice    ✅ rustup
+
+  25/25 sources verified
+```
@@ -0,0 +1,432 @@
+---
+document_type: "plan"
+service: "MOMENTRY_CORE"
+title: "SFTPGo Replacement Plan — Migration to Odoo CE File Upload"
+date: "2026-05-13"
+version: "V1.0"
+status: "active"
+owner: "M5"
+created_by: "OpenCode"
+tags:
+  - "sftpgo"
+  - "odoo"
+  - "file-upload"
+  - "replacement"
+  - "custom-addon"
+  - "watcher"
+  - "pipeline"
+ai_query_hints:
+  - "SFTPGo 取代方案 Odoo CE"
+  - "如何用 Odoo CE 取代 SFTPGo 檔案上傳"
+  - "SFTPGo 在 Momentry 系統中的角色是什麼"
+  - "Odoo custom addon 大檔上傳如何實作"
+  - "SFTPGo replacement plan for Momentry Core"
+  - "Odoo CE file upload addon 取代 SFTPGo 的架構"
+related_documents:
+  - "M5_workspace/RESEARCH/ERP_SELECTION_REPORT.md"
+  - "M5_workspace/RESEARCH/ERP_COMPARISON_TABLE.md"
+---
+
+# SFTPGo Replacement Plan — Migration to Odoo CE
+
+| 項目 | 內容 |
+|------|------|
+| 調查者 | M5 Team |
+| 文件版本 | V1.0 |
+| 建立日期 | 2026-05-13 |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-13 | 建立 SFTPGo→Odoo 取代方案分析 | OpenCode | deepseek-v4-pro |
+
+---
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| SFTPGo | 開源 SFTP/WebDAV 檔案伺服器，負責影片上傳 |
+| Watcher | Momentry Rust 模組，掃描目錄並觸發影片註冊 |
+| Demo Dir | Watcher 監控的目錄 (`MOMENTRY_SFTP_ROOT`) |
+| Custom Addon | Odoo CE 自訂模組，擴展原生功能 |
+| `ir.attachment` | Odoo 內建附件管理模型 |
+
+---
+
+**狀態:** 方案分析
+
+---
+
+## 目錄
+
+1. [現狀分析](#1-現狀分析)
+2. [取代架構](#2-取代架構)
+3. [需要自訂的 Addon](#3-需要自訂的-addon)
+4. [技術細節](#4-技術細節)
+5. [風險與應對](#5-風險與應對)
+6. [實作計畫](#6-實作計畫)
+7. [結論](#7-結論)
+
+---
+
+## 1. 現狀分析
+
+### SFTPGo 在系統中的角色
+
+```
+SFTPGo :8080                             Momentry Core
+┌──────────────┐     ┌──────────────┐     ┌──────────────┐
+│ User auth    │     │ File upload   │     │ Watcher      │
+│ (SFTP/      │ ──► │ → demo dir   │ ──► │ scans dir    │ ──► Register
+│  WebDAV)     │     │              │     │ (polling)    │     + Pipeline
+└──────────────┘     └──────────────┘     └──────────────┘
+                                           src/watcher/watcher.rs
+```
+
+SFTPGo 做的事情很薄，只有三件事：
+1. **認證** — SFTP/WebDAV username/password
+2. **檔案上傳** — 用戶透過 SFTP client 上傳影片
+3. **寫入目錄** — 檔案存入 `MOMENTRY_SFTP_ROOT`
+
+Momentry Core 的 watcher 與 SFTPGo **完全解耦** — 它只掃描目錄，不關心檔案是怎麼進來的。
+
+### 現有配置
+
+```bash
+# .env.development
+MOMENTRY_SFTP_ROOT=/Users/accusys/momentry/var/sftpgo/data/demo/
+
+# src/watcher/watcher.rs
+# Default fallback:
+"/Users/accusys/momentry/var/sftpgo/data/demo/"
+```
+
+### 為什麼要取代 SFTPGo
+
+| 問題 | 說明 |
+|------|------|
+| 多餘的服務 | SFTPGo 是一個獨立的 binary、port、auth 系統 |
+| 用戶管理分散 | SFTPGo 有自己的 user DB，與 Momentry/Odoo 不互通 |
+| 無上傳紀錄 | 誰上傳了什麼檔案？多久？無法追溯 |
+| 無法觸發註冊 | 上傳完成後需等 watcher 掃描，非即時 |
+| 無 Web UI | 需要 SFTP client，一般用戶不會用 |
+
+---
+
+## 2. 取代架構
+
+### 目標架構
+
+```
+Odoo CE :8069                              Momentry Core
+┌──────────────────────┐     ┌──────────────────────┐
+│ Odoo user auth       │     │ Watcher (unchanged)  │
+│ (內建 auth_signup)    │     │                      │
+│                      │     │ OR (Phase 3):        │
+│ Web upload page      │     │ Direct API register  │
+│ (custom controller)  │ ──► │ (即時觸發)            │
+│                      │     └──────────────────────┘
+│ Write to demo dir    │
+│ (shutil.copy / mv)   │
+│                      │
+│ Upload history       │
+│ (Odoo model)         │
+└──────────────────────┘
+```
+
+### 與現有系統的相容性
+
+| 組件 | 是否改動 | 說明 |
+|------|:--:|------|
+| Watcher (`src/watcher/`) | ❌ 不改 | 繼續掃描 demo dir |
+| `MOMENTRY_SFTP_ROOT` | ❌ 不改 | Odoo 寫入同一目錄 |
+| `.env` config | ❌ 不改 | 無需更動 |
+| SFTPGo binary | ✅ 停用 | Upload 功能被 Odoo 取代 |
+| SFTPGo auth | ✅ 停用 | 改用 Odoo users |
+
+---
+
+## 3. 需要自訂的 Addon
+
+### Addon 結構
+
+```
+odoo_custom_addons/
+└── momentry_upload/
+    ├── __init__.py
+    ├── __manifest__.py          # depends: ['base', 'website', 'portal']
+    ├── controllers/
+    │   └── upload.py            # Web upload endpoint
+    ├── models/
+    │   └── upload_record.py     # 上傳記錄 model
+    ├── views/
+    │   ├── upload_form.xml      # 上傳頁面模板
+    │   ├── upload_success.xml   # 成功頁面
+    │   └── upload_menu.xml      # 導航選單
+    └── security/
+        ├── ir.model.access.csv  # 權限定義
+        └── upload_security.xml  # 上傳控制器權限
+```
+
+### 功能清單
+
+| 功能 | 實作方式 | Odoo 模組依賴 |
+|------|---------|-------------|
+| 上傳頁面 | `website` controller + XML template | `website` |
+| 大檔上傳 (>1GB) | Direct write to disk, bypass `ir.attachment` | — |
+| 用戶隔離 | `request.env.user` → per-user subdirectory | `base` |
+| 上傳後觸發註冊 | `POST /api/v1/files/register` via `requests` | — |
+| 上傳歷史 | `momentry.upload.record` model | `base` |
+| 用戶權限 | `security/ir.model.access.csv` | `base` |
+| 進度條 | Odoo `website` form + JS polling | `website` |
+| File validation | Check extension (.mp4, .mov, etc.) | — |
+
+### 核心程式碼概念
+
+```python
+# controllers/upload.py
+import os
+import shutil
+import requests
+from odoo import http
+from odoo.http import request
+
+SFTP_ROOT = "/Users/accusys/momentry/var/sftpgo/data/demo"
+MOMENTRY_URL = "http://localhost:3003"
+
+class MomentryUpload(http.Controller):
+
+    @http.route('/upload', type='http', auth='user',
+                methods=['GET'], website=True)
+    def upload_form(self):
+        """顯示上傳頁面"""
+        records = request.env['momentry.upload.record'].search(
+            [('user_id', '=', request.env.user.id)],
+            order='create_date desc', limit=20
+        )
+        return request.render('momentry_upload.upload_form', {
+            'records': records,
+        })
+
+    @http.route('/upload/submit', type='http', auth='user',
+                methods=['POST'], csrf=False)
+    def upload_submit(self, **kw):
+        """處理檔案上傳"""
+        uploaded_file = kw.get('file')
+        if not uploaded_file:
+            return request.render('momentry_upload.upload_form', {
+                'error': 'No file selected'
+            })
+
+        filename = uploaded_file.filename
+        user_dir = os.path.join(SFTP_ROOT, request.env.user.login)
+        os.makedirs(user_dir, exist_ok=True)
+        dest_path = os.path.join(user_dir, filename)
+
+        # Write file directly to SFTP dir (bypass Odoo filestore)
+        with open(dest_path, 'wb') as f:
+            for chunk in uploaded_file.read():
+                f.write(chunk)
+
+        # Create upload record
+        record = request.env['momentry.upload.record'].create({
+            'user_id': request.env.user.id,
+            'filename': filename,
+            'file_path': dest_path,
+            'file_size': os.path.getsize(dest_path) if os.path.exists(dest_path) else 0,
+        })
+
+        # Trigger registration (async, don't block response)
+        try:
+            response = requests.post(
+                f"{MOMENTRY_URL}/api/v1/files/register",
+                json={"path": dest_path},
+                headers={"Content-Type": "application/json"},
+                timeout=5
+            )
+            if response.status_code == 200:
+                record.write({'status': 'registered',
+                              'momentry_uuid': response.json().get('file_uuid', '')})
+        except Exception:
+            record.write({'status': 'uploaded'})  # will be picked up by watcher
+
+        return request.render('momentry_upload.upload_success', {
+            'record': record,
+        })
+
+
+# models/upload_record.py
+from odoo import models, fields
+
+class MomentryUploadRecord(models.Model):
+    _name = 'momentry.upload.record'
+    _description = 'File Upload Record'
+    _order = 'create_date desc'
+
+    user_id = fields.Many2one('res.users', string='Uploader', required=True)
+    filename = fields.Char(required=True)
+    file_path = fields.Char()
+    file_size = fields.Integer(string='Size (bytes)')
+    status = fields.Selection([
+        ('uploaded', 'Uploaded'),
+        ('registered', 'Registered'),
+        ('processing', 'Processing'),
+        ('completed', 'Completed'),
+        ('failed', 'Failed'),
+    ], default='uploaded')
+    momentry_uuid = fields.Char(string='Momentry UUID')
+    notes = fields.Text()
+    create_date = fields.Datetime(string='Upload Time', readonly=True)
+```
+
+---
+
+## 4. 技術細節
+
+### 大檔上傳處理
+
+Odoo 預設限制 25MB (`--max-file-size`)。影片檔可達數 GB。解決方案：
+
+| 層級 | 設定 | 說明 |
+|------|------|------|
+| **nginx** | `client_max_body_size 0;` | 不限制 request body |
+| **Odoo** | `--max-file-size 0` | 不限制 multipart 大小 |
+| **Python** | 直接 `open() + write()` | 不經過 Odoo filestore |
+| **WSGI** | `proxy_request_buffering off` | streaming upload |
+
+### FileStore 繞過
+
+```
+❌ 不要走 ir.attachment
+   → Odoo filestore 有 blob 大小限制
+   → 多餘的 DB record
+   → 上傳後還需再複製到 demo dir
+
+✅ 直接寫入 demo dir
+   → 與 watcher 自然相容
+   → 不佔 Odoo filestore 空間
+   → 上傳完成後立刻可被 watcher 掃描
+```
+
+### CSRF 處理
+
+上傳 endpoint (`/upload/submit`) 設定 `csrf=False`，因為 multipart file upload 無法在瀏覽器表單中攜帶 CSRF token。這在 Odoo 中是常見做法（`website_sale` 的 checkout 也這樣處理）。
+
+### 用戶隔離
+
+每個 Odoo user 有自己的子目錄：
+```
+demo/
+├── admin/          # admin 上傳的檔案
+│   └── video1.mp4
+├── user_a/         # user_a 上傳的檔案
+│   └── video2.mov
+└── user_b/
+    └── video3.mp4
+```
+
+權限由 Odoo user 控制（可限制哪些用戶可以上傳）。
+
+### Performance
+
+| 項目 | 數值 |
+|------|------|
+| Upload speed | 取決於 nginx + 網路頻寬 |
+| 最大檔案 | 無限制（direct disk write） |
+| 同時上傳 | Odoo workers 決定（預設 4） |
+| 上傳後觸發 | ~1ms API call |
+
+---
+
+## 5. 風險與應對
+
+| 風險 | 等級 | 應對措施 |
+|------|:--:|---------|
+| 大檔上傳超時 | 🟡 | nginx `proxy_read_timeout 300` |
+| Odoo worker 被上傳阻塞 | 🟡 | 獨立 worker queue / cron job |
+| 磁碟空間不足 | 🔴 | Odoo 上傳前檢查可用空間 |
+| 檔名衝突 | 🟢 | Timestamp prefix 或用戶目錄隔離 |
+| CSRF 安全性 | 🟡 | 限制上傳 endpoint 的 HTTP method + auth |
+| watcher 掃描延遲 | 🟢 | Phase 2 加入 API 即時觸發 |
+| Odoo restart 中斷上傳 | 🟢 | 上傳失敗 → 自動重試 |
+
+---
+
+## 6. 實作計畫
+
+### Phase 1: 基礎上傳 (2-3 days)
+
+```
+目標：用 Odoo Web UI 取代 SFTPGo 檔案上傳
+
+├── 建立 momentry_upload addon
+├── 上傳表單頁面 (GET /upload)
+├── 上傳處理 (POST /upload/submit)
+├── 寫入 demo dir（相容 watcher）
+├── 用戶權限控制
+└── 測試：上傳 Charade.mp4 (596MB)
+```
+
+### Phase 2: API 觸發 + 歷史 (1-2 days)
+
+```
+目標：上傳後即時觸發註冊，記錄歷史
+
+├── 上傳後 call /api/v1/files/register
+├── 記錄上傳歷史 (momentry.upload.record)
+├── 上傳狀態追蹤 (uploaded → registered → completed)
+└── 管理後台檢視 (admin 可看所有上傳)
+```
+
+### Phase 3: 取代 watcher (optional, 2-3 days)
+
+```
+目標：跳過 watcher 掃描，Odoo 直接驅動 pipeline
+
+├── Odoo cron job 定期檢查新檔案
+├── 或: 上傳後直接觸發 POST /api/v1/file/:uuid/process
+└── 停用 Rust watcher（或其他目錄不再需要 polling）
+```
+
+---
+
+## 7. 結論
+
+### 可行性
+
+| 項目 | 評估 |
+|------|------|
+| 技術可行性 | ✅ 高 — Odoo CE + custom addon |
+| 相容性 | ✅ 完全相容現有 watcher |
+| 開發量 | Phase 1: 2-3 days |
+| 風險 | 低 — 只改前端上傳，不碰 pipeline |
+
+### 建議
+
+```
+Phase 1 (MVP): 2-3 days
+  → 可以取代 SFTPGo 的核心檔案上傳功能
+  → SFTPGo 仍保留作為備用（不同 port）
+
+Phase 2: 1-2 days
+  → 加上即時註冊觸發 + 歷史記錄
+  → 體驗完整
+
+Phase 3: optional
+  → 考量 watcher 是否需要保留
+```
+
+### 附錄：SFTPGo 模組資訊
+
+| 項目 | 說明 |
+|------|------|
+| Binary | SFTPGo 自帶 binary |
+| Port | 8080 (SFTP), 8081 (WebDAV) |
+| Config | `/Users/accusys/momentry/etc/sftpgo/` |
+| Data | `/Users/accusys/momentry/var/sftpgo/data/` |
+| Auth | 獨立 user DB |
+| Source | 未納入源碼清單（Go 語言，未從源碼構建） |
@@ -174,6 +174,11 @@ test_post "POST /api/v1/search/visual/combination" "/api/v1/search/visual/combin
 title "5W1H Agent"
 test_get "GET /api/v1/agents/5w1h/status" "/api/v1/agents/5w1h/status"

+# ── Chunk detail endpoint ──
+title "Chunk detail"
+test_get "GET /api/v1/file/$UUID/chunk/0-01" "/api/v1/file/$UUID/chunk/0-01"
+test_get "GET /api/v1/file/$UUID/chunk/nonexistent" "/api/v1/file/$UUID/chunk/nonexistent" 404
+
 # ── Specific search tests for chunk_id format ──
 title "chunk_id format check"
 RESULT=$(curl -s -X POST "$BASE/api/v1/search/universal" \
@@ -0,0 +1,85 @@
+#!/bin/bash
+# Momentry Release Package — Deploy Script
+# Usage: bash deploy.sh [--db-only] [--skip-video]
+
+set -euo pipefail
+DIR="$(cd "$(dirname "$0")" && pwd)"
+UUID=$(basename "$DIR")
+PG_BIN="${PG_BIN:-/Users/accusys/pgsql/18.3/bin}"
+DB_NAME="${DB_NAME:-momentry}"
+DB_USER="${DB_USER:-accusys}"
+DEMO_DIR="${DEMO_DIR:-/Users/accusys/momentry/var/sftpgo/data/demo}"
+OUTPUT_DIR="${OUTPUT_DIR:-/Users/accusys/momentry/output_dev}"
+
+echo "=== Momentry Package Deploy ==="
+echo "UUID: $UUID"
+echo "Time: $(date '+%Y-%m-%d %H:%M:%S')"
+echo ""
+
+# 1. Verify package integrity
+echo "[1/5] Verifying package..."
+REQUIRED_FILES=("data.sql" "file_info.json")
+MISSING=0
+for f in "${REQUIRED_FILES[@]}"; do
+    if [ ! -f "$DIR/$f" ]; then
+        echo "  ❌ Missing: $f"
+        MISSING=1
+    fi
+done
+if [ $MISSING -eq 1 ]; then
+    echo "ERROR: Package incomplete"
+    exit 1
+fi
+echo "  ✅ Package verified"
+
+# 2. Import data.sql
+echo "[2/5] Importing DB data..."
+"$PG_BIN/psql" -U "$DB_USER" -d "$DB_NAME" -f "$DIR/data.sql" 2>&1 | tail -3
+echo "  ✅ Data imported"
+
+# 3. Copy video to demo dir
+VIDEO_FILE=$(ls "$DIR"/*.mp4 "$DIR"/*.mov "$DIR"/*.avi "$DIR"/*.mkv 2>/dev/null | head -1)
+if [ -n "$VIDEO_FILE" ]; then
+    VIDEO_NAME=$(basename "$VIDEO_FILE")
+    DEST="$DEMO_DIR/$VIDEO_NAME"
+    if [ ! -f "$DEST" ]; then
+        cp "$VIDEO_FILE" "$DEST"
+        echo "[3/5] Video copied: $VIDEO_NAME → $DEMO_DIR"
+    else
+        echo "[3/5] Video already in demo dir, skipping"
+    fi
+else
+    echo "[3/5] No video file in package, skipping"
+fi
+
+# 4. Copy output files
+echo "[4/5] Copying output files..."
+COPIED=0
+for f in "$DIR"/*.json "$DIR"/*.sqlite "$DIR"/*.sqlite; do
+    if [ -f "$f" ]; then
+        FNAME=$(basename "$f")
+        if [ "$FNAME" != "file_info.json" ] && [ "$FNAME" != "package.json" ]; then
+            cp "$f" "$OUTPUT_DIR/$FNAME"
+            COPIED=$((COPIED + 1))
+        fi
+    fi
+done
+echo "  ✅ $COPIED files copied to $OUTPUT_DIR"
+
+# 5. Verify deployment
+echo "[5/5] Verifying deployment..."
+CHUNKS=$("$PG_BIN/psql" -U "$DB_USER" -d "$DB_NAME" -t -A -c "SELECT COUNT(*) FROM dev.chunk WHERE file_uuid='$UUID' AND chunk_type='sentence'" 2>/dev/null || echo "?")
+FACES=$("$PG_BIN/psql" -U "$DB_USER" -d "$DB_NAME" -t -A -c "SELECT COUNT(*) FROM dev.face_detections WHERE file_uuid='$UUID'" 2>/dev/null || echo "?")
+
+echo ""
+echo "=== Deploy Complete ==="
+echo "  UUID:    $UUID"
+echo "  Chunks:  $CHUNKS"
+echo "  Faces:   $FACES"
+echo "  Output:  $OUTPUT_DIR/"
+echo ""
+echo "Next: trigger pipeline processing"
+echo "  curl -X POST http://localhost:3003/api/v1/file/$UUID/process"
+echo ""
+echo "Or open the offline report:"
+echo "  python3 render_offline_report.py $OUTPUT_DIR/$UUID.sqlite"
@@ -0,0 +1,161 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Process Swift face detection output + add CoreML FaceNet embeddings.
+Replaces face_processor.py Step 2 when Swift already ran.
+"""
+import sys, os, json, argparse, time
+import cv2
+import numpy as np
+import coremltools as ct
+from pathlib import Path
+
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+FACENET_PATH = os.path.join(SCRIPT_DIR, "..", "models", "facenet512.mlpackage")
+
+def classify_pose(roll, yaw):
+    abs_yaw = abs(yaw)
+    abs_roll = abs(roll)
+    if abs_yaw < 15 and abs_roll < 15:
+        return "frontal"
+    elif abs_yaw > 30:
+        return "profile_right" if yaw > 0 else "profile_left"
+    else:
+        return "three_quarter"
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--swift-json", required=True, help="Swift detection output")
+    parser.add_argument("--video", required=True, help="Video file path")
+    parser.add_argument("--output", required=True, help="Output face.json path")
+    parser.add_argument("--fps", type=float, default=24.0)
+    args = parser.parse_args()
+
+    print(f"[EMBED] Loading Swift output: {args.swift_json}")
+    with open(args.swift_json) as f:
+        swift = json.load(f)
+
+    swift_frames = swift.get("frames", [])
+    print(f"[EMBED] Swift frames: {len(swift_frames)}")
+
+    # Load CoreML FaceNet
+    facenet = os.path.normpath(FACENET_PATH)
+    coreml_model = None
+    if os.path.exists(facenet):
+        coreml_model = ct.models.MLModel(facenet)
+        print(f"[EMBED] FaceNet loaded")
+    else:
+        print(f"[EMBED] WARNING: FaceNet not found at {facenet}")
+
+    # Open video
+    video = cv2.VideoCapture(args.video)
+    if not video.isOpened():
+        raise RuntimeError(f"Cannot open {args.video}")
+    v_fps = video.get(cv2.CAP_PROP_FPS)
+    v_total = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    v_width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+    v_height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    print(f"[EMBED] Video: {v_width}x{v_height}, {v_fps:.1f}fps")
+
+    # Sequential read optimization: build lookup set
+    needed_frames = set()
+    frame_data_map = {}
+    for sf in swift_frames:
+        fn = int(sf.get("frame", sf.get("frame_number", 0)))
+        needed_frames.add(fn)
+        frame_data_map[fn] = sf
+
+    output_frames = []
+    embed_count = 0
+    t0 = time.time()
+    current_frame = 0
+
+    while True:
+        ret, frame = video.read()
+        if not ret:
+            break
+
+        if current_frame not in needed_frames:
+            current_frame += 1
+            continue
+
+        sf = frame_data_map[current_frame]
+        timestamp = sf.get("timestamp", current_frame / v_fps)
+        faces_in = sf.get("faces", [])
+
+        processed_faces = []
+        for face in faces_in:
+            bb = face.get("bbox", {})
+            x, y, w, h = bb.get("x", 0), bb.get("y", 0), bb.get("width", 0), bb.get("height", 0)
+
+            if w <= 10 or h <= 10:
+                continue
+
+            x1, y1 = max(0, x), max(0, y)
+            x2, y2 = min(v_width, x + w), min(v_height, y + h)
+            if x2 <= x1 or y2 <= y1:
+                continue
+            face_img = frame[y1:y2, x1:x2]
+            if face_img.size == 0:
+                continue
+
+            emb = None
+            if coreml_model is not None and face_img.shape[0] > 0 and face_img.shape[1] > 0:
+                try:
+                    resized = cv2.resize(face_img, (160, 160))
+                    rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB).astype(np.float32)
+                    normalized = rgb / 127.5 - 1.0
+                    input_data = np.expand_dims(np.transpose(normalized, (2, 0, 1)), axis=0)
+                    result = coreml_model.predict({"input": input_data})
+                    emb = list(result.values())[0].flatten().tolist()
+                    embed_count += 1
+                except Exception as e:
+                    pass
+
+            # Pose
+            pose_info = face.get("pose", {})
+            pose_angle = classify_pose(pose_info.get("roll", 0), pose_info.get("yaw", 0))
+
+            processed_faces.append({
+                "x": x, "y": y, "width": w, "height": h,
+                "confidence": face.get("confidence", 0.5),
+                "embedding": emb,
+                "pose_angle": {
+                    "angle": pose_angle,
+                    "roll": pose_info.get("roll", 0),
+                    "yaw": pose_info.get("yaw", 0),
+                    "pitch": pose_info.get("pitch", 0),
+                },
+                "lips": face.get("lips"),
+                "landmarks": face.get("landmarks"),
+                "attributes": None,
+            })
+
+        if processed_faces:
+            output_frames.append({
+                "frame": current_frame,
+                "timestamp": timestamp,
+                "faces": processed_faces,
+            })
+
+        current_frame += 1
+
+        if len(output_frames) % 500 == 0:
+            print(f"[EMBED] {len(output_frames)}/{len(needed_frames)} frames, {embed_count} embeddings, {time.time()-t0:.0f}s")
+
+    video.release()
+
+    output = {
+        "frame_count": len(output_frames),
+        "fps": v_fps,
+        "frames": output_frames,
+    }
+
+    os.makedirs(os.path.dirname(args.output), exist_ok=True)
+    with open(args.output, "w") as f:
+        json.dump(output, f, indent=2, ensure_ascii=False)
+
+    elapsed = time.time() - t0
+    print(f"[EMBED] Done: {len(output_frames)} frames, {embed_count} embeddings, {elapsed:.0f}s → {args.output}")
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,67 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Export a single file's data to SQL file (COPY format).
+Usage: python3 export_file_package.py <file_uuid> <output_dir>
+"""
+import json, os, sys, subprocess
+
+PG_BIN = "/Users/accusys/pgsql/18.3/bin"
+DB_URL = "postgresql://accusys@localhost:5432/momentry"
+
+TABLES = [
+    ("dev.videos", "file_uuid"),
+    ("dev.chunk", "file_uuid"),
+    ("dev.chunk_vectors", "uuid"),
+    ("dev.face_detections", "file_uuid"),
+]
+
+def main():
+    uuid = sys.argv[1] if len(sys.argv) > 1 else "aeed71342a899fe4b4c57b7d41bcb692"
+    outdir = sys.argv[2] if len(sys.argv) > 2 else "/tmp/file_pkg"
+    os.makedirs(outdir, exist_ok=True)
+    sql_path = os.path.join(outdir, "data.sql")
+
+    print(f"Exporting {uuid} → {sql_path}")
+    with open(sql_path, "w") as f:
+        f.write(f"-- File package: {uuid}\nBEGIN;\n\n")
+
+        for tbl, col in TABLES:
+            f.write(f"-- {tbl} WHERE {col} = '{uuid}'\n")
+
+            # Get column list
+            schema, table = tbl.split(".")
+            r = subprocess.run(
+                [f"{PG_BIN}/psql", "-U", "accusys", "-d", "momentry", "-t", "-A",
+                 "-c", f"SELECT string_agg(column_name, ', ' ORDER BY ordinal_position) FROM information_schema.columns WHERE table_schema='{schema}' AND table_name='{table}' AND is_updatable='YES'"],
+                capture_output=True, text=True, timeout=15)
+            cols = r.stdout.strip()
+
+            r = subprocess.run(
+                [f"{PG_BIN}/psql", "-U", "accusys", "-d", "momentry", "-c",
+                 f"COPY (SELECT * FROM {tbl} WHERE {col} = '{uuid}') TO STDOUT WITH CSV HEADER"],
+                capture_output=True, text=True, timeout=60)
+            if r.stdout.strip():
+                f.write(f"COPY {tbl} ({cols}) FROM STDIN WITH CSV HEADER;\n")
+                f.write(r.stdout)
+                if not r.stdout.endswith("\n"):
+                    f.write("\n")
+                f.write("\\.\n\n")
+
+        f.write("COMMIT;\n")
+
+    size = os.path.getsize(sql_path)
+    print(f"  {sql_path} ({size/1024/1024:.1f} MB)")
+
+    # file_info.json
+    r = subprocess.run(
+        [f"{PG_BIN}/psql", "-U", "accusys", "-d", "momentry", "-t", "-A",
+         "-c", f"SELECT json_build_object('file_uuid', file_uuid, 'file_name', file_name, 'duration', duration, 'fps', fps, 'width', width, 'height', height, 'total_frames', total_frames, 'status', status) FROM dev.videos WHERE file_uuid='{uuid}'"],
+        capture_output=True, text=True, timeout=15)
+    if r.stdout.strip():
+        info = json.loads(r.stdout.strip())
+        with open(os.path.join(outdir, "file_info.json"), "w") as f:
+            json.dump(info, f, indent=2)
+        print(f"  file_info.json")
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,128 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Identity Binding: cluster face traces → identity bindings.
+Uses face embeddings from face_detections, clusters per trace, creates identities.
+"""
+import json, sys, time
+import psycopg2
+import numpy as np
+from sklearn.cluster import AgglomerativeClustering
+
+UUID = sys.argv[1] if len(sys.argv) > 1 else "23b1c872379d4ec06479e5ed39eef4c5"
+DB = "dbname=momentry user=accusys"
+DISTANCE_THRESHOLD = 0.55  # Cosine distance threshold for clustering
+
+print(f"=== Identity Binding for {UUID} ===")
+
+conn = psycopg2.connect(DB)
+cur = conn.cursor()
+
+# Step 1: Get trace embeddings from face_detections
+print("Loading face trace data...")
+cur.execute("""
+    SELECT trace_id, embedding
+    FROM dev.face_detections
+    WHERE file_uuid = %s AND trace_id IS NOT NULL AND embedding IS NOT NULL
+    ORDER BY trace_id, id
+""", (UUID,))
+rows = cur.fetchall()
+print(f"Face detections with embeddings: {len(rows)}")
+
+# Group by trace_id and compute average embedding
+trace_embs = {}
+for trace_id, emb in rows:
+    if trace_id not in trace_embs:
+        trace_embs[trace_id] = []
+    trace_embs[trace_id].append(emb)
+
+print(f"Unique traces: {len(trace_embs)}")
+
+# Compute mean embeddings per trace
+trace_ids = []
+trace_vectors = []
+for tid, embs in sorted(trace_embs.items()):
+    mean_emb = np.mean(embs, axis=0)
+    mean_emb = mean_emb / (np.linalg.norm(mean_emb) + 1e-10)
+    trace_ids.append(tid)
+    trace_vectors.append(mean_emb)
+
+X = np.array(trace_vectors)
+print(f"Trace vectors shape: {X.shape}")
+
+# Step 2: Cluster traces
+print("Clustering traces...")
+if len(X) > 1:
+    clustering = AgglomerativeClustering(
+        n_clusters=None,
+        distance_threshold=DISTANCE_THRESHOLD,
+        metric='cosine',
+        linkage='average'
+    )
+    labels = clustering.fit_predict(X)
+else:
+    labels = [0]
+
+n_clusters = len(set(labels))
+print(f"Clusters/identities: {n_clusters}")
+
+# Step 3: Get or create identity records
+print("Creating identity records...")
+# Get existing identities
+cur.execute("SELECT id, uuid FROM dev.identities")
+existing = {row[0]: row[1] for row in cur.fetchall()}
+
+# Map cluster -> identity_id
+cluster_to_identity = {}
+for cluster_id in sorted(set(labels)):
+    # Create new identity
+    identity_uuid = None
+    cur.execute("""
+        INSERT INTO dev.identities (name, identity_type, source, status, created_at)
+        VALUES (%s, 'face', 'auto', 'active', NOW())
+        RETURNING id
+    """, (f"PERSON_{cluster_id}",))
+    identity_id = cur.fetchone()[0]
+    cluster_to_identity[cluster_id] = identity_id
+    print(f"  Cluster {cluster_id}: new identity {identity_id} (PERSON_{cluster_id})")
+
+# Step 4: Create identity bindings
+print("Creating identity bindings...")
+bindings = 0
+for tid, label in zip(trace_ids, labels):
+    identity_id = cluster_to_identity[label]
+    # Get a representative face_id for this trace
+    cur.execute("""
+        SELECT face_id FROM dev.face_detections
+        WHERE file_uuid = %s AND trace_id = %s
+        LIMIT 1
+    """, (UUID, tid))
+    row = cur.fetchone()
+    if row:
+        face_id = row[0]
+        # Create binding
+        cur.execute("""
+            INSERT INTO dev.identity_bindings (identity_id, identity_type, identity_value, confidence, created_at)
+            VALUES (%s, 'trace', %s, 0.8, NOW())
+            ON CONFLICT DO NOTHING
+        """, (identity_id, str(tid)))
+        bindings += 1
+
+        # Also update face_detection with identity_id
+        cur.execute("""
+            UPDATE dev.face_detections SET identity_id = %s
+            WHERE file_uuid = %s AND trace_id = %s
+        """, (identity_id, UUID, tid))
+
+conn.commit()
+print(f"Created {bindings} identity bindings for {n_clusters} identities")
+
+# Summary
+print(f"\n=== Summary ===")
+cur.execute("SELECT COUNT(*) FROM dev.identities WHERE source = 'auto'")
+print(f"Total auto-generated identities: {cur.fetchone()[0]}")
+cur.execute("SELECT COUNT(*) FROM dev.identity_bindings")
+print(f"Total identity bindings: {cur.fetchone()[0]}")
+
+cur.close()
+conn.close()
+print("=== Done ===")
@@ -0,0 +1,250 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Offline Report Generator — Uses SQLite file (no PostgreSQL needed).
+Generates comprehensive HTML report with charts, heatmaps, and vector stats.
+
+Usage:
+  python3 render_offline_report.py <uuid>.sqlite [output.html]
+  python3 render_offline_report.py <uuid>.sqlite --identity <id>
+"""
+import sys, json, sqlite3, os, argparse
+from collections import defaultdict
+
+parser = argparse.ArgumentParser()
+parser.add_argument("sqlite_path", help="Path to the .sqlite file")
+parser.add_argument("output", nargs="?", default=None, help="Output HTML path")
+parser.add_argument("--identity", "-i", type=int, default=None, help="Filter by identity_id")
+args = parser.parse_args()
+
+SQLITE_PATH = args.sqlite_path
+OUT = args.output or SQLITE_PATH.replace(".sqlite", "_report.html")
+IDENTITY = args.identity
+
+if not os.path.exists(SQLITE_PATH):
+    print(f"ERROR: {SQLITE_PATH} not found")
+    sys.exit(1)
+
+# Load sqlite-vec extension if available
+VEC_DYLIB = None
+for path in [
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), "vec0.dylib"),
+    "/tmp/vec0.dylib",
+]:
+    if os.path.exists(path):
+        VEC_DYLIB = path
+        break
+
+conn = sqlite3.connect(SQLITE_PATH)
+if VEC_DYLIB:
+    conn.enable_load_extension(True)
+    try:
+        conn.load_extension(VEC_DYLIB)
+    except:
+        pass
+    conn.enable_load_extension(False)
+c = conn.cursor()
+
+# Read video metadata
+c.execute("SELECT file_uuid, file_name, duration, fps FROM videos LIMIT 1")
+row = c.fetchone()
+if not row:
+    print("No video data found")
+    sys.exit(1)
+file_uuid, video_name, duration, fps = row[0], row[1], float(row[2] or 6785), float(row[3] or 25.0)
+sample_interval = 3  # 8Hz face detection
+hz = fps / sample_interval
+
+# Build identity filter
+identity_filter = ""
+identity_params = []
+if IDENTITY is not None:
+    identity_filter = " AND identity_id = ?"
+    identity_params = [IDENTITY]
+
+# Query trace spans
+trace_query = f"SELECT trace_id, MIN(frame_number), MAX(frame_number), MIN(timestamp_secs), MAX(timestamp_secs), COUNT(*) FROM face_detections WHERE trace_id IS NOT NULL{identity_filter} GROUP BY trace_id ORDER BY MIN(timestamp_secs)"
+c.execute(trace_query, identity_params)
+trace_spans = c.fetchall()
+
+# Query density
+density_query = f"SELECT CAST(FLOOR(timestamp_secs/5) AS INTEGER) as bkt, COUNT(*) as cnt FROM face_detections WHERE trace_id IS NOT NULL{identity_filter} GROUP BY bkt ORDER BY bkt"
+c.execute(density_query, identity_params)
+density = {r[0]: r[1] for r in c.fetchall()}
+
+# Total detections
+c.execute(f"SELECT COUNT(*) FROM face_detections WHERE 1=1{identity_filter}", identity_params)
+total_detections = c.fetchone()[0]
+
+# Trace-to-identity mapping (for tooltips)
+trace_to_identity = {}
+c.execute("SELECT DISTINCT trace_id, identity_id FROM face_detections WHERE trace_id IS NOT NULL AND identity_id IS NOT NULL")
+for tid, iid in c.fetchall():
+    trace_to_identity[tid] = iid
+# Get identity names
+id_names = {}
+if trace_to_identity:
+    unique_ids = set(trace_to_identity.values())
+    placeholders = ",".join(["?" for _ in unique_ids])
+    c.execute(f"SELECT id, name FROM identities WHERE id IN ({placeholders})", list(unique_ids))
+    id_names = {r[0]: r[1] for r in c.fetchall()}
+
+# Identity info
+identity_info = None
+if IDENTITY is not None:
+    c.execute("SELECT id, name, identity_type, source, status FROM identities WHERE id=?", [IDENTITY])
+    r = c.fetchone()
+    if r:
+        identity_info = {"id": r[0], "name": r[1], "type": r[2], "source": r[3], "status": r[4]}
+else:
+    c.execute("SELECT identity_id, COUNT(*) as fc, COUNT(DISTINCT trace_id) as tc FROM face_detections WHERE identity_id IS NOT NULL GROUP BY identity_id ORDER BY fc DESC LIMIT 10")
+    top_identities = c.fetchall()
+
+# TKG stats
+c.execute("SELECT COUNT(*) FROM tkg_nodes")
+tkg_nodes = c.fetchone()[0]
+c.execute("SELECT node_type, COUNT(*) FROM tkg_nodes GROUP BY node_type")
+tkg_types = dict(c.fetchall())
+c.execute("SELECT COUNT(*) FROM tkg_edges")
+tkg_edges = c.fetchone()[0]
+
+# Vector counts
+vec_counts = {}
+for tbl in ["chunk_embeddings", "face_embeddings", "voice_embeddings"]:
+    try:
+        c.execute(f"SELECT COUNT(*) FROM {tbl}")
+        vec_counts[tbl] = c.fetchone()[0]
+    except:
+        vec_counts[tbl] = 0
+
+c.close()
+conn.close()
+
+BUCKET = 5
+num_buckets = int(duration / BUCKET) + 1
+max_density = max(density.values()) if density else 1
+
+def build_html():
+    h = []
+    h.append('<!DOCTYPE html><html><head><meta charset="utf-8"><title>Offline Report — {}</title>'.format(video_name[:50]))
+    h.append('<style>')
+    h.append('body{font-family:-apple-system,BlinkMacSystemFont,sans-serif;margin:20px;background:#0d1117;color:#c9d1d9}')
+    h.append('h1,h2{color:#e94560}')
+    h.append('.stats{display:flex;gap:12px;margin:8px 0;flex-wrap:wrap}')
+    h.append('.stat{background:#161b22;padding:6px 14px;border-radius:6px}')
+    h.append('.stat .num{font-size:20px;font-weight:bold;color:#e94560}')
+    h.append('.stat .label{font-size:10px;color:#8b949e}')
+    h.append('.viz{position:relative;background:#0d1117;border:1px solid #30363d;margin:8px 0;overflow:hidden}')
+    h.append('.bar{display:block;position:absolute;height:3px;background:#e94560;opacity:0.7;border-radius:1px}')
+    h.append('.bar:hover{height:8px;opacity:1}')
+    h.append('table{border-collapse:collapse;width:100%;color:#c9d1d9}')
+    h.append('th{background:#161b22;text-align:left;padding:6px 10px}')
+    h.append('td{padding:4px 10px;border-bottom:1px solid #21262d}')
+    h.append('</style></head><body>')
+    
+    sub = " (identity: {})".format(identity_info["name"]) if identity_info else ""
+    h.append('<h1>📊 Offline Report — {}{}</h1>'.format(video_name[:60], sub))
+    h.append('<div style="color:#666;font-size:11px;margin-bottom:10px">Source: {} | Generated: offline (SQLite)</div>'.format(os.path.basename(SQLITE_PATH)))
+    
+    # Identity card
+    if identity_info:
+        h.append('<div style="background:#161b22;border:1px solid #30363d;border-radius:8px;padding:16px;margin:12px 0">')
+        h.append('<h3 style="margin:0;color:#e94560">Identity Details</h3>')
+        h.append('<table><tr><td style="color:#8b949e;width:80px">ID</td><td>{}</td></tr>'.format(identity_info["id"]))
+        h.append('<tr><td style="color:#8b949e">Name</td><td style="font-weight:bold">{}</td></tr>'.format(identity_info["name"]))
+        h.append('<tr><td style="color:#8b949e">Type</td><td>{}</td></tr>'.format(identity_info["type"]))
+        h.append('<tr><td style="color:#8b949e">Source</td><td>{}</td></tr>'.format(identity_info["source"]))
+        h.append('<tr><td style="color:#8b949e">Status</td><td>{}</td></tr>'.format(identity_info["status"]))
+        h.append('</table></div>')
+    
+    # Stats row
+    h.append('<div class="stats">')
+    h.append('<div class="stat"><div class="num">{:,}</div><div class="label">traces</div></div>'.format(len(trace_spans)))
+    h.append('<div class="stat"><div class="num">{:,}</div><div class="label">detections</div></div>'.format(total_detections))
+    h.append('<div class="stat"><div class="num">{:.0f}s</div><div class="label">duration</div></div>'.format(duration))
+    h.append('<div class="stat"><div class="num">{}</div><div class="label">max/{}s</div></div>'.format(max_density, BUCKET))
+    h.append('<div class="stat"><div class="num">{:.0f}fps</div><div class="label">video fps</div></div>'.format(fps))
+    h.append('<div class="stat"><div class="num">{:.0f}Hz</div><div class="label">sample rate</div></div>'.format(hz))
+    h.append('<div class="stat"><div class="num">{:,}</div><div class="label">{}s buckets</div></div>'.format(num_buckets, BUCKET))
+    h.append('</div>')
+    
+    # Database summary
+    h.append('<h2>Database Contents</h2>')
+    h.append('<table>')
+    h.append('<tr><th>Table</th><th style="text-align:right">Rows</th><th>Type</th></tr>')
+    for name, count in [
+        ("videos", 1), ("chunk", len(trace_spans)),
+        ("face_detections", total_detections), ("identities", len(id_names) if not IDENTITY else 1),
+        ("tkg_nodes", tkg_nodes), ("tkg_edges", tkg_edges),
+    ]:
+        h.append('<tr><td>{}</td><td style="text-align:right">{:,}</td><td>flat</td></tr>'.format(name, count))
+
+    for name, dim in [("chunk_embeddings", 768), ("face_embeddings", 512), ("voice_embeddings", 192)]:
+        count = vec_counts.get(name, 0)
+        h.append('<tr><td>{}</td><td style="text-align:right">{:,}</td><td>vec0 ({}D)</td></tr>'.format(name, count, dim))
+    h.append('</table>')
+    
+    # TKG breakdown
+    if tkg_types:
+        h.append('<h2>TKG Nodes</h2>')
+        h.append('<div class="stats">')
+        for ntype, cnt in sorted(tkg_types.items()):
+            h.append('<div class="stat"><div class="num">{:,}</div><div class="label">{}</div></div>'.format(cnt, ntype))
+        h.append('</div>')
+    
+    # 1. Density histogram
+    h.append('<h2>Face Density Over Time</h2>')
+    w_px = num_buckets * 2 + 20
+    h.append('<div class="viz" style="width:{}px;height:80px">'.format(w_px))
+    for b in range(num_buckets):
+        v = density.get(b, 0)
+        h_px = max(2, int(60 * v / max(1, max_density * 0.6))) if v > 0 else 0
+        if v == 0:
+            color = "#0d1117"
+        else:
+            i = min(v / (max(1, max_density * 0.5)), 1.0)
+            r = int(233 * i + 13 * (1 - i))
+            g = int(69 * i + 13 * (1 - i))
+            bv = int(96 * i + 23 * (1 - i))
+            color = "rgb({},{},{})".format(r, g, bv)
+        h.append('<span style="position:absolute;left:{}px;bottom:0;width:2px;height:{}px;background:{}" title="{}s: {} faces"></span>'.format(b*2+10, h_px, color, b*BUCKET, v))
+    h.append('</div>')
+    
+    # 2. Trace timeline
+    h.append('<h2>Trace Timeline</h2>')
+    show_traces = min(len(trace_spans), 2000)
+    bar_h = 2
+    chart_height = show_traces * (bar_h + 1) + 10
+    h.append('<div class="viz" style="width:{}px;height:{}px">'.format(w_px, chart_height))
+    for i, (tid, fn0, fn1, t0, t1, cnt) in enumerate(trace_spans[:show_traces]):
+        left = int(t0 / duration * (w_px - 20)) + 10
+        width = max(3, int((t1 - t0) / duration * (w_px - 20)))
+        top = i * (bar_h + 1) + 5
+        opacity = 1.0 if cnt > 5 else 0.3
+        identity_note = ""
+        iid = trace_to_identity.get(tid)
+        if iid and iid in id_names:
+            identity_note = ", identity: {}".format(id_names[iid])
+        h.append('<span class="bar" style="left:{}px;top:{}px;width:{}px;height:{}px;opacity:{}" title="T{}: {:.0f}s–{:.0f}s, {} faces{}"></span>'.format(
+            left, top, width, bar_h, opacity, tid, t0, t1, cnt, identity_note))
+    h.append('</div>')
+    
+    # 3. Top identities
+    if not IDENTITY and top_identities:
+        h.append('<h2>Top Identities</h2>')
+        h.append('<table>')
+        h.append('<tr><th>ID</th><th>Name</th><th style="text-align:right">Faces</th><th style="text-align:right">Traces</th></tr>')
+        for iid, fc, tc in top_identities:
+            name = id_names.get(iid, "#{}".format(iid))[:50]
+            h.append('<tr><td style="color:#8b949e">{}</td><td>{}</td><td style="text-align:right">{:,}</td><td style="text-align:right">{}</td></tr>'.format(iid, name, fc, tc))
+        h.append('</table>')
+    
+    h.append('</body></html>')
+    return '\n'.join(h)
+
+html = build_html()
+with open(OUT, 'w') as f:
+    f.write(html)
+
+print("Saved: {}".format(OUT))
+print("Traces: {}, Detections: {}, Duration: {:.0f}s, Sample: {:.0f}Hz".format(len(trace_spans), total_detections, duration, hz))
+print("Size: {:.0f}KB".format(len(html) / 1024))
@@ -0,0 +1,164 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Speaker Assignment: cluster voice vectors from Qdrant, assign speaker IDs to DB chunks.
+"""
+import json, sys, time
+import psycopg2
+import numpy as np
+from urllib.request import Request, urlopen
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.metrics.pairwise import cosine_similarity
+
+UUID = sys.argv[1] if len(sys.argv) > 1 else "23b1c872379d4ec06479e5ed39eef4c5"
+QDRANT = "http://localhost:6333"
+DB = "dbname=momentry user=accusys"
+COLLECTION = "momentry_dev_voice"
+
+print(f"=== Speaker Assignment for {UUID} ===")
+
+# Step 1: Read voice vectors from Qdrant
+print("Reading voice vectors from Qdrant...")
+vectors = []
+chunk_ids = []
+# We need to scroll through all points
+offset = None
+while True:
+    data = {"limit": 100, "with_payload": True, "with_vector": True}
+    if offset is not None:
+        data["offset"] = offset
+    req = Request(f"{QDRANT}/collections/{COLLECTION}/points/scroll",
+        data=json.dumps(data).encode(),
+        headers={"Content-Type": "application/json"}, method="POST")
+    resp = json.loads(urlopen(req).read())
+    result = resp["result"]
+    points = result.get("points", [])
+    if not points:
+        break
+    for pt in points:
+        payload = pt.get("payload", {})
+        cid = payload.get("chunk_id", "")
+        # Only get vectors for THIS UUID's chunks
+        # Filter by checking DB later, or rely on Qdrant payload
+        vectors.append(pt["vector"])
+        chunk_ids.append(cid)
+    offset = result.get("next_page_offset")
+    if offset is None:
+        break
+    print(f"  Read {len(vectors)} vectors...")
+
+print(f"Total vectors: {len(vectors)}")
+
+# Step 2: Filter to only our UUID's chunks (from DB)
+conn = psycopg2.connect(DB)
+cur = conn.cursor()
+cur.execute("SELECT chunk_id FROM dev.chunk WHERE file_uuid = %s AND chunk_type = 'sentence' ORDER BY id", (UUID,))
+db_chunk_ids = set(row[0] for row in cur.fetchall())
+print(f"DB chunk_ids: {len(db_chunk_ids)}")
+
+# Filter vectors to match DB chunks
+filtered_vectors = []
+filtered_chunk_ids = []
+for v, cid in zip(vectors, chunk_ids):
+    if cid in db_chunk_ids:
+        filtered_vectors.append(v)
+        filtered_chunk_ids.append(cid)
+
+vectors = filtered_vectors
+chunk_ids = filtered_chunk_ids
+print(f"Matched vectors: {len(vectors)}")
+
+# Sort by chunk_id (which is numeric string)
+indices = sorted(range(len(chunk_ids)), key=lambda i: int(chunk_ids[i]) if chunk_ids[i].isdigit() else 0)
+vectors = [vectors[i] for i in indices]
+chunk_ids = [chunk_ids[i] for i in indices]
+
+# Step 3: Read speaker_change from asr.json
+asr_path = f"/Users/accusys/momentry/output_dev/{UUID}.asr.json"
+with open(asr_path) as f:
+    asr_data = json.load(f)
+segments = asr_data.get("segments", [])
+speaker_changes = {}
+for seg in segments:
+    speaker_changes[seg["chunk_id"]] = seg.get("speaker_change", False)
+
+# Step 4: Cluster embeddings
+print("Clustering...")
+X = np.array(vectors)
+
+# Compute cosine distance matrix
+# Cosine distance = 1 - cosine_similarity
+cos_sim = cosine_similarity(X)
+cos_dist = 1 - cos_sim
+
+# Use AgglomerativeClustering with cosine distance
+# Determine optimal n_clusters by looking at speaker_change boundaries
+# First pass: use speaker_change as hard boundaries to get initial clusters
+# Then refine
+
+# Simpler: use a distance threshold
+n = len(vectors)
+labels = np.full(n, -1, dtype=int)
+current_speaker = 0
+
+# Start with first chunk as speaker 0
+labels[0] = current_speaker
+centroids = [np.array(vectors[0])]  # per-cluster centroid
+
+for i in range(1, n):
+    has_change = speaker_changes.get(chunk_ids[i], False)
+    vec = np.array(vectors[i])
+
+    if has_change:
+        # Speaker change: check if this is a NEW speaker or returning to a previous one
+        # Compare with centroid of current speaker vs others
+        similarities = [float(np.dot(vec, c) / (np.linalg.norm(vec) * np.linalg.norm(c) + 1e-10)) for c in centroids]
+        best_sim = max(similarities) if similarities else 0
+        best_cluster = similarities.index(best_sim) if similarities else 0
+
+        if best_sim > 0.65 and best_cluster != current_speaker:
+            # Returning to a previous speaker
+            labels[i] = best_cluster
+        elif best_sim < 0.55:
+            # New speaker
+            current_speaker = len(centroids)
+            labels[i] = current_speaker
+            centroids.append(vec)
+        else:
+            # Stay with current speaker (false change detection)
+            labels[i] = current_speaker
+            centroids[current_speaker] = (centroids[current_speaker] + vec) / 2
+    else:
+        # No speaker change: same speaker as previous
+        labels[i] = current_speaker
+        centroids[current_speaker] = (centroids[current_speaker] + vec) / 2
+
+n_speakers = len(set(labels))
+print(f"Identified {n_speakers} unique speakers")
+
+# Step 5: Update DB chunks with speaker assignment
+print("Updating DB chunks...")
+# Map: chunk_id -> speaker_id
+speaker_map = {}
+for cid, label in zip(chunk_ids, labels):
+    speaker_map[cid] = f"SPEAKER_{label}"
+
+updated = 0
+for cid, spk_id in speaker_map.items():
+    cur.execute("""
+        UPDATE dev.chunk SET metadata = COALESCE(metadata, '{}'::jsonb) || %s::jsonb
+        WHERE file_uuid = %s AND chunk_id = %s AND chunk_type = 'sentence'
+    """, (json.dumps({"speaker_id": spk_id}), UUID, cid))
+    updated += 1
+
+conn.commit()
+print(f"Updated {updated} chunks with speaker IDs")
+
+# Step 6: Save speaker map
+speaker_map_path = f"/Users/accusys/momentry/output_dev/{UUID}.speaker_map.json"
+with open(speaker_map_path, "w") as f:
+    json.dump({"speakers": n_speakers, "assignments": speaker_map}, f, indent=2)
+print(f"Speaker map saved: {speaker_map_path}")
+
+cur.close()
+conn.close()
+print("=== Done ===")
@@ -1,204 +0,0 @@
-#!/opt/homebrew/bin/python3.11
-"""
-Split ASR segments at detected speaker change points.
-Uses ECAPA-TDNN sub-window classification against reference centroids.
-
-Output: new asrx_fine.json with fine-grained segments + parent_asr_idx reference.
-"""
-import json, sys, os, time, argparse, subprocess, tempfile, shutil
-import numpy as np
-from collections import Counter
-from pathlib import Path
-
-sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
-sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "asrx_self"))
-from main_fixed import SelfASRXFixed
-from speaker_encoder import extract_speaker_embedding, normalize_embeddings
-import torchaudio, psycopg2
-
-SUB_WIN = 0.5
-SUB_STRIDE = 0.25
-CHANGE_CONFIRM = 2
-MIN_DUR = 0.7
-BATCH_SIZE = 500
-
-def load_reference(uuid, db_url):
-    conn = psycopg2.connect(db_url)
-    cur = conn.cursor()
-    cur.execute("SELECT chunk_index, metadata->>'new_speaker_name' FROM dev.chunks WHERE file_uuid=%s AND chunk_type='sentence' ORDER BY chunk_index", (uuid,))
-    name_by_idx = dict(cur.fetchall())
-    conn.close()
-    
-    asrx_path = f"/Users/accusys/momentry/output_dev/{uuid}.asrx.json"
-    asrx_full = json.load(open(asrx_path))
-    ref = {"Cary Grant": [], "Audrey Hepburn": [], "Unknown": []}
-    for i, seg in enumerate(asrx_full["segments"]):
-        name = name_by_idx.get(i, "Unknown")
-        if name in ref and i < len(asrx_full.get("embeddings", [])):
-            ref[name].append(np.array(asrx_full["embeddings"][i]))
-    
-    centroids = {}
-    for name, el in ref.items():
-        if el:
-            c = np.mean(el, axis=0)
-            centroids[name] = c / (np.linalg.norm(c) + 1e-10)
-    name_to_speaker = {}
-    for i, seg in enumerate(asrx_full["segments"]):
-        name = name_by_idx.get(i, "Unknown")
-        sid = seg["speaker_id"]
-        name_to_speaker.setdefault(name, sid)
-    return centroids, name_to_speaker
-
-def extract_audio(video_path, sr=16000):
-    tmp = tempfile.mkdtemp(prefix="asr_split_")
-    wav = os.path.join(tmp, "audio.wav")
-    subprocess.run(["ffmpeg", "-y", "-v", "quiet", "-i", video_path,
-        "-ar", str(sr), "-ac", "1", "-sample_fmt", "s16", wav], check=True, capture_output=True, timeout=300)
-    wav_data, sr_actual = torchaudio.load(wav)
-    if wav_data.shape[0] > 1:
-        wav_data = wav_data.mean(dim=0, keepdim=True)
-    return wav_data, sr_actual, tmp
-
-def classify(emb, centroids):
-    return max(centroids, key=lambda n: float(np.dot(emb, centroids[n])))
-
-def process_batch(asr_segs, wav, sr, centroids, encoder, offset_start=0):
-    ws = int(SUB_WIN * sr)
-    sw = int(SUB_STRIDE * sr)
-    results = []
-    for si, s in enumerate(asr_segs):
-        st = s["start"] - offset_start
-        et = s["end"] - offset_start
-        dur = et - st
-        
-        if dur < 1.0:
-            a = wav[:, int(st*sr):int(et*sr)]
-            e = extract_speaker_embedding(encoder, a.numpy(), sr)
-            e /= np.linalg.norm(e) + 1e-10
-            results.append((s["start"], s["end"], classify(e, centroids), si))
-            continue
-        
-        ss = int(st*sr); se = int(et*sr)
-        sub_e, sub_t = [], []
-        for wpos in range(ss, se-ws+1, sw):
-            chunk = wav[:, wpos:wpos+ws]
-            sub_e.append(extract_speaker_embedding(encoder, chunk.numpy(), sr))
-            sub_t.append(wpos/sr + offset_start)
-        
-        if len(sub_e) < 3:
-            a = wav[:, ss:se]
-            e = extract_speaker_embedding(encoder, a.numpy(), sr)
-            e /= np.linalg.norm(e) + 1e-10
-            results.append((s["start"], s["end"], classify(e, centroids), si))
-            continue
-        
-        sub_e = normalize_embeddings(np.array(sub_e))
-        names = []
-        for i in range(len(sub_e)):
-            names.append(classify(sub_e[i], centroids))
-        
-        # Smooth
-        sm = list(names)
-        for i in range(1, len(names)-1):
-            sm[i] = Counter(names[max(0,i-1):min(len(names),i+2)]).most_common(1)[0][0]
-        
-        # Find splits
-        splits = []
-        prev = sm[0]
-        for i in range(1, len(sm)):
-            if sm[i] != prev:
-                if i+CHANGE_CONFIRM < len(sm) and all(sm[i]==sm[j] for j in range(i, i+CHANGE_CONFIRM+1)):
-                    splits.append(sub_t[i]); prev = sm[i]
-                elif i+CHANGE_CONFIRM >= len(sm):
-                    splits.append(sub_t[i]); prev = sm[i]
-        
-        if not splits:
-            results.append((s["start"], s["end"], Counter(names).most_common(1)[0][0], si))
-        else:
-            boundaries = [s["start"]] + splits + [s["end"]]
-            for pi in range(len(boundaries)-1):
-                ps, pe = boundaries[pi], boundaries[pi+1]
-                if pe-ps < MIN_DUR: continue
-                sub_i = [i for i, t in enumerate(sub_t) if ps <= t < pe]
-                lbl = Counter([names[i] for i in sub_i]).most_common(1)[0][0] if sub_i else Counter(names).most_common(1)[0][0]
-                results.append((round(ps,2), round(pe,2), lbl, si))
-    
-    return results
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--uuid", default="aeed71342a899fe4b4c57b7d41bcb692")
-    parser.add_argument("--output", help="Output path for fine ASRX JSON")
-    args = parser.parse_args()
-    
-    UUID = args.uuid
-    BASE = "/Users/accusys/momentry/output_dev"
-    DB_URL = "postgresql://accusys@localhost:5432/momentry?host=/tmp"
-    VIDEO = "/Users/accusys/momentry/var/sftpgo/data/demo/Charade (1963) Cary Grant & Audrey Hepburn \uff5c Comedy Mystery Romance Thriller \uff5c Full Movie.mp4"
-    
-    print(f"Processing {UUID}")
-    
-    centroids, name_to_speaker = load_reference(UUID, DB_URL)
-    print(f"Centroids: {list(centroids.keys())}")
-    
-    asr = json.load(open(f"{BASE}/{UUID}.asr.json"))
-    asr_segs = asr["segments"]
-    print(f"ASR segments: {len(asr_segs)}")
-    
-    print("Extracting audio...")
-    wav, sr, tmp_dir = extract_audio(VIDEO)
-    print(f"Audio: {wav.shape[1]/sr:.0f}s")
-    
-    inst = SelfASRXFixed()
-    encoder = inst.speaker_encoder
-    
-    all_results = []
-    t0 = time.time()
-    for batch_start in range(0, len(asr_segs), BATCH_SIZE):
-        batch = asr_segs[batch_start:batch_start + BATCH_SIZE]
-        segs = process_batch(batch, wav, sr, centroids, encoder)
-        all_results.extend(segs)
-        pct = (batch_start + len(batch)) * 100 // len(asr_segs)
-        print(f"  {batch_start+len(batch)}/{len(asr_segs)} ({pct}%) -> {len(all_results)} segments [{time.time()-t0:.0f}s]")
-    
-    shutil.rmtree(tmp_dir, ignore_errors=True)
-    
-    # Build output
-    spk_stats = {}
-    out_segs = []
-    # Assign sequential SPEAKER_X IDs based on name order
-    name_order = {name: i for i, name in enumerate(sorted(set(s[2] for s in all_results)))}
-    
-    for start, end, name, asr_idx in all_results:
-        sid = f"SPEAKER_{name_order[name]}"
-        dur = end - start
-        spk_stats.setdefault(sid, {"count": 0, "duration": 0})
-        spk_stats[sid]["count"] += 1
-        spk_stats[sid]["duration"] += dur
-        out_segs.append({
-            "start_time": start,
-            "end_time": end,
-            "speaker_id": sid,
-            "speaker_name": name,
-            "parent_asr_idx": asr_idx,
-        })
-    
-    output = {
-        "uuid": UUID,
-        "language": "en",
-        "segments": out_segs,
-        "speaker_stats": spk_stats,
-        "total_asr_segments": len(asr_segs),
-        "total_fine_segments": len(out_segs),
-    }
-    
-    output_path = args.output or f"{BASE}/{UUID}.asrx_fine.json"
-    json.dump(output, open(output_path, "w"), indent=2)
-    print(f"\nSaved: {output_path}")
-    print(f"Segments: {len(out_segs)} (was {len(asr_segs)}, +{len(out_segs)-len(asr_segs)})")
-    print(f"Speakers: {len(spk_stats)}")
-    for sid, st in sorted(spk_stats.items()):
-        print(f"  {sid}: {st['count']} segs, {st['duration']:.0f}s")
-
-if __name__ == "__main__":
-    main()
@@ -0,0 +1,284 @@
+#!/opt/homebrew/bin/python3.11
+"""
+One-pass ASR + Speaker Change Detection + Split → asr.json
+"""
+import json, os, sys, time, argparse, subprocess, tempfile, shutil
+import numpy as np
+from pathlib import Path
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "asrx_self"))
+from speaker_encoder import load_speaker_encoder, extract_speaker_embedding, normalize_embeddings
+import torchaudio
+from faster_whisper import WhisperModel
+
+SUB_WIN = 0.5
+SUB_STRIDE = 0.25
+MIN_DUR = 0.3
+SIM_THRESHOLD = 0.45
+CHANGE_CONFIRM = 2
+
+def extract_audio(video_path, tmp_dir, sr=16000):
+    wav_path = os.path.join(tmp_dir, "audio.wav")
+    subprocess.run(["ffmpeg", "-y", "-v", "quiet", "-i", video_path,
+        "-ar", str(sr), "-ac", "1", "-sample_fmt", "s16", wav_path],
+        check=True, capture_output=True, timeout=300)
+    wav_data, sr_actual = torchaudio.load(wav_path)
+    if wav_data.shape[0] > 1:
+        wav_data = wav_data.mean(dim=0, keepdim=True)
+    return wav_data, sr_actual
+
+def transcribe_pass1(model, wav_path, vad_params=None):
+    print("  [faster-whisper] Transcribing...")
+    if vad_params is None:
+        vad_params = {"min_silence_duration_ms": 500, "speech_pad_ms": 200}
+    segments, info = model.transcribe(wav_path, beam_size=5,
+        vad_filter=True, word_timestamps=True, vad_parameters=vad_params)
+    pass1 = []
+    for i, seg in enumerate(segments):
+        words = []
+        if seg.words:
+            for w in seg.words:
+                words.append({"word": w.word.strip(), "start": round(w.start,3), "end": round(w.end,3)})
+        pass1.append({
+            "index": i,
+            "start": round(seg.start, 3),
+            "end": round(seg.end, 3),
+            "text": seg.text.strip(),
+            "words": words,
+        })
+    print(f"  Pass1 segments: {len(pass1)}")
+    return pass1
+
+def detect_speaker_changes(wav_data, sr, pass1_segs, encoder, progress_step=100):
+    print("  [Speaker Detection] Scanning...")
+    ws = int(SUB_WIN * sr)
+    sw = int(SUB_STRIDE * sr)
+    change_points = []  # List[List[float]] → change times per pass1 segment
+    t0 = time.time()
+
+    for si, seg in enumerate(pass1_segs):
+        st = int(seg["start"] * sr)
+        et = int(seg["end"] * sr)
+        dur = seg["end"] - seg["start"]
+
+        if dur < 1.0:
+            change_points.append([])
+            continue
+
+        sub_embs = []
+        sub_times = []
+        for wpos in range(st, et - ws + 1, sw):
+            chunk = wav_data[:, wpos:wpos+ws]
+            emb = extract_speaker_embedding(encoder, chunk.numpy(), sr)
+            emb = emb / (np.linalg.norm(emb) + 1e-10)
+            sub_embs.append(emb)
+            sub_times.append(wpos / sr)
+
+        if len(sub_embs) < 3:
+            change_points.append([])
+            continue
+
+        sub_embs = normalize_embeddings(np.array(sub_embs))
+        cps = []
+        # Require CHANGE_CONFIRM consecutive low-similarity windows before registering a change
+        low_run = 0
+        for i in range(1, len(sub_embs)):
+            sim = float(np.dot(sub_embs[i-1], sub_embs[i]))
+            if sim < SIM_THRESHOLD:
+                low_run += 1
+                if low_run >= CHANGE_CONFIRM:
+                    # Change point at the START of the low-sim run
+                    cps.append(round(sub_times[i - low_run + 1], 2))
+                    low_run = 0
+            else:
+                low_run = 0
+        change_points.append(cps)
+
+        if (si + 1) % progress_step == 0:
+            pct = (si + 1) * 100 // len(pass1_segs)
+            print(f"    {si+1}/{len(pass1_segs)} ({pct}%) [{time.time()-t0:.0f}s]")
+
+    total_changes = sum(len(cps) for cps in change_points)
+    print(f"  Speaker changes detected: {total_changes} in {len(pass1_segs)} segments ({time.time()-t0:.0f}s)")
+    return change_points
+
+def build_segments(pass1_segs, change_points, wav_data, sr, asr_model, tmp_dir, fps=24.0):
+    print("  [Split] Building final segments...")
+    final = []
+    chunk_idx = 0
+
+    for si, seg in enumerate(pass1_segs):
+        cps = change_points[si]
+        if not cps:
+            final.append({
+                "chunk_id": str(chunk_idx),
+                "pass1_index": si,
+                "start_time": seg["start"],
+                "end_time": seg["end"],
+                "start_frame": int(seg["start"] * fps),
+                "end_frame": int(seg["end"] * fps),
+                "text": seg["text"],
+            })
+            chunk_idx += 1
+            continue
+
+        seg["split"] = True
+        boundaries = [seg["start"]] + cps + [seg["end"]]
+        for pi in range(len(boundaries) - 1):
+            ps, pe = boundaries[pi], boundaries[pi+1]
+            if pe - ps < MIN_DUR:
+                continue
+
+            # Try word_timestamp mapping first (wider tolerance)
+            sub_words = [w["word"] for w in seg["words"] if w["start"] >= ps - 0.3 and w["end"] <= pe + 0.3]
+            text = " ".join(sub_words).strip() if sub_words else ""
+
+            # Fallback: call faster-whisper on the sub-audio chunk
+            if not text:
+                import soundfile as sf
+                chunk_path = os.path.join(tmp_dir, f"sub_{chunk_idx}.wav")
+                a_chunk = wav_data[:, int(ps*sr):int(pe*sr)].numpy()[0]
+                if len(a_chunk) > sr * 0.3:  # skip if < 0.3s
+                    sf.write(chunk_path, a_chunk, sr)
+                    try:
+                        sub_segs, _ = asr_model.transcribe(chunk_path, beam_size=5,
+                            vad_filter=True, vad_parameters={"min_silence_duration_ms": 100})
+                        text = " ".join(s.text.strip() for s in sub_segs)
+                    except:
+                        pass
+                    os.remove(chunk_path)
+                if not text:
+                    text = " ".join([w["word"] for w in seg["words"]
+                        if w["start"] >= ps - 0.5 and w["end"] <= pe + 0.5]).strip()
+                if not text:
+                    text = seg["text"][:60]
+
+            final.append({
+                "chunk_id": str(chunk_idx),
+                "pass1_index": si,
+                "start_time": round(ps, 3),
+                "end_time": round(pe, 3),
+                "start_frame": int(ps * fps),
+                "end_frame": int(pe * fps),
+                "text": text,
+                "speaker_change": True,
+            })
+            chunk_idx += 1
+
+    print(f"  Final segments: {len(final)}")
+    return final
+
+def voice_vectors_to_qdrant(wav_data, sr, final_segs, encoder, qdrant_url="http://localhost:6333"):
+    print("  [Voice Vectors] Extracting 192D embeddings...")
+    embeddings = []
+    t0 = time.time()
+    for si, seg in enumerate(final_segs):
+        st = int(seg["start_time"] * sr)
+        et = int(seg["end_time"] * sr)
+        a_chunk = wav_data[:, st:et]
+        emb = extract_speaker_embedding(encoder, a_chunk.numpy(), sr)
+        emb = emb / (np.linalg.norm(emb) + 1e-10)
+        embeddings.append({"chunk_id": seg["chunk_id"], "embedding": emb.tolist()})
+        if (si + 1) % 500 == 0:
+            print(f"    {si+1}/{len(final_segs)} [{time.time()-t0:.0f}s]")
+
+    print(f"  Writing to Qdrant...")
+    from urllib.request import Request, urlopen
+    batch = []
+    for i, e in enumerate(embeddings):
+        batch.append({"id": i + 1, "vector": e["embedding"],
+            "payload": {"chunk_id": e["chunk_id"], "chunk_type": "sentence"}})
+        if len(batch) >= 100:
+            req = Request(f"{qdrant_url}/collections/momentry_dev_voice/points?wait=true",
+                data=json.dumps({"points": batch}).encode(),
+                headers={"Content-Type": "application/json"}, method="PUT")
+            try: urlopen(req)
+            except: pass
+            batch = []
+    # Flush remaining
+    if batch:
+        req = Request(f"{qdrant_url}/collections/momentry_dev_voice/points?wait=true",
+            data=json.dumps({"points": batch}).encode(),
+            headers={"Content-Type": "application/json"}, method="PUT")
+        try: urlopen(req)
+        except: pass
+
+    print(f"  Voice vectors: {len(embeddings)} pts → Qdrant [{time.time()-t0:.0f}s]")
+    return embeddings
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--video", default="/Users/accusys/momentry/var/sftpgo/data/demo/Charade (1963) Cary Grant & Audrey Hepburn ｜ Comedy Mystery Romance Thriller ｜ Full Movie.mp4")
+    parser.add_argument("--output", help="Output path for asr.json", default="/Users/accusys/momentry/output_dev/aeed71342a899fe4b4c57b7d41bcb692.asr.json")
+    parser.add_argument("--sample", type=int, help="Process only first N pass1 segments (for testing)")
+    parser.add_argument("--no-qdrant", action="store_true", help="Skip Qdrant upload")
+    args = parser.parse_args()
+
+    t0 = time.time()
+
+    # Load models
+    print("=== Loading Models ===")
+    asr_model = WhisperModel("small", device="cpu", compute_type="int8")
+    print("  faster-whisper small loaded")
+    encoder = load_speaker_encoder()
+    print("  ECAPA-TDNN loaded")
+    print()
+
+    # Extract audio
+    print("=== Audio Extraction ===")
+    tmp_dir = tempfile.mkdtemp(prefix="transcribe_")
+    wav_data, sr = extract_audio(args.video, tmp_dir)
+    print(f"  Audio: {wav_data.shape[1]/sr:.0f}s, {sr}Hz")
+    wav_path = os.path.join(tmp_dir, "audio.wav")
+    print()
+
+    # Step 1: faster-whisper pass1
+    print("=== Step 1: Pass1 Transcription ===")
+    pass1_segs = transcribe_pass1(asr_model, wav_path)
+    if args.sample:
+        pass1_segs = pass1_segs[:args.sample]
+        print(f"  SAMPLE MODE: limiting to {args.sample} segments")
+    print()
+
+    # Step 2: Speaker change detection
+    print("=== Step 2: Speaker Change Detection ===")
+    change_points = detect_speaker_changes(wav_data, sr, pass1_segs, encoder)
+    print()
+
+    # Step 3: Build final segments
+    print("=== Step 3: Build Final Segments ===")
+    final_segs = build_segments(pass1_segs, change_points, wav_data, sr, asr_model, tmp_dir)
+    print()
+
+    # Step 4: Voice vectors → Qdrant
+    if not args.no_qdrant:
+        print("=== Step 4: Voice Vectors → Qdrant ===")
+        voice_vectors_to_qdrant(wav_data, sr, final_segs, encoder)
+        print()
+
+    # Step 5: Write asr.json
+    print("=== Step 5: Write asr.json ===")
+    uuid = os.path.basename(args.output).replace(".asr.json", "")
+    output = {
+        "file_uuid": uuid,
+        "pass1": pass1_segs,
+        "segments": final_segs,
+    }
+    with open(args.output, "w") as f:
+        json.dump(output, f, indent=2, ensure_ascii=False)
+    sz = os.path.getsize(args.output)
+    print(f"  {args.output} ({sz/1024:.0f} KB)")
+
+    # Cleanup
+    shutil.rmtree(tmp_dir, ignore_errors=True)
+
+    elapsed = time.time() - t0
+    print(f"\n=== Done ({elapsed:.0f}s) ===")
+    print(f"  Pass1 segments: {len(pass1_segs)}")
+    print(f"  Final segments: {len(final_segs)}")
+    fp = args.output
+    print(f"  Output: {fp}")
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,57 @@
+#!/bin/bash
+# Momentry Release Package — Verify Script
+# Usage: bash verify.sh
+
+set -euo pipefail
+DIR="$(cd "$(dirname "$0")" && pwd)"
+UUID=$(basename "$DIR")
+PG_BIN="${PG_BIN:-/Users/accusys/pgsql/18.3/bin}"
+DB_NAME="${DB_NAME:-momentry}"
+DB_USER="${DB_USER:-accusys}"
+
+echo "=== Package Verification ==="
+echo "UUID: $UUID"
+echo ""
+
+# Check files
+FILES=("data.sql" "file_info.json" "$UUID.sqlite" "$UUID.identities.json" "$UUID.asr.json" "$UUID.face.json" "$UUID.speaker_map.json")
+echo "## 1. File Integrity"
+for f in "${FILES[@]}"; do
+    if [ -f "$DIR/$f" ]; then
+        SIZE=$(ls -lh "$DIR/$f" | awk '{print $5}')
+        echo "  ✅ $f ($SIZE)"
+    else
+        echo "  ⚠️  $f (not found)"
+    fi
+done
+
+# Check DB (if accessible)
+if "$PG_BIN/psql" -U "$DB_USER" -d "$DB_NAME" -c "SELECT 1" >/dev/null 2>&1; then
+    echo ""
+    echo "## 2. Database"
+    for tbl in chunk face_detections tkg_nodes tkg_edges identities identity_bindings; do
+        COUNT=$("$PG_BIN/psql" -U "$DB_USER" -d "$DB_NAME" -t -A -c "SELECT COUNT(*) FROM dev.$tbl WHERE file_uuid='$UUID' OR uuid='$UUID'" 2>/dev/null || echo "N/A")
+        echo "  $tbl: $COUNT"
+    done
+else
+    echo ""
+    echo "## 2. Database (offline — check $UUID.sqlite)"
+    if [ -f "$DIR/$UUID.sqlite" ]; then
+        python3 -c "
+import sqlite3
+conn = sqlite3.connect('$DIR/$UUID.sqlite')
+c = conn.cursor()
+for tbl in ['chunk', 'face_detections', 'identities', 'tkg_nodes', 'tkg_edges']:
+    c.execute(f'SELECT COUNT(*) FROM {tbl}')
+    print(f'  {tbl}: {c.fetchone()[0]}')
+conn.close()
+" 2>/dev/null || echo "  (sqlite3 unavailable)"
+    fi
+fi
+
+echo ""
+echo "## 3. Pipeline Status"
+echo "  $("$PG_BIN/psql" -U "$DB_USER" -d "$DB_NAME" -t -A -c "SELECT status FROM dev.videos WHERE file_uuid='$UUID'" 2>/dev/null || echo "unknown")"
+
+echo ""
+echo "=== Verification Complete ==="