Add Charade face matching experience report

Documents the journey from Rust pipeline snowball bug through 5 iterations of pgvector-based matching to the final 11-identity centroid approach with dual-gate and ambiguity cleanup.
docs: add Identity Best-Face API requirement document for frontend team
2026-06-02 05:01:56 +08:00 · 2026-06-01 21:58:54 +08:00 · 2026-06-01 15:15:17 +08:00 · 2026-05-30 10:03:49 +08:00 · 2026-05-30 10:03:48 +08:00 · 2026-05-29 23:14:14 +08:00
1543 changed files with 303162 additions and 63547 deletions
@@ -10,7 +10,7 @@ MOMENTRY_REDIS_PREFIX=momentry_dev:

 # Worker Configuration (enabled for development)
 MOMENTRY_WORKER_ENABLED=true
-MOMENTRY_MAX_CONCURRENT=1
+MOMENTRY_MAX_CONCURRENT=6
 MOMENTRY_POLL_INTERVAL=10
 MOMENTRY_WORKER_BATCH_SIZE=5

@@ -23,13 +23,13 @@ MONGODB_URL=mongodb://localhost:27017
 MONGODB_DATABASE=momentry_dev

 # Redis (already isolated via prefix)
-REDIS_URL=redis://:accusys@localhost:6379
-REDIS_PASSWORD=accusys
+REDIS_URL=redis://127.0.0.1:6379
+# REDIS_PASSWORD not set - Redis has no password configured

 # Qdrant Vector Database - Collection isolation
 QDRANT_URL=http://localhost:6333
 QDRANT_API_KEY=Test3200Test3200Test3200
-QDRANT_COLLECTION=momentry_dev_rule1
+QDRANT_COLLECTION=momentry_dev_rule1_v2

 # Paths
 MOMENTRY_OUTPUT_DIR=/Users/accusys/momentry/output_dev
@@ -37,8 +37,8 @@ MOMENTRY_BACKUP_DIR=/Users/accusys/momentry/backup/momentry_dev
 MOMENTRY_SFTP_ROOT=/Users/accusys/momentry/var/sftpgo/data/demo/

 # Python (for processing scripts)
-MOMENTRY_PYTHON_PATH=/opt/homebrew/bin/python3.11
-MOMENTRY_SCRIPTS_DIR=/Users/accusys/momentry_core_0.1/scripts
+MOMENTRY_PYTHON_PATH=/Users/accusys/momentry_core/venv/bin/python
+MOMENTRY_SCRIPTS_DIR=/Users/accusys/momentry_core/scripts

 # Logging
 RUST_LOG=debug
@@ -67,4 +67,15 @@ REDIS_CACHE_TTL_VIDEO_META=3600
 # 多個同義詞檔案（逗號分隔），會覆蓋 MOMENTRY_SYNONYM_FILE
 # MOMENTRY_SYNONYM_FILES=/path/to/first.json,/path/to/second.json
 #
-# 示例檔案：docs/examples/custom_synonyms.json
+# 示例檔案：docs/examples/custom_synonyms.json
+
+# TMDb Integration (probe phase - auto-create identities from movie metadata)
+TMDB_API_KEY=e9cde52197f6f8df4d9db99da93db1fb
+MOMENTRY_TMDB_PROBE_ENABLED=true
+# LLM for 5W1H summary (points to M5 Gemma4)
+MOMENTRY_LLM_SUMMARY_URL=http://127.0.0.1:8082/v1/chat/completions
+MOMENTRY_LLM_SUMMARY_MODEL=google_gemma-4-26B-A4B-it-Q5_K_M.gguf
+MOMENTRY_LLM_SUMMARY_ENABLED=true
+
+# Embedding (ANE CoreML server)
+MOMENTRY_EMBED_URL=http://localhost:11436
@@ -1,70 +1,53 @@
-# Momentry Core Configuration Template
-# Copy this file to .env and customize for your environment
-# DO NOT commit .env with real credentials to version control
+# Momentry Core Environment Configuration
+# Copy this file to .env and fill in your values
+# DO NOT commit .env to version control

-# ===========================================
-# Database Configuration
-# ===========================================
-DATABASE_URL=postgres://user:password@localhost:5432/momentry
+# === Database ===
+DATABASE_URL=postgres://accusys@localhost:5432/momentry
+DATABASE_SCHEMA=dev

-# ===========================================
-# Redis Configuration
-# ===========================================
-REDIS_URL=redis://user:password@localhost:6379
-REDIS_PASSWORD=your_redis_password
-
-# ===========================================
-# MongoDB Configuration
-# ===========================================
-MONGODB_URL=mongodb://user:password@localhost:27017/admin
+# === MongoDB ===
+MONGODB_URL=mongodb://localhost:27017
 MONGODB_DATABASE=momentry
+MONGODB_CACHE_ENABLED=true

-# ===========================================
-# Qdrant Configuration
-# ===========================================
-QDRANT_URL=http://localhost:6333
-QDRANT_API_KEY=your_qdrant_api_key
+# === Redis ===
+REDIS_URL=redis://:accusys@localhost:6379
+REDIS_PASSWORD=accusys
+MOMENTRY_REDIS_PREFIX=momentry_dev:
+
+# === Qdrant ===
 QDRANT_COLLECTION=momentry_rule1

-# ===========================================
-# API Server Configuration
-# ===========================================
-API_HOST=127.0.0.1
-API_PORT=3000
+# === API Keys ===
+MOMENTRY_API_KEY=muser_your_key_here
+MOMENTRY_DEMO_API_KEY=muser_your_demo_key_here
+JWT_SECRET=your_jwt_secret_here_change_in_production
+SFTPGO_BASE_URL=http://127.0.0.1:8080

-# ===========================================
-# Directory Paths
-# ===========================================
-MOMENTRY_OUTPUT_DIR=/path/to/output
-MOMENTRY_BACKUP_DIR=/path/to/backup
-MOMENTRY_SCRIPTS_DIR=/path/to/momentry_core/scripts
+TMDB_API_KEY=your_tmdb_api_key_here
+
+# === LLM ===
+MOMENTRY_LLM_SUMMARY_URL=http://127.0.0.1:8082/v1/chat/completions
+MOMENTRY_LLM_SUMMARY_MODEL=google_gemma-4-26B-A4B-it-Q5_K_M.gguf
+MOMENTRY_LLM_SUMMARY_TIMEOUT=120
+
+# === Paths ===
+MOMENTRY_OUTPUT_DIR=/Users/accusys/momentry/output_dev
+MOMENTRY_BACKUP_DIR=/Users/accusys/momentry/backup
+MOMENTRY_SCRIPTS_DIR=/Users/accusys/momentry_core_0.1/scripts
 MOMENTRY_PYTHON_PATH=/opt/homebrew/bin/python3.11
+MOMENTRY_FFMPEG=/opt/homebrew/opt/ffmpeg-full/bin/ffmpeg
+MOMENTRY_MEDIA_BASE_URL=

-# ===========================================
-# Processor Timeouts (seconds)
-# ===========================================
+# === Encryption ===
+AUDIT_ENCRYPTION_KEY=    # 32 bytes hex (64 hex chars)
+
+# === Processor Timeouts (seconds) ===
 MOMENTRY_ASR_TIMEOUT=3600
 MOMENTRY_CUT_TIMEOUT=3600
 MOMENTRY_DEFAULT_TIMEOUT=7200

-# ===========================================
-# Watch Directories (comma separated)
-# ===========================================
-WATCH_DIRECTORIES=~/Videos,~/Downloads
-
-# ===========================================
-# Logging
-# ===========================================
-RUST_LOG=info
-# Options: trace, debug, info, warn, error
-
-# ===========================================
-# Ollama (for LLM integration)
-# ===========================================
-OLLAMA_HOST=http://localhost:11434
-
-# ===========================================
-# Model Paths
-# ===========================================
-# EMBEDDING_MODEL_PATH=./models/embedding
-# LLM_MODEL_PATH=./models/llm
+# === Server ===
+MOMENTRY_SERVER_PORT=3003
+MOMENTRY_LOG_LEVEL=info
@@ -1,92 +1,20 @@
-# Environment - Local configs (NEVER commit these)
-.env
-.env.local
-.env.*.local
-
-# Build artifacts
 target/
-venv/
-
-# Generated files
-thumbnails/
-*.asr.json
-*.probe.json
-test_asr.json
-
-# Local output (machine learning results)
-output/
-*.pt
-
-# Cache
-.ruff_cache/
-
-# OS files
 .DS_Store
-.Spotlight-V100
-.Trashes
-
-# Logs
+.env
+.env.development
+*.gguf
+*.mlpackage
+*.pt
+*.pth
+*.bin
+*.onnx
+*.zip
+*.tar.gz
+venv/
+__pycache__/
+node_modules/
+*.log
+/tmp/
 *.log

-# SSH keys (NEVER commit)
-id_*
-!id_*.pub
-
-# IDE and editor
-.vscode/
-.idea/
-*.swp
-*.swo
-*~
-
-# Documentation backups
-# docs_v1.0/ (Moved to active tracking)
-
-# Frontend dependencies
-node_modules/
-portal/src-tauri/target/
-
-# Python cache
-__pycache__/
-*.pyc
-*.pyo
-
-# Test artifacts
-test_output/
-test_output_simple/
-test_output_v2/
-*.mp4
-*.pt
-server.pid
-server.pid.*
-
-# Backup files
-*.bak
-*.backup
-*.bak[0-9]
-
-# Model files
-models/
-model_checkpoints/
-pretrained_models/
-
-# Desktop app
-momentry_desktop/
-
-# Release artifacts (track docs, ignore binaries)
-release/*.zip
-release/momentry_v*
-release/*.sql
-release/dev_data_*.sql
-release/public_schema_*.sql
-release/migrate_*.sql
-
-# But track release documentation
-!release/*.md
-!release/*.txt
-
-# Data directories
-data/
-
-# System status
-system_status_*.md
+scripts/swift_processors/.build/
@@ -11,6 +11,9 @@ Rust-based digital asset management system with video analysis and RAG capabilit
 - **絕對不可修改 n8n 工作流或設定**
 - **絕對不可修改 WordPress 或 n8n 的資料庫 table**
 - **除非是 release 作業，絕對不可動 port 3002 (production)**
+- **🔴 DELETE / REMOVE / DROP / CLEAR 任何資料前必須先問使用者「要刪嗎？」獲得明確同意後才能執行**
+- **🔴 Qdrant collection 刪除、DB truncate、檔案刪除、資料清空 — 一律要先問**
+- **🔴 不確定是否該刪 → 先問，不要自己決定**

 ### 開發範圍界定
 | 範圍 | 狀態 | 說明 |
@@ -29,10 +32,52 @@ Rust-based digital asset management system with video analysis and RAG capabilit
 | Production | 3002 | ❌ 禁止修改 | `cargo run -- server` (僅 release 時) |
 | Portal (Tauri) | 1420 | 前端開發 | `npm run tauri dev` |

+### 日誌與啟動
+| 服務 | 日誌路徑 | 啟動方式 |
+|------|----------|----------|
+| Production (3002) | `logs/momentry_3002.log` | `./run-server-3002.sh` |
+| Playground (3003) | `logs/momentry_3003.log` | `./run-server-3003.sh` |
+| Worker / 歷史 | `logs/nohup_worker_*.log` | 由 worker 自動產生 |
+
+> **注意**: 所有伺服器日誌統一存放於專案內 `logs/` 目錄。
+> 啟動腳本會自動 kill 舊程序、重 build（若需要）、並將日誌導向 `logs/`。
+
+## ⚠️ 交叉污染防制 (Cross-Contamination Prevention)
+
+**每個執行前必須評估是否會汙染其他獨立作業。**
+
+### Scope Isolation Matrix
+
+| 執行內容 | 允許的 Scope | 禁止影響 | 檢查事項 |
+|----------|-------------|----------|----------|
+| M4 delivery binary | `target/release/momentry` | Playground (3003), Production (3002) | 確認舊 process 未被誤殺 |
+| Playground server | `localhost:3003`, `dev.*` schema | Production (3002), `public.*` schema | `DATABASE_SCHEMA=dev` |
+| Production deploy | `localhost:3002`, `public.*` schema | Playground (3003), `dev.*` schema | 先停 production，不影響 playground |
+| Git commit | 只包含意圖修改的檔案 | 無關的 untracked files | `git status` 確認 stage 內容正確 |
+| CI / packaged tests | 測試環境 | 正式資料 | 測試用 DB 不能連到 production |
+| Doc changes | 指定文件 | 其他文件、程式碼 | `git diff --stat` 檢查 scope |
+| SQL migration | 目標 schema | 其他 schema、無關 table | `WHERE` clause 要精準 |
+| `sed` / `grep` / mass edit | 目標檔案集 | 非目標檔案 | 先用 `grep -c` 確認只有目標檔案匹配 |
+
+### Recent Violations / Near-Misses
+
+| 事件 | 問題 | 防止方式 |
+|------|------|----------|
+| `sed` API doc 編號 | `sed -i '' 's/.../.../g'` 改到所有行 | 先 `grep -c` 確認匹配，`git diff` 再提交 |
+| 亂加 `/api/v1/register` route | 不必要的 API 別名，汙染路由表 | 角色切換：路由設計不該由實作方決定 |
+| `API_WORKSPACE/` vs `GUIDES/` vs `REFERENCE/` vs `DESIGN/` vs `OPERATIONS/` vs `INTEGRATIONS/` | 文件放到錯誤分類 | API 文件改在 API_WORKSPACE/modules/ 編輯，`make deploy` 生成到 GUIDES/ |
+| Build release binary in plan mode | 浪費時間，無意義 | 嚴格遵守 plan/build mode 規定 |
+
+### ⛔ 嚴格測試隔離規則 (Strict Test Isolation)
+- **所有測試 (Test) 必須在 Dev (3003) 進行**。
+- **絕對禁止 (ABSOLUTELY FORBIDDEN)** 在任何測試指令、Demo 流程或 API 檢查中使用 `localhost:3002`。
+- 即使是「測試 Unregister」或「檢查版本」，若未明確標示為 "Production Deployment"，一律視為違規。
+- **預設行為**: 所有 curl, CLI, 或程式碼測試指令，預設 URL 必須為 `http://localhost:3003`。
+
 ### 違反後果
 - 修改 WordPress/n8n 可能影響 marcom 團隊工作與生產環境
 - 修改 WordPress/n8n 資料庫 table 可能破壞自動化流程與資料完整性
- 修改 port 3002 可能中斷正在使用的服務
+- 修改 port 3002 可能中斷正在使用的服務 (這是非常嚴重的錯誤)
 - 所有 dev 測試必須在 playground (3003) 進行

 ---
@@ -157,6 +202,21 @@ cargo run -- server --host 0.0.0.0 --port 3002
 # Run playground (development binary)
 cargo run --bin momentry_playground -- server
 cargo run --bin momentry_playground -- --help
+
+# Start servers (recommended — auto-build & logs to logs/)
+./run-server-3002.sh
+./run-server-3003.sh
+```
+
+### Server Logs
+All runtime logs are centralized in `logs/`:
+```bash
+# View real-time logs
+tail -f logs/momentry_3002.log
+tail -f logs/momentry_3003.log
+
+# Check recent errors
+grep -i "error\|panic\|FAIL" logs/momentry_*.log | tail -20
 ```

 ### ⚠️ CRITICAL: `cargo build --release` PROHIBITION
@@ -351,6 +411,12 @@ cargo run --features player --bin momentry_player -- -o
 - `MOMENTRY_CUT_TIMEOUT` - CUT timeout in seconds (default: 3600)
 - `MOMENTRY_DEFAULT_TIMEOUT` - Default timeout (default: 7200)

+### TMDb Integration (Face Clustering)
+- `TMDB_API_KEY` - TMDb API key for movie metadata lookup (required for `MOMENTRY_TMDB_PROBE_ENABLED=true`)
+- `MOMENTRY_TMDB_PROBE_ENABLED` - Enable TMDb probe during registration (default: `false`)
+  - Register phase: searches TMDb by filename, creates identities with tmdb_id/tmdb_profile
+  - Post-process phase: matches detected faces against TMDb identities via cosine similarity
+
 ### Synonym Expansion
 - `MOMENTRY_SYNONYM_FILES` - Comma-separated paths to synonym JSON files (e.g., `data/english_synonyms.json,data/llm_synonyms.json`)
 - `MOMENTRY_SYNONYM_FILE` - Single synonym JSON file path (deprecated, use above)
@@ -366,6 +432,7 @@ cargo run --features player --bin momentry_player -- -o
 - Monitor directory is a separate system (not Rust)
 - PythonExecutor provides unified script execution with timeout support
 - Redis 1.0.x for improved performance
+- FaceNet CoreML model (`models/facenet512.mlpackage`) replaces InsightFace for embedding extraction (MIT license, ANE-accelerated)

 ### LLM Synonym Generation

@@ -484,6 +551,40 @@ shellcheck scripts/*.sh monitor/**/*.sh

 **注意**: Hook 只檢查 error 等級的 shellcheck 問題，style 警告會顯示但不阻擋提交。

+## Gitea Sync
+
+主要 sync 管道為 Gitea：`http://192.168.110.200:3000/admin/momentry_core.git`
+
+### 產生 Access Token（首次設定）
+
+```bash
+# admin 帳號密碼為 AccusysTest!
+TOKEN=$(curl -s -X POST "http://192.168.110.200:3000/api/v1/users/admin/tokens" \
+  -u "admin:AccusysTest!" \
+  -H "Content-Type: application/json" \
+  -d '{"name":"m5max128_push","scopes":["write:repository"]}' | jq -r '.sha1')
+echo $TOKEN
+```
+
+### 設定 Remote
+
+```bash
+# 用 token 取代密碼
+git remote add origin http://admin:TOKEN@192.168.110.200:3000/admin/momentry_core.git
+
+# 同步
+git pull origin main
+git push origin main
+```
+
+### Token 記錄
+
+| 機器 | Token |
+|------|-------|
+| M5Max128 | `c33768c4cc26c0f4c575dcce832e92e5cf192773` (write:repository + write:user) |
+
+**注意**: Token 有 write:repository scope，勿外洩。如需新增 token 給其他機器，各自產自己的 token。
+
 ## Release Workflow

 ### Release 前準備
@@ -627,3 +728,93 @@ Phase 1: marcom 建構 (現在)    → Elementor 頁面建構
 Phase 2: 交付審視 (TBD)      → 功能確認 / 重構評估
 Phase 3: OpenCode 重構        → 純程式碼實作，交付無 Elementor 依賴版本
 ```
+
+## M4 通知規範
+
+### 固定通知方式
+
+通知 M4 的唯一管道：**`M4_workspace/` 下建立回覆文件 + `git commit`**。不需口頭、即時訊息、郵件。
+
+### 命名規則
+
+```
+docs_v1.0/M4_workspace/YYYY-MM-DD_<topic>_response.md   (回覆 M4 問題)
+docs_v1.0/M4_workspace/YYYY-MM-DD_<topic>.md             (主動通報)
+docs_v1.0/M4_workspace/YYYY-MM-DD_<topic>_test_report.md (測試報告)
+```
+
+### 觸發時機
+
+| 情境 | 動作 |
+|------|------|
+| M4 提交問題報告到 `M4_workspace/` | 修復後，回覆 `*_response.md` |
+| 完成 M4 要求的任務 | 回覆 `*_response.md` |
+| 重大變更（模型替換、架構變更） | 主動通知 `*.md` |
+| 新測試包產出 | `*_test_report.md` |
+
+### 交付檢查
+
+1. 文件寫入 `docs_v1.0/M4_workspace/`
+2. `git add` 包含該文件
+3. `git commit` 含相關變更
+4. M4 透過 git log 查看
+
+詳細規範見 `docs_v1.0/M4_workspace/M4_NOTIFICATION_PROTOCOL.md`。
+
+## UUID Naming Rule
+
+**Never use bare `uuid` in API route paths, query params, JSON keys, or code variable names. Always qualify:**
+
+| Context | Must use | Never |
+|---------|----------|-------|
+| Video/file resource | `file_uuid` | `uuid` |
+| Identity resource | `identity_uuid` | `uuid` |
+| Query parameter | `file_uuid=`, `identity_uuid=` | `uuid=` |
+| Route path | `:file_uuid`, `:identity_uuid` | `:uuid` |
+| JSON key | `"file_uuid"`, `"identity_uuid"` | `"uuid"` |
+
+This applies to docs, code, API responses, and curl examples. Exceptions: internal database primary key names (e.g. `identities.uuid` column).
+
+## Document Compliance Checklist
+
+Before creating any file in `docs_v1.0/` (API_WORKSPACE, GUIDES, REFERENCE, DESIGN, OPERATIONS, INTEGRATIONS), verify all items below.
+**IMPORTANT**: API functional documents are generated from `API_WORKSPACE/modules/`. Edit modules there, then run `make deploy` in `API_WORKSPACE/` to update `GUIDES/`. Never edit generated files in `GUIDES/` directly. See `DESIGN/Modular_Doc_System_V1.0.md` for the full system design.
+
+### P0 — Mandatory (7 items)
+
+| # | Check | Rule |
+|---|-------|------|
+| 1 | YAML frontmatter | `title`, `version`, `date`, `author`, `status` present |
+| 2 | Version history | Table at bottom of file tracking changes |
+| 3 | Top info table | scope, status, applicable to, etc. |
+| 4 | PascalCase filename | e.g. `DetectorRegistry.md`, not `detector_registry.md` |
+| 5 | `_` separator | Within filenames use `_`, never spaces or other chars |
+| 6 | English content | Entire file in English |
+| 7 | Correct directory | File must reside in appropriate directory: `API_WORKSPACE/modules/` (API endpoint modules), `GUIDES/` (user docs, generated), `REFERENCE/` (data models), `DESIGN/` (architecture), `OPERATIONS/` (infra/release), `INTEGRATIONS/` (n8n/tests) |
+
+### P0b — UUID Naming
+
+| # | Check | Rule |
+|---|-------|------|
+| 8 | `file_uuid` not bare `uuid` | All file references use `file_uuid` (see UUID Naming Rule above) |
+| 9 | `identity_uuid` not bare `uuid` | All identity references use `identity_uuid` |
+
+### P1 — Suggested (3 items)
+
+| # | Check | Note |
+|---|-------|------|
+| 1 | Cross-references | Link to related docs in API_WORKSPACE/, GUIDES/, REFERENCE/, DESIGN/, OPERATIONS/ |
+| 2 | Glossary terms | Define non-obvious terms inline or link glossary |
+| 3 | Diagrams | Include Mermaid/ASCII diagram for complex topics |
+
+### Exception
+
+`M4_workspace/` files are exempt from this checklist (free-format reply documents).
+
+---
+
+## Delivery Procedure
+
+完整交付程序（M4_workspace → M5 → Release → Deploy → Public）見：
+
+`docs_v1.0/OPERATIONS/DELIVERY_PROCEDURE.md`
@@ -1,143 +0,0 @@
-# Changelog
-
-All notable changes to this project will be documented in this file.
-
-The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
-
-## [Unreleased]
-
-### Added
- Gitea API token integration
- n8n API key integration
- API key caching with Moka
- Rate limiting for API key validation
- Constant-time hash comparison
- OpenAPI documentation with utoipa
-
-## [0.1.0] - 2026-03-21
-
-### Added
-
-#### API Key Management System
- API key generation with secure random (UUID v4)
- SHA256 key hashing
- 5 key types: System, User, Service, Integration, Emergency
- Key expiration with configurable TTL
- Grace period for key rotation
-
-#### Anomaly Detection
- High request rate detection (>1000/min)
- High error rate detection (>50%)
- Multiple IP detection (>5/hour)
- Unusual time activity detection
- Redis Pub/Sub for anomaly alerts
-
-#### Rotation Mechanism
- Automatic rotation scheduling
- Manual rotation requests
- Forced rotation for security incidents
- Grace period management per key type:
-  - System: 72 hours
-  - User: 24 hours
-  - Service: 48 hours
-  - Integration: 24 hours
-  - Emergency: 0 hours (immediate)
-
-#### PostgreSQL Integration
- `api_keys` table for key storage
- `api_key_audit_log` table for audit trail
- `api_key_anomalies` table for anomaly records
- Full CRUD operations for API keys
-
-#### Redis Integration
- Anomaly alert Pub/Sub (`momentry:anomaly:alerts`)
- Key anomaly state tracking
- Real-time alert notifications
-
-#### CLI Commands
- `momentry api-key create` - Create new API key
- `momentry api-key list` - List all API keys
- `momentry api-key validate` - Validate an API key
- `momentry api-key revoke` - Revoke an API key
- `momentry api-key rotate` - Request key rotation
- `momentry api-key stats` - Show statistics
-
-#### Gitea Integration
- Create Gitea Personal Access Tokens
- List user tokens
- Delete tokens
- Local token tracking
- CLI commands:
-  - `momentry gitea create`
-  - `momentry gitea list`
-  - `momentry gitea delete`
-  - `momentry gitea verify`
-
-#### n8n Integration
- Create n8n API keys
- List API keys
- Delete API keys
- Local key tracking
- CLI commands:
-  - `momentry n8n create`
-  - `momentry n8n list`
-  - `momentry n8n delete`
-  - `momentry n8n verify`
-
-#### Security Features
- Constant-time hash comparison (subtle crate)
- Rate limiting for validation attempts
- IP-based lockout after failed attempts
- Configurable thresholds via environment variables
-
-#### Performance Optimizations
- Moka-based API key validation cache
- Configurable TTL and capacity
- Reduced database queries for hot keys
-
-#### Documentation
- API Key Management design document
- Redis user configuration guide
- Gitea token integration guide
- n8n API key integration guide
- Optimization plan with task codes
-
-### Environment Variables
-
-#### API Key Configuration
-```
-CACHE_TTL_SECONDS=300          # Cache TTL (default: 300)
-CACHE_MAX_CAPACITY=10000       # Max cache entries (default: 10000)
-RATE_LIMIT_MAX_ATTEMPTS=5      # Max failed attempts (default: 5)
-RATE_LIMIT_WINDOW_SECONDS=900  # Lockout duration (default: 900)
-```
-
-#### Service URLs
-```
-GITEA_URL=http://localhost:3000
-N8N_URL=https://n8n.momentry.ddns.net
-```
-
-### Database Schema
-
-#### Tables Created
- `api_keys` - API key storage
- `api_key_audit_log` - Audit trail
- `api_key_anomalies` - Anomaly records
- `gitea_tokens` - Gitea token tracking
- `n8n_api_keys` - n8n API key tracking
-
-### Dependencies Added
- `uuid` - UUID generation
- `subtle` - Constant-time comparison
- `moka` - Async cache
- `utoipa` - OpenAPI documentation
- `utoipa-swagger-ui` - Swagger UI
-
---
-
-## Version History
-
-| Version | Date | Description |
-|---------|------|-------------|
-| 0.1.0 | 2026-03-21 | Initial release with API Key Management |
@@ -166,6 +166,30 @@ version = "1.2.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "03918c3dbd7701a85c6b9887732e2921175f26c350b4563841d0958c21d57e6d"

+[[package]]
+name = "argon2"
+version = "0.5.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "3c3610892ee6e0cbce8ae2700349fcf8f98adb0dbfbee85aec3c9179d29cc072"
+dependencies = [
+ "base64ct",
+ "blake2",
+ "cpufeatures",
+ "password-hash",
+]
+
+[[package]]
+name = "async-compression"
+version = "0.4.42"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e79b3f8a79cccc2898f31920fc69f304859b3bd567490f75ebf51ae1c792a9ac"
+dependencies = [
+ "compression-codecs",
+ "compression-core",
+ "pin-project-lite",
+ "tokio",
+]
+
 [[package]]
 name = "async-lock"
 version = "3.4.2"
@@ -378,6 +402,15 @@ dependencies = [
 "wyz",
 ]

+[[package]]
+name = "blake2"
+version = "0.10.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "46502ad458c9a52b69d4d4d32775c788b7a1b85e8bc9d482d92250fc0e3f8efe"
+dependencies = [
+ "digest",
+]
+
 [[package]]
 name = "block-buffer"
 version = "0.10.4"
@@ -594,6 +627,23 @@ dependencies = [
 "static_assertions",
 ]

+[[package]]
+name = "compression-codecs"
+version = "0.4.38"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ce2548391e9c1929c21bf6aa2680af86fe4c1b33e6cea9ac1cfeec0bd11218cf"
+dependencies = [
+ "compression-core",
+ "flate2",
+ "memchr",
+]
+
+[[package]]
+name = "compression-core"
+version = "0.4.32"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cc14f565cf027a105f7a44ccf9e5b424348421a1d8952a8fc9d499d313107789"
+
 [[package]]
 name = "concurrent-queue"
 version = "2.5.0"
@@ -1564,6 +1614,12 @@ dependencies = [
 "pin-project-lite",
 ]

+[[package]]
+name = "http-range-header"
+version = "0.4.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9171a2ea8a68358193d15dd5d70c1c10a2afc3e7e4c5bc92bc9f025cebd7359c"
+
 [[package]]
 name = "httparse"
 version = "1.10.1"
@@ -2052,6 +2108,21 @@ dependencies = [
 "wasm-bindgen",
 ]

+[[package]]
+name = "jsonwebtoken"
+version = "9.3.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5a87cc7a48537badeae96744432de36f4be2b4a34a05a5ef32e9dd8a1c169dde"
+dependencies = [
+ "base64 0.22.1",
+ "js-sys",
+ "pem",
+ "ring",
+ "serde",
+ "serde_json",
+ "simple_asn1",
+]
+
 [[package]]
 name = "kqueue"
 version = "1.1.1"
@@ -2219,6 +2290,15 @@ dependencies = [
 "winapi",
 ]

+[[package]]
+name = "matchers"
+version = "0.2.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d1525a2a28c7f4fa0fc98bb91ae755d1e2d1505079e05539e35bc876b5d65ae9"
+dependencies = [
+ "regex-automata",
+]
+
 [[package]]
 name = "matches"
 version = "0.1.10"
@@ -2340,10 +2420,11 @@ dependencies = [

 [[package]]
 name = "momentry_core"
-version = "0.1.0"
+version = "1.0.0"
 dependencies = [
 "aes-gcm",
 "anyhow",
+ "argon2",
 "async-trait",
 "atty",
 "axum",
@@ -2358,6 +2439,7 @@ dependencies = [
 "futures-util",
 "hex",
 "jieba-rs",
+ "jsonwebtoken",
 "libc",
 "mac_address",
 "md5",
@@ -2369,6 +2451,7 @@ dependencies = [
 "qdrant-client",
 "ratatui",
 "redis",
+ "regex",
 "reqwest",
 "sdl2",
 "serde",
@@ -2379,6 +2462,7 @@ dependencies = [
 "tempfile",
 "thiserror 1.0.69",
 "tokio",
+ "tokio-util",
 "tower 0.4.13",
 "tower-http 0.5.2",
 "tracing",
@@ -2704,6 +2788,17 @@ dependencies = [
 "windows-link",
 ]

+[[package]]
+name = "password-hash"
+version = "0.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "346f04948ba92c43e8469c1ee6736c7563d71012b17d40745260fe106aac2166"
+dependencies = [
+ "base64ct",
+ "rand_core 0.6.4",
+ "subtle",
+]
+
 [[package]]
 name = "paste"
 version = "1.0.15"
@@ -2719,6 +2814,16 @@ dependencies = [
 "digest",
 ]

+[[package]]
+name = "pem"
+version = "3.0.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1d30c53c26bc5b31a98cd02d20f25a7c8567146caf63ed593a9d87b2775291be"
+dependencies = [
+ "base64 0.22.1",
+ "serde_core",
+]
+
 [[package]]
 name = "pem-rfc7468"
 version = "0.7.0"
@@ -3869,6 +3974,18 @@ version = "0.3.9"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "703d5c7ef118737c72f1af64ad2f6f8c5e1921f818cdcb97b8fe6fc69bf66214"

+[[package]]
+name = "simple_asn1"
+version = "0.6.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0d585997b0ac10be3c5ee635f1bab02d512760d14b7c468801ac8a01d9ae5f1d"
+dependencies = [
+ "num-bigint",
+ "num-traits",
+ "thiserror 2.0.18",
+ "time",
+]
+
 [[package]]
 name = "siphasher"
 version = "1.0.2"
@@ -4750,12 +4867,21 @@ checksum = "1e9cd434a998747dd2c4276bc96ee2e0c7a2eadf3cae88e52be55a05fa9053f5"
 dependencies = [
 "bitflags 2.11.1",
 "bytes",
+ "futures-util",
 "http",
 "http-body",
 "http-body-util",
+ "http-range-header",
+ "httpdate",
+ "mime",
+ "mime_guess",
+ "percent-encoding",
 "pin-project-lite",
+ "tokio",
+ "tokio-util",
 "tower-layer",
 "tower-service",
+ "tracing",
 ]

 [[package]]
@@ -4764,13 +4890,18 @@ version = "0.6.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d4e6559d53cc268e5031cd8429d05415bc4cb4aefc4aa5d6cc35fbf5b924a1f8"
 dependencies = [
+ "async-compression",
 "bitflags 2.11.1",
 "bytes",
+ "futures-core",
 "futures-util",
 "http",
 "http-body",
+ "http-body-util",
 "iri-string",
 "pin-project-lite",
+ "tokio",
+ "tokio-util",
 "tower 0.5.3",
 "tower-layer",
 "tower-service",
@@ -4838,10 +4969,14 @@ version = "0.3.23"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "cb7f578e5945fb242538965c2d0b04418d38ec25c79d160cd279bf0731c8d319"
 dependencies = [
+ "matchers",
 "nu-ansi-term",
+ "once_cell",
+ "regex-automata",
 "sharded-slab",
 "smallvec",
 "thread_local",
+ "tracing",
 "tracing-core",
 "tracing-log",
 ]
@@ -1,6 +1,6 @@
 [package]
 name = "momentry_core"
-version = "0.1.0"
+version = "1.0.0"
 edition = "2021"
 authors = ["Momentry Team"]
 description = "Digital asset management system with video analysis and RAG"
@@ -11,7 +11,7 @@ anyhow = "1.0"
 thiserror = "1.0"
 tokio = { version = "1", features = ["full"] }
 tracing = "0.1"
-tracing-subscriber = "0.3"
+tracing-subscriber = { version = "0.3", features = ["env-filter"] }
 once_cell = "1.19"
 libc = "0.2"
 dotenv = "0.15"
@@ -26,6 +26,7 @@ futures-util = "0.3"
 # Serialization
 serde = { version = "1.0", features = ["derive"] }
 serde_json = "1.0"
+regex = "1"
 chrono = { version = "0.4", features = ["serde"] }

 # UUID
@@ -38,6 +39,8 @@ mac_address = "1.1"
 subtle = "2.5"
 aes-gcm = "0.10"
 base64 = "0.22"
+ argon2 = "0.5"
+ jsonwebtoken = "9.3"

 # Text processing
 jieba-rs = "0.8.1"
@@ -52,13 +55,13 @@ sqlx = { version = "0.8", features = ["runtime-tokio", "postgres", "sqlite", "js
 mongodb = { version = "2", features = ["tokio-runtime"] }
 bson = { version = "2", features = ["chrono-0_4"] }
 qdrant-client = "1.7"
-reqwest = { version = "0.12", features = ["json"] }
+reqwest = { version = "0.12", features = ["json", "gzip"] }
 pgvector = { version = "0.3", features = ["sqlx"] }

 # HTTP Server
 axum = { version = "0.7", features = ["multipart"] }
 tower = "0.4"
-tower-http = { version = "0.5", features = ["cors"] }
+tower-http = { version = "0.5", features = ["cors", "fs"] }

 # API Documentation
 utoipa = { version = "4", features = ["axum_extras", "chrono", "uuid"] }
@@ -79,6 +82,7 @@ crossterm = "0.28"

 # Terminal
 atty = "0.2"
+tokio-util = { version = "0.7.18", features = ["io"] }

 # System

@@ -98,6 +102,10 @@ optional = true
 name = "momentry"
 path = "src/main.rs"

+[[bin]]
+name = "momentry-cli"
+path = "src/bin/cli.rs"
+
 [[bin]]
 name = "momentry_player"
 path = "src/player/main.rs"
@@ -122,6 +130,14 @@ path = "src/bin/test_bm25_simple.rs"
 name = "integrated_player"
 path = "src/bin/integrated_player.rs"

+[[bin]]
+name = "release"
+path = "src/bin/release.rs"
+
+[[bin]]
+name = "service"
+path = "src/bin/service.rs"
+
 [build-dependencies]
 chrono = "0.4"

@@ -0,0 +1,277 @@
+# Identity Best-Face API
+
+**狀態：** 規劃中
+**提出日期：** 2026-06-01
+**提出者：** WordPress Portal 前端團隊
+
+---
+
+## 1. 背景
+
+WordPress Portal 的 People 頁面需要在 identity detail view 與 grid card 中顯示代表臉部縮圖。目前前端作法：
+
+1. `GET /identity/{uuid}/traces` → 取得所有 trace 列表（含 `avg_confidence`）
+2. 對每個 trace 載入第一幀 thumbnail → `GET /file/{uuid}/trace/{tid}/thumbnail`
+3. 從有 thumbnail 的 trace 中，選 `avg_confidence` 最高者作為代表圖
+
+### 現有問題
+
+- **品質不佳**：trace thumbnail 固定取第一幀，不一定是該 trace 內最清晰或正面的臉部畫面
+- **浪費頻寬**：前端需發送大量並行請求（最多 20 trace × thumbnail），多數 thumbnail 最終不會被使用
+- **無快取**：每次進入 detail view 都要重複載入所有 thumbnail
+- **不一致**：同樣 identity 在 grid card 與 detail view 可能顯示不同代表圖
+
+---
+
+## 2. 目標
+
+後端新增一個 endpoint，對指定 identity **跨所有 trace** 選出品質最佳（最清晰）的臉部畫面，並提供可直接使用的縮圖 URL，支援 disk cache。
+
+---
+
+## 3. API 規格
+
+### `GET /api/v1/identity/:identity_uuid/best-face`
+
+無 query parameter。
+
+#### 成功回應 `200`
+
+```json
+{
+  "success": true,
+  "identity_uuid": "a6fb22eebefaef17e62af874997c5944",
+  "name": "Audrey Hepburn",
+  "source": "fresh",
+  "best": {
+    "file_uuid": "a6fb22eebefaef17e62af874997c5944",
+    "trace_id": 42,
+    "frame_number": 3120,
+    "timestamp_secs": 124.8,
+    "bbox": {
+      "x": 240,
+      "y": 180,
+      "width": 120,
+      "height": 160
+    },
+    "confidence": 0.97,
+    "quality_score": 18624.0,
+    "blur_score": 2.1,
+    "thumbnail_url": "/api/v1/file/a6fb22eebefaef17e62af874997c5944/trace/42/thumbnail"
+  }
+}
+```
+
+#### 無可用臉部 `200`
+
+```json
+{
+  "success": true,
+  "identity_uuid": "a6fb22eebefaef17e62af874997c5944",
+  "name": "Audrey Hepburn",
+  "source": "fresh",
+  "best": null
+}
+```
+
+#### 欄位說明
+
+| 欄位 | 型態 | 說明 |
+|------|------|------|
+| `success` | boolean | 請求是否成功 |
+| `identity_uuid` | string | identity UUID（32字元無連字號） |
+| `name` | string | identity 名稱 |
+| `source` | string | `"fresh"`（即時計算）或 `"cache"`（來自 disk cache） |
+| `best` | object/null | 最佳臉部資訊，無可用臉部時為 `null` |
+| `best.file_uuid` | string | 該臉部所屬檔案 UUID |
+| `best.trace_id` | int | 該臉部所屬 trace ID |
+| `best.frame_number` | int | 代表臉的影格編號 |
+| `best.timestamp_secs` | float | 代表臉的時間戳（秒） |
+| `best.bbox` | object | 臉部 bounding box `{x, y, width, height}` |
+| `best.confidence` | float | 該臉部的 detection confidence |
+| `best.quality_score` | float | 品質分數 = `(width * height) * confidence` |
+| `best.blur_score` | float | 模糊度分數（ffmpeg blurdetect），越低越清晰 |
+| `best.thumbnail_url` | string | 縮圖 URL（相對路徑，可直接用於瀏覽器） |
+
+---
+
+## 4. 實作建議
+
+### 4.1 建議放置位置
+
+**選項 A（建議）：** `src/api/trace_agent_api.rs`
+
+- 原因：核心邏輯重用 `select_rep_face()`（目前為 `pub(crate)`，位於同一檔案），無需修改既有的 function visibility
+- 在 `trace_agent_routes()` 中新增路由
+
+**選項 B：** `src/api/identity_binding.rs`
+
+- 需將 `select_rep_face` 改為 `pub` 才能跨檔案呼叫
+- 路由語意上更接近 identity 操作
+
+### 4.2 演算法
+
+```
+1. DISK CACHE CHECK
+   路徑：{OUTPUT_DIR}/identities/{uuid}/best_face.json
+   讀取 identity.json 的 updated_at，與 cache 中記錄的版本比較
+   若 cache 未過期 → 直接回傳（source: "cache"）
+   若無 cache 或已過期 → 繼續計算
+
+2. QUERY IDENTITY
+   SELECT id, name FROM identities
+   WHERE REPLACE(uuid::text, '-', '') = $1
+
+3. QUERY TOP N TRACES
+   SELECT fd.file_uuid, fd.trace_id,
+          AVG(fd.confidence)::float8 AS avg_conf
+   FROM {schema}.face_detections fd
+   WHERE fd.identity_id = $1
+     AND fd.confidence > 0.7
+     AND (fd.metadata->>'qc_ok' IS NULL
+          OR (fd.metadata->>'qc_ok')::boolean = true)
+   GROUP BY fd.file_uuid, fd.trace_id
+   ORDER BY avg_conf DESC
+   LIMIT 5
+
+4. FOR EACH TRACE (並行)
+   select_rep_face(pool, file_uuid, trace_id, err_fn)
+   　→ 回傳該 trace 內 blur_score 最低（最清晰）的臉
+   失敗則 skip（log warning）
+
+5. SELECT BEST AMONG RESULTS
+   主排序：blur_score ASC（越低越清晰）
+   次排序：quality_score DESC（blur_score 差距 < 0.5 時）
+   全部失敗 → best = null
+
+6. WRITE DISK CACHE
+   路徑：{OUTPUT_DIR}/identities/{uuid}/best_face.json
+   內容：best 欄位 + 計算時間 + identity updated_at
+
+7. RESPONSE
+```
+
+### 4.3 效能參數
+
+| 參數 | 值 | 說明 |
+|------|----|------|
+| TOP N | 5 | 只對 confidence 最高的 5 個 trace 做 blurdetect |
+| confidence 門檻 | > 0.7 | 同既有的 `select_rep_face` 邏輯 |
+| QC 過濾 | qc_ok = true/null | 同既有邏輯 |
+| ffmpeg timeout | inherit from Command | 每個 trace 約 1-3s |
+| cache TTL | 直到下一次 bind/unbind/merge | 事件驅動失效 |
+
+### 4.4 快取策略
+
+**寫入時機：** `get_identity_best_face` 計算完成後
+
+**失效時機（刪除 `best_face.json`）：**
+
+| 觸發 operation | 所在檔案 | 備註 |
+|---------------|---------|------|
+| `bind_trace` (POST) | `identity_binding.rs` | 新增 face 關聯 |
+| `unbind` (POST) | `identity_binding.rs` | 移除 face 關聯 |
+| `mergeinto` (POST) | `identity_binding.rs` | source + target 雙雙清除 |
+| `profile-image` (POST) | `identity_api.rs` | 使用者上傳新大頭照 |
+
+**Cache 驗證機制：** 儲存計算時的 `identity.updated_at`，每次請求時比對：
+- 若 identity 的 `updated_at` 未變 → cache 有效
+- 若已變 → 重新計算
+
+### 4.5 建議的新增/修改檔案
+
+| 檔案 | 動作 | 說明 |
+|------|------|------|
+| `src/api/trace_agent_api.rs` | **新增** handler + struct + route | ~+130 行 |
+| `src/api/identity_binding.rs` | **修改** 3 處 + cache invalidation helper | ~+25 行 |
+| `src/api/identity_api.rs` | **修改** 1 處（profile-image POST） | ~+5 行 |
+
+### 4.6 需要的新 struct
+
+**`src/api/trace_agent_api.rs`**（或獨立檔案 `src/core/identity_best_face.rs`）：
+
+```rust
+#[derive(Debug, Serialize, Deserialize)]
+pub struct BestFaceResponse {
+    pub success: bool,
+    pub identity_uuid: String,
+    pub name: String,
+    pub source: String,
+    pub best: Option<BestFaceResult>,
+}
+
+#[derive(Debug, Serialize, Deserialize)]
+pub struct BestFaceResult {
+    pub file_uuid: String,
+    pub trace_id: i32,
+    pub frame_number: i64,
+    pub timestamp_secs: f64,
+    pub bbox: RepFaceBbox,
+    pub confidence: f64,
+    pub quality_score: f64,
+    pub blur_score: f64,
+    pub thumbnail_url: String,
+}
+```
+
+### 4.7 Cache Invalidation Helper Function
+
+```rust
+async fn invalidate_best_face_cache(output_dir: &str, uuid_clean: &str) {
+    let path = format!("{}/identities/{}/best_face.json", output_dir, uuid_clean);
+    let _ = tokio::fs::remove_file(path).await;
+}
+```
+
+---
+
+## 5. 前端整合參考（供後端團隊理解使用情境）
+
+WP snippet 72 (`ms-people.js`) 的 `loadPersonDetail` 中，優先使用新 endpoint：
+
+```js
+async function loadPersonDetail(person) {
+  if (person.thumb && person._hasProfileImage) return;
+
+  try {
+    const res = await apiFetch('/identity/' + person.id + '/best-face');
+    if (res?.success && res?.best) {
+      const b = res.best;
+      person.thumb = `${API_BASE}/file/${b.file_uuid}/trace/${b.trace_id}/thumbnail?api_key=${API_KEY}`;
+      person._hasProfileImage = true;
+      updateDetailAvatar(person);
+      return;
+    }
+  } catch (e) { /* fallback to legacy */ }
+
+  // 原邏輯：traces → thumbnails → confidence sort
+}
+```
+
+同樣可用於 grid card 的代表圖載入（`loadGridThumbnails`）：
+
+```js
+// 一次性載入所有 pending identity 的 best-face
+const results = await Promise.allSettled(
+  persons.map(p => apiFetch('/identity/' + p.id + '/best-face'))
+);
+```
+
+---
+
+## 6. 驗收標準
+
+1. `GET /api/v1/identity/{uuid}/best-face` → `200` + valid JSON
+2. 有 trace 的 identity → `best` 不為 null，且 `blur_score` 為該 identity 所有 trace 中最低
+3. 無 trace 的 identity → `best: null`
+4. 短時間內重複請求同一 identity → `source: "cache"`，回應時間 < 10ms
+5. 綁定新 trace 後再次請求 → `source: "fresh"`（cache 已正確失效）
+6. `thumbnail_url` 可直接用於 `<img>` 顯示
+
+---
+
+## 7. 風險與注意事項
+
+- **首次請求延遲**：對有大量 trace 的 identity（如主角），首次請求可能需 5-15 秒。建議前端顯示 loading state
+- **ffmpeg 資源**：同時多個請求可能導致高 CPU 使用。可考慮加入 per-identity lock 避免重複計算
+- **邊界案例**：trace 內的 faces 全部 confidence ≤ 0.7 或 qc_ok=false，則該 trace 被跳過，可能導致 `best: null`
@@ -0,0 +1,78 @@
+# Sync Notes 2026-05-21
+
+## M5Max128 收到後需要做的事
+
+```bash
+cd ~/momentry_core
+git pull origin main       # 拉取所有變更
+cat SYNC_V1.1.md           # 閱讀此文件
+
+# 資料庫變更（必須先執行，否則 worker 會 fail）
+psql -U accusys -d momentry -c "ALTER TABLE public.pre_chunks ALTER COLUMN coordinate_index SET DEFAULT 0;"
+
+# 重建 + 重啟
+cargo build --release --bin momentry
+./run-server-3002.sh
+```
+
+---
+
+## Bugs Fixed (13)
+
+| # | 問題 | 根因 | 修復 |
+|---|------|------|------|
+| 1 | `GET /identity/:uuid/files` 空資料 | SQL 缺 `REPLACE(uuid)` + 缺 `JOIN videos` | 改用 `REPLACE(uuid::text...)` + JOIN videos + `frame_number/fps` |
+| 2 | `GET /identity/:uuid/faces` crash + 空 | `i64`/`INT4` 型別不符 + 硬編碼 NULL/0 | `id::bigint`、`confidence::float8` + 真實欄位 |
+| 3 | `GET /identity/:uuid` crash | `IdentityDetailRecord.id` 是 `i64` 但 DB 是 `INT4` | `id::bigint as id` |
+| 4 | `GET /file/:uuid/identities` 空 | 雙重 stub（handler + DB 都 `Vec::new()`） | 完整實作 + 正確 total count |
+| 5 | `GET /identities/search?q=Louis` 500 | `c.text_content` NULL 但 Rust tuple 用 `String` | 改 `Option<String>` |
+| 6 | `POST /search/universal` person type first/last_time null | `search_persons_internal` 用 `timestamp_secs` | 改 `frame_number/fps` + JOIN videos |
+| 7 | faces/files/chunks total 不正確 | `total: data.len()` | 獨立 COUNT 查詢 |
+| 8 | `GET /identity/:uuid/traces` 無分頁 | 缺 page/page_size | 新增 `TracesQuery` + LIMIT/OFFSET |
+| 9 | 身分比對 frame-level 不穩定 | frame-level Qdrant | 改 **trace-level**（AVG embedding per trace） |
+| 10 | Charade face embedding 不在 Qdrant | 沒跑 `sync_face_embeddings` | match API 自動 push + ANN search |
+| 11 | 無眼睛 face 推入 Qdrant | 無 QC 過濾 | `face_landmark_qc.py --apply` + Qdrant sync 過濾 `qc_ok` |
+| 12 | TMDb 比對 dev/prod 不一致 | Qdrant ANN 不同 collection | trace-level 改善穩定性 |
+| 13 | `faces/files/chunks total` 顯示 page_size | `total: data.len()` | 改為獨立 COUNT 查詢 |
+
+## ✨ 新功能 (6)
+
+| # | 功能 | 說明 |
+|---|------|------|
+| 1 | `POST /api/v1/tmdb/fetch` | 從 TMDb 下載 cast → 建立 identity + json + jpg + Qdrant |
+| 2 | `POST /api/v1/agents/tmdb/match/:file_uuid` | 推 face → Qdrant ANN search → bind identity |
+| 3 | `GET /api/v1/identity/:uuid/status` | 檢查 identity.json + profile.jpg 是否存在 |
+| 4 | `/health` 新增 watcher/worker/時區 | `watcher_running`、`worker_running`、`system_timezone` |
+| 5 | `SYSTEM_TIMEZONE` config | 自動偵測系統時區，可 `MOMENTRY_TIMEZONE` 覆蓋 |
+| 6 | `GET /identity/:uuid/traces` 分頁 | `?page=1&page_size=20` |
+
+## 🔧 資料庫變更
+
+```sql
+-- 必須執行（否則 worker 的 CUT processor 會失敗）
+ALTER TABLE public.pre_chunks ALTER COLUMN coordinate_index SET DEFAULT 0;
+
+-- 選擇性（face_landmark_qc.py --apply 需要）
+ALTER TABLE public.face_detections ADD COLUMN metadata jsonb DEFAULT '{}'::jsonb;
+```
+
+## 🗑️ 清理
+
+- 刪除 2,769 個孤兒 `person_xxx` identity（無 face_detections）
+- `person_identities` + `person_appearances` table 已 DROP
+
+## 📂 主要檔案變更
+
+| 檔案 | 說明 |
+|------|------|
+| `src/api/identity_api.rs` | identity detail/files/faces total 修正 + status endpoint |
+| `src/api/identity_binding.rs` | traces 分頁（新增 `page`/`page_size`/`total`） |
+| `src/api/server.rs` | health 新增 watcher/worker/system_timezone |
+| `src/api/tmdb_api.rs` | **新檔案** — tmdb/fetch + match 端點 |
+| `src/api/universal_search.rs` | person search 改 frame_number/fps |
+| `src/core/config.rs` | 新增 SYSTEM_TIMEZONE |
+| `src/core/db/qdrant_db.rs` | search_face_collection + sync_trace_embeddings + batch upsert |
+| `src/core/db/postgres_db.rs` | get_identity_files/faces 修正 + get_file_identities 實作 |
+| `src/core/tmdb/probe.rs` | extract_movie_name 改進（只取 `(` 前） |
+| `scripts/face_landmark_qc.py` | 新增 `--apply` + `--schema` 參數 |
+| `Cargo.toml` | reqwest 加 `gzip` feature |
@@ -1 +0,0 @@
-/Users/accusys/momentry_core_0.1/output/a1b10138a6bbb0cd.cut.json
@@ -1 +0,0 @@
-/Users/accusys/momentry_core_0.1/output/a1b10138a6bbb0cd.face.json
@@ -1 +0,0 @@
-/Users/accusys/momentry_core_0.1/output/a1b10138a6bbb0cd.ocr.json
@@ -1 +0,0 @@
-/Users/accusys/momentry_core_0.1/output/a1b10138a6bbb0cd.pose.json
@@ -1 +0,0 @@
-/Users/accusys/momentry_core_0.1/output/a1b10138a6bbb0cd.story.json
@@ -1 +0,0 @@
-/Users/accusys/momentry_core_0.1/output/a1b10138a6bbb0cd.yolo.json
@@ -1,161 +0,0 @@
-#!/usr/bin/env python3
-"""Benchmark ASR processor direct vs chunked transcription overhead."""
-
-import sys
-import os
-import subprocess
-import json
-import tempfile
-import time
-import shutil
-import statistics
-
-# Use a small video clip for consistent benchmarking
-VIDEO_SOURCE = "../test_video/BigBuckBunny_320x180.mp4"  # 10 minutes, 62MB
-if not os.path.exists(VIDEO_SOURCE):
-    print(f"Video not found: {VIDEO_SOURCE}")
-    sys.exit(1)
-
-# Create temporary directory for all test runs
-temp_dir = tempfile.mkdtemp(prefix="asr_bench_")
-print(f"Benchmark directory: {temp_dir}")
-
-
-def run_asr_mode(mode_name, max_direct_duration, chunk_duration=600):
-    """Run ASR processor with given parameters, return timing and resource stats."""
-    clip_path = os.path.join(temp_dir, f"clip_{mode_name}.mp4")
-    output_path = os.path.join(temp_dir, f"output_{mode_name}.json")
-
-    # Copy source video to clip path (no transcoding)
-    shutil.copy2(VIDEO_SOURCE, clip_path)
-
-    env = os.environ.copy()
-    env["MOMENTRY_ASR_MAX_DIRECT_DURATION"] = str(max_direct_duration)
-    env["MOMENTRY_ASR_CHUNK_DURATION"] = str(chunk_duration)
-    env["MOMENTRY_ASR_MODEL_SIZE"] = "tiny"
-    env["MOMENTRY_ASR_COMPUTE_TYPE"] = "int8"
-
-    cmd = [
-        "/opt/homebrew/bin/python3.11",
-        "scripts/asr_processor.py",
-        clip_path,
-        output_path,
-        "--uuid",
-        f"bench_{mode_name}",
-    ]
-
-    # Start monitoring (external)
-    import psutil
-
-    start_time = time.time()
-    proc = subprocess.Popen(
-        cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env
-    )
-
-    # Monitor CPU and memory of child process
-    cpu_percents = []
-    memory_mbs = []
-
-    while True:
-        try:
-            p = psutil.Process(proc.pid)
-            cpu = p.cpu_percent(interval=0.1)
-            mem = p.memory_info().rss / (1024 * 1024)
-            cpu_percents.append(cpu)
-            memory_mbs.append(mem)
-        except (psutil.NoSuchProcess, psutil.AccessDenied):
-            break
-        if proc.poll() is not None:
-            # Process ended, wait a bit for final stats
-            time.sleep(0.1)
-            break
-
-    stdout, stderr = proc.communicate(timeout=1)
-    elapsed = time.time() - start_time
-    returncode = proc.returncode
-
-    # Read output
-    segments = []
-    if os.path.exists(output_path):
-        with open(output_path, "r") as f:
-            data = json.load(f)
-            segments = data.get("segments", [])
-
-    # Clean up temporary files
-    try:
-        os.unlink(clip_path)
-        os.unlink(output_path)
-    except:
-        pass
-
-    return {
-        "mode": mode_name,
-        "elapsed": elapsed,
-        "returncode": returncode,
-        "segments": len(segments),
-        "cpu_avg": statistics.mean(cpu_percents) if cpu_percents else 0,
-        "cpu_max": max(cpu_percents) if cpu_percents else 0,
-        "memory_avg": statistics.mean(memory_mbs) if memory_mbs else 0,
-        "memory_max": max(memory_mbs) if memory_mbs else 0,
-        "stderr": stderr.decode() if stderr else "",
-    }
-
-
-try:
-    # Run direct transcription (clip duration ~600s, max_direct=1800)
-    print("Running direct transcription benchmark...")
-    direct = run_asr_mode("direct", max_direct_duration=1800, chunk_duration=600)
-
-    # Run chunked transcription (force chunked with max_direct=300, chunk=120)
-    print("Running chunked transcription benchmark...")
-    chunked = run_asr_mode("chunked", max_direct_duration=300, chunk_duration=120)
-
-    # Calculate overhead
-    overhead = (chunked["elapsed"] - direct["elapsed"]) / direct["elapsed"] * 100
-
-    # Print results
-    print("\n" + "=" * 60)
-    print("ASR PROCESSOR BENCHMARK RESULTS")
-    print("=" * 60)
-    print(f"Test video: {VIDEO_SOURCE}")
-    print(f"Video duration: ~10 minutes (600 seconds)")
-    print()
-    print("Direct Transcription:")
-    print(f"  Time: {direct['elapsed']:.1f}s")
-    print(f"  Segments: {direct['segments']}")
-    print(f"  CPU avg/max: {direct['cpu_avg']:.1f}% / {direct['cpu_max']:.1f}%")
-    print(
-        f"  Memory avg/max: {direct['memory_avg']:.1f} MB / {direct['memory_max']:.1f} MB"
-    )
-    print()
-    print("Chunked Transcription:")
-    print(f"  Time: {chunked['elapsed']:.1f}s")
-    print(f"  Segments: {chunked['segments']}")
-    print(f"  CPU avg/max: {chunked['cpu_avg']:.1f}% / {chunked['cpu_max']:.1f}%")
-    print(
-        f"  Memory avg/max: {chunked['memory_avg']:.1f} MB / {chunked['memory_max']:.1f} MB"
-    )
-    print()
-    print("OVERHEAD ANALYSIS:")
-    print(f"  Time overhead: {overhead:.2f}%")
-    if overhead <= 5:
-        print(f"  ✅ PASS: Overhead ≤5% requirement")
-    else:
-        print(f"  ❌ FAIL: Overhead exceeds 5% limit")
-    print()
-
-    # Check for errors
-    if direct["returncode"] != 0:
-        print(f"WARNING: Direct transcription returned {direct['returncode']}")
-    if chunked["returncode"] != 0:
-        print(f"WARNING: Chunked transcription returned {chunked['returncode']}")
-
-except Exception as e:
-    print(f"Benchmark failed: {e}")
-    import traceback
-
-    traceback.print_exc()
-finally:
-    # Clean up directory
-    shutil.rmtree(temp_dir, ignore_errors=True)
-    print(f"Cleaned up {temp_dir}")
@@ -1,151 +0,0 @@
-#!/usr/bin/env python3
-"""Benchmark ASR with realistic chunk sizes."""
-
-import sys
-import os
-import subprocess
-import json
-import tempfile
-import time
-import shutil
-import statistics
-
-VIDEO_SOURCE = "../test_video/BigBuckBunny_320x180.mp4"  # 10 minutes, 62MB
-if not os.path.exists(VIDEO_SOURCE):
-    print(f"Video not found: {VIDEO_SOURCE}")
-    sys.exit(1)
-
-
-def run_asr_mode(mode_name, max_direct_duration, chunk_duration, description):
-    """Run ASR processor with given parameters, return timing."""
-    clip_path = os.path.join(temp_dir, f"clip_{mode_name}.mp4")
-    output_path = os.path.join(temp_dir, f"output_{mode_name}.json")
-
-    # Copy source video to clip path
-    shutil.copy2(VIDEO_SOURCE, clip_path)
-
-    env = os.environ.copy()
-    env["MOMENTRY_ASR_MAX_DIRECT_DURATION"] = str(max_direct_duration)
-    env["MOMENTRY_ASR_CHUNK_DURATION"] = str(chunk_duration)
-    env["MOMENTRY_ASR_MODEL_SIZE"] = "tiny"
-    env["MOMENTRY_ASR_COMPUTE_TYPE"] = "int8"
-
-    cmd = [
-        "/opt/homebrew/bin/python3.11",
-        "scripts/asr_processor.py",
-        clip_path,
-        output_path,
-        "--uuid",
-        f"bench_{mode_name}",
-    ]
-
-    start_time = time.time()
-    proc = subprocess.run(cmd, capture_output=True, env=env, text=True)
-    elapsed = time.time() - start_time
-    returncode = proc.returncode
-
-    # Read output
-    segments = []
-    language = ""
-    if os.path.exists(output_path):
-        with open(output_path, "r") as f:
-            data = json.load(f)
-            segments = data.get("segments", [])
-            language = data.get("language", "")
-
-    # Clean up
-    try:
-        os.unlink(clip_path)
-        os.unlink(output_path)
-    except:
-        pass
-
-    return {
-        "mode": mode_name,
-        "description": description,
-        "elapsed": elapsed,
-        "returncode": returncode,
-        "segments": len(segments),
-        "language": language,
-        "stderr": proc.stderr[:200] if proc.stderr else "",
-    }
-
-
-# Create temporary directory
-temp_dir = tempfile.mkdtemp(prefix="asr_bench_real_")
-print(f"Benchmark directory: {temp_dir}")
-
-try:
-    # Test 1: Direct transcription (video is 10 min, max_direct=30 min)
-    print("\n1. Direct transcription (max_direct=1800s, chunk=600s):")
-    direct = run_asr_mode(
-        "direct",
-        max_direct_duration=1800,
-        chunk_duration=600,
-        description="Direct (video < 30min threshold)",
-    )
-    print(f"   Time: {direct['elapsed']:.1f}s, Segments: {direct['segments']}")
-
-    # Test 2: Chunked with 1 chunk (force chunked but chunk size = video duration)
-    print("\n2. Chunked with 1 chunk (max_direct=300s, chunk=600s):")
-    chunked1 = run_asr_mode(
-        "chunked1",
-        max_direct_duration=300,
-        chunk_duration=600,
-        description="Chunked with 1 chunk (10 min)",
-    )
-    print(f"   Time: {chunked1['elapsed']:.1f}s, Segments: {chunked1['segments']}")
-
-    # Test 3: Chunked with 2 chunks (5 min each)
-    print("\n3. Chunked with 2 chunks (max_direct=300s, chunk=300s):")
-    chunked2 = run_asr_mode(
-        "chunked2",
-        max_direct_duration=300,
-        chunk_duration=300,
-        description="Chunked with 2 chunks (5 min each)",
-    )
-    print(f"   Time: {chunked2['elapsed']:.1f}s, Segments: {chunked2['segments']}")
-
-    # Test 4: Chunked with 5 chunks (2 min each) - worst case
-    print("\n4. Chunked with 5 chunks (max_direct=300s, chunk=120s):")
-    chunked5 = run_asr_mode(
-        "chunked5",
-        max_direct_duration=300,
-        chunk_duration=120,
-        description="Chunked with 5 chunks (2 min each)",
-    )
-    print(f"   Time: {chunked5['elapsed']:.1f}s, Segments: {chunked5['segments']}")
-
-    # Calculate overheads
-    print("\n" + "=" * 60)
-    print("OVERHEAD ANALYSIS (compared to direct transcription)")
-    print("=" * 60)
-
-    for test in [chunked1, chunked2, chunked5]:
-        if direct["elapsed"] > 0:
-            overhead = (test["elapsed"] - direct["elapsed"]) / direct["elapsed"] * 100
-            status = "✅ ≤5%" if overhead <= 5 else "❌ >5%"
-            print(f"\n{test['description']}:")
-            print(f"  Time: {test['elapsed']:.1f}s (direct: {direct['elapsed']:.1f}s)")
-            print(f"  Overhead: {overhead:.2f}% {status}")
-            print(f"  Segments: {test['segments']} (direct: {direct['segments']})")
-            if test["segments"] != direct["segments"]:
-                print(f"  ⚠️ Segment count mismatch!")
-
-    # Summary
-    print("\n" + "=" * 60)
-    print("SUMMARY")
-    print("=" * 60)
-    print(f"Video: {os.path.basename(VIDEO_SOURCE)} (~10 minutes)")
-    print(f"\nKey finding: Overhead depends heavily on chunk count.")
-    print(f"With realistic chunk sizes (10 min), overhead should be minimal.")
-
-except Exception as e:
-    print(f"Benchmark failed: {e}")
-    import traceback
-
-    traceback.print_exc()
-finally:
-    # Clean up directory
-    shutil.rmtree(temp_dir, ignore_errors=True)
-    print(f"\nCleaned up {temp_dir}")
@@ -1,19 +1,81 @@
-use chrono::Local;
-use std::env;
+use std::collections::BTreeMap;
+use std::path::Path;

 fn main() {
-    let now = Local::now();
-    let build_time = now.format("%Y-%m-%d %H:%M:%S").to_string();
+    let version = std::env::var("CARGO_PKG_VERSION").unwrap_or_else(|_| "unknown".to_string());

-    // Get version from Cargo.toml
-    let version = env!("CARGO_PKG_VERSION");
-    let full_version = format!("{} (build: {})", version, build_time);
+    let git_hash = std::process::Command::new("git")
+        .args(["rev-parse", "--short", "HEAD"])
+        .output()
+        .ok()
+        .and_then(|o| String::from_utf8(o.stdout).ok())
+        .map(|s| s.trim().to_string())
+        .unwrap_or_else(|| "unknown".to_string());

-    // Set build-time environment variables
-    println!("cargo:rustc-env=BUILD_VERSION={}", full_version);
-    println!("cargo:rustc-env=BUILD_TIME={}", build_time);
-    println!("cargo:rustc-env=VERSION={}", version);
+    let timestamp = std::process::Command::new("date")
+        .args(["-u", "+%Y-%m-%dT%H:%M:%SZ"])
+        .output()
+        .ok()
+        .and_then(|o| String::from_utf8(o.stdout).ok())
+        .map(|s| s.trim().to_string())
+        .unwrap_or_else(|| "unknown".to_string());

-    // Also print for debugging
-    println!("cargo:warning=Building version: {}", full_version);
+    println!("cargo:rustc-env=BUILD_VERSION={}", version);
+    println!("cargo:rustc-env=BUILD_GIT_HASH={}", git_hash);
+    println!("cargo:rustc-env=BUILD_TIMESTAMP={}", timestamp);
+
+    // ── Schema migration manifest ──
+    // Scan release/migrate_*.sql, compute SHA256, embed as JSON string
+    let manifest_dir = std::env::var("CARGO_MANIFEST_DIR").unwrap_or_else(|_| ".".to_string());
+    let release_dir = Path::new(&manifest_dir).join("release");
+
+    let mut migrations = BTreeMap::new(); // sorted by filename
+    if let Ok(entries) = std::fs::read_dir(&release_dir) {
+        for entry in entries.flatten() {
+            let path = entry.path();
+            let fname = path.file_name().and_then(|n| n.to_str()).unwrap_or("");
+            if fname.starts_with("migrate_") && fname.ends_with(".sql") {
+                if let Ok(content) = std::fs::read(&path) {
+                    let hash = sha256_hex(&content);
+                    migrations.insert(fname.to_string(), hash);
+                }
+            }
+        }
+    }
+
+    // Encode as comma-separated: name1:hash1,name2:hash2,...
+    let manifest: String = migrations
+        .iter()
+        .map(|(name, hash)| format!("{}:{}", name, hash))
+        .collect::<Vec<_>>()
+        .join(",");
+    println!("cargo:rustc-env=REQUIRED_MIGRATIONS={}", manifest);
+    println!(
+        "cargo:info=Embedded {} migration checksums",
+        migrations.len()
+    );
+}
+
+fn sha256_hex(data: &[u8]) -> String {
+    use std::io::Write;
+    use std::process::{Command, Stdio};
+    if let Ok(mut child) = Command::new("shasum")
+        .arg("-a")
+        .arg("256")
+        .stdin(Stdio::piped())
+        .stdout(Stdio::piped())
+        .spawn()
+    {
+        if let Some(mut stdin) = child.stdin.take() {
+            let _ = stdin.write_all(data);
+        }
+        if let Ok(out) = child.wait_with_output() {
+            if let Ok(s) = String::from_utf8(out.stdout) {
+                if let Some(hash) = s.split(' ').next() {
+                    return hash.to_string();
+                }
+            }
+        }
+    }
+    "unknown".to_string()
 }
@@ -1,7 +0,0 @@
-#!/opt/homebrew/bin/python3.11
-try:
-    import whisper
-
-    print("whisper available")
-except ImportError as e:
-    print(f"whisper not available: {e}")
@@ -1,200 +0,0 @@
-#!/usr/bin/env python3
-"""
-Chunked transcription to handle large audio files.
-"""
-
-import sys
-import time
-import tempfile
-import json
-import subprocess
-from pathlib import Path
-import numpy as np
-
-
-def split_audio(input_path, chunk_duration=1800, output_dir=None):
-    """Split audio into chunks using ffmpeg."""
-    if output_dir is None:
-        output_dir = Path(tempfile.mkdtemp(prefix="audio_chunks_"))
-    else:
-        output_dir = Path(output_dir)
-    output_dir.mkdir(exist_ok=True, parents=True)
-
-    # Get total duration
-    cmd = [
-        "ffprobe",
-        "-v",
-        "error",
-        "-show_entries",
-        "format=duration",
-        "-of",
-        "csv=p=0",
-        str(input_path),
-    ]
-    result = subprocess.run(cmd, capture_output=True, text=True)
-    total_duration = float(result.stdout.strip())
-
-    print(
-        f"Total audio duration: {total_duration:.1f}s ({total_duration / 3600:.1f} hrs)"
-    )
-    print(f"Splitting into {chunk_duration}s chunks...")
-
-    chunks = []
-    start = 0
-    chunk_idx = 0
-    while start < total_duration:
-        chunk_path = output_dir / f"chunk_{chunk_idx:04d}.wav"
-        cmd = [
-            "ffmpeg",
-            "-i",
-            str(input_path),
-            "-ss",
-            str(start),
-            "-t",
-            str(chunk_duration),
-            "-acodec",
-            "pcm_s16le",
-            "-ar",
-            "16000",
-            "-ac",
-            "1",
-            "-y",
-            str(chunk_path),
-        ]
-        subprocess.run(cmd, capture_output=True)
-        if chunk_path.exists() and chunk_path.stat().st_size > 0:
-            chunks.append(
-                {
-                    "path": chunk_path,
-                    "start_time": start,
-                    "end_time": min(start + chunk_duration, total_duration),
-                }
-            )
-        else:
-            print(f"Warning: Chunk {chunk_idx} may be empty")
-        start += chunk_duration
-        chunk_idx += 1
-
-    print(f"Created {len(chunks)} chunks in {output_dir}")
-    return chunks, output_dir
-
-
-def transcribe_chunk(chunk_info, model, chunk_idx, total_chunks):
-    """Transcribe a single chunk."""
-    print(
-        f"[{chunk_idx + 1}/{total_chunks}] Transcribing chunk {chunk_info['start_time']:.1f}-{chunk_info['end_time']:.1f}"
-    )
-    start_time = time.time()
-
-    segments, info = model.transcribe(str(chunk_info["path"]), beam_size=5)
-    results = []
-    for segment in segments:
-        # Adjust timestamps by chunk start time
-        results.append(
-            {
-                "start": segment.start + chunk_info["start_time"],
-                "end": segment.end + chunk_info["start_time"],
-                "text": segment.text.strip(),
-            }
-        )
-
-    elapsed = time.time() - start_time
-    print(f"  → {len(results)} segments in {elapsed:.1f}s")
-    return results, info
-
-
-def main():
-    import argparse
-
-    parser = argparse.ArgumentParser(description="Chunked transcription")
-    parser.add_argument("audio_path", help="Audio file path")
-    parser.add_argument(
-        "--chunk-duration",
-        type=int,
-        default=1800,
-        help="Chunk duration in seconds (default: 1800 = 30 min)",
-    )
-    parser.add_argument("--model-size", default="tiny", help="Whisper model size")
-    parser.add_argument("--compute-type", default="int8", help="Compute type")
-    parser.add_argument(
-        "--output", "-o", default="chunked_transcription.json", help="Output JSON path"
-    )
-    args = parser.parse_args()
-
-    audio_path = Path(args.audio_path)
-    if not audio_path.exists():
-        print(f"Error: File not found: {audio_path}")
-        sys.exit(1)
-
-    print(f"Chunked Transcription for {audio_path}")
-    print(f"Model: {args.model_size}, Compute: {args.compute_type}")
-    print(
-        f"Chunk duration: {args.chunk_duration}s ({args.chunk_duration / 60:.1f} min)"
-    )
-
-    # Split audio
-    chunks, temp_dir = split_audio(audio_path, chunk_duration=args.chunk_duration)
-    if not chunks:
-        print("No chunks created")
-        sys.exit(1)
-
-    # Load model once
-    print("Loading Whisper model...")
-    from faster_whisper import WhisperModel
-
-    model_start = time.time()
-    model = WhisperModel(args.model_size, device="cpu", compute_type=args.compute_type)
-    print(f"Model loaded in {time.time() - model_start:.1f}s")
-
-    # Process each chunk
-    all_segments = []
-    language = None
-    language_prob = None
-
-    for i, chunk in enumerate(chunks):
-        try:
-            segments, info = transcribe_chunk(chunk, model, i, len(chunks))
-            all_segments.extend(segments)
-            if language is None:
-                language = info.language
-                language_prob = info.language_probability
-        except Exception as e:
-            print(f"Error transcribing chunk {i}: {e}")
-            import traceback
-
-            traceback.print_exc()
-            # Continue with next chunk
-
-    # Sort segments by start time
-    all_segments.sort(key=lambda x: x["start"])
-
-    # Save results
-    output = {
-        "language": language or "unknown",
-        "language_probability": language_prob or 0.0,
-        "segments": all_segments,
-        "chunk_count": len(chunks),
-        "chunk_duration": args.chunk_duration,
-        "total_segments": len(all_segments),
-    }
-
-    output_path = Path(args.output)
-    output_path.parent.mkdir(exist_ok=True, parents=True)
-    with open(output_path, "w") as f:
-        json.dump(output, f, indent=2)
-
-    print(f"\nTranscription completed:")
-    print(f"  Total segments: {len(all_segments)}")
-    print(
-        f"  Language: {output['language']} (prob {output['language_probability']:.2f})"
-    )
-    print(f"  Results saved to: {output_path}")
-
-    # Cleanup temp directory
-    import shutil
-
-    shutil.rmtree(temp_dir, ignore_errors=True)
-
-
-if __name__ == "__main__":
-    main()
@@ -1,64 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-    <key>Label</key>
-    <string>com.momentry.api</string>
-    
-    <key>UserName</key>
-    <string>accusys</string>
-    
-    <key>GroupName</key>
-    <string>staff</string>
-    
-    <key>WorkingDirectory</key>
-    <string>/Users/accusys/momentry_core_0.1</string>
-    
-    <key>ProgramArguments</key>
-    <array>
-        <string>/Users/accusys/momentry_core_0.1/target/release/momentry</string>
-        <string>server</string>
-        <string>--port</string>
-        <string>3002</string>
-    </array>
-    
-    <key>EnvironmentVariables</key>
-    <dict>
-        <key>PATH</key>
-        <string>/opt/homebrew/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin</string>
-        
-        <key>DATABASE_URL</key>
-        <string>postgres://accusys@localhost:5432/momentry</string>
-        
-        <key>DB_MAX_CONNECTIONS</key>
-        <string>50</string>
-        
-        <key>DB_ACQUIRE_TIMEOUT</key>
-        <string>30</string>
-        
-        <key>REDIS_URL</key>
-        <string>redis://:accusys@localhost:6379</string>
-        
-        <key>REDIS_PASSWORD</key>
-        <string>accusys</string>
-        
-        <key>OLLAMA_HOST</key>
-        <string>http://localhost:11434</string>
-        
-        <key>QDRANT_URL</key>
-        <string>http://127.0.0.1:6333</string>
-    </dict>
-    
-    <key>RunAtLoad</key>
-    <true/>
-    
-    <key>KeepAlive</key>
-    <true/>
-    
-    <key>StandardOutPath</key>
-    <string>/Users/accusys/momentry/log/momentry_api.log</string>
-    
-    <key>StandardErrorPath</key>
-    <string>/Users/accusys/momentry/log/momentry_api.error.log</string>
-</dict>
-</plist>
@@ -0,0 +1,22 @@
+# Port Registry - Momentry Core
+# Each port must have exactly one owner.
+# Before adding a service: pick a free port, add a row here, then configure.
+#
+# Port	Service		Owner			Config Key			Default			Source
+22	ssh		sshd			-				-			macOS
+80	http		Caddy			-				-			Caddyfile
+443	https		Caddy			-				-			Caddyfile
+2019	caddy-admin	Caddy			-				-			Caddyfile (internal)
+3000	gitea		gitea			-				3000			start_momentry.sh
+3002	production	momentry		MOMENTRY_SERVER_PORT		3002			run-server-3002.sh
+3003	playground	momentry_playground	MOMENTRY_SERVER_PORT		3003			start_momentry.sh
+3200	dashboard	Caddy			-				-			Caddyfile
+3306	mariadb		mariadbd		-				3306			start_momentry.sh
+5432	postgresql	postgres		DATABASE_URL			postgres://...:5432	start_momentry.sh
+6379	redis		redis-server		REDIS_URL			redis://...:6379	start_momentry.sh
+6333	qdrant		qdrant			QDRANT_URL			http://...:6333		start_momentry.sh
+8081	wordpress	Caddy			-				-			Caddyfile
+8082	llm		llama-server		MOMENTRY_LLM_CHAT_URL		http://...:8082		start_momentry.sh
+9000	php-fpm		php-fpm			-				9000			brew services
+11434	ollama		ollama			MOMENTRY_OLLAMA_URL		http://...:11434	start_momentry.sh
+11436	embedding	embeddinggemma		MOMENTRY_EMBED_URL		http://...:11436	start_momentry.sh
@@ -1,98 +0,0 @@
-use anyhow::Result;
-use sqlx::postgres::PgPoolOptions;
-
-#[tokio::main]
-async fn main() -> Result<()> {
-    // Database connection
-    let pool = PgPoolOptions::new()
-        .max_connections(5)
-        .connect("postgres://accusys@localhost:5432/momentry")
-        .await?;
-
-    let video_uuid = "9760d0820f0cf9a7";
-    let video_id = 28;
-    let video_path = "/Users/accusys/momentry/var/sftpgo/data/demo/ExaSAN PCIe series - Director Ou Yu-Zhi Shares His Experience.mp4";
-
-    println!("Creating monitor job for video:");
-    println!("  UUID: {}", video_uuid);
-    println!("  ID: {}", video_id);
-    println!("  Path: {}", video_path);
-
-    // 1. Create monitor job
-    let job_row = sqlx::query(
-        r#"
-        INSERT INTO monitor_jobs (uuid, video_path, status)
-        VALUES ($1, $2, 'pending')
-        RETURNING id, uuid, video_path, status
-        "#
-    )
-    .bind(video_uuid)
-    .bind(video_path)
-    .fetch_one(&pool)
-    .await?;
-
-    let job_id: i32 = job_row.get(0);
-    let job_uuid: String = job_row.get(1);
-    let job_status: String = job_row.get(3);
-
-    println!("\nCreated monitor job:");
-    println!("  Job ID: {}", job_id);
-    println!("  Job UUID: {}", job_uuid);
-    println!("  Status: {}", job_status);
-
-    // 2. Update video with job_id
-    sqlx::query(
-        r#"
-        UPDATE videos 
-        SET job_id = $1, updated_at = CURRENT_TIMESTAMP 
-        WHERE id = $2
-        "#
-    )
-    .bind(job_id)
-    .bind(video_id)
-    .execute(&pool)
-    .await?;
-
-    println!("Updated video {} with job_id {}", video_id, job_id);
-
-    // 3. Update monitor_jobs with video_id
-    sqlx::query(
-        r#"
-        UPDATE monitor_jobs 
-        SET video_id = $1, updated_at = CURRENT_TIMESTAMP 
-        WHERE id = $2
-        "#
-    )
-    .bind(video_id)
-    .bind(job_id)
-    .execute(&pool)
-    .await?;
-
-    println!("Updated monitor_jobs {} with video_id {}", job_id, video_id);
-
-    // 4. Create processor results for this job
-    let processors = vec!["asr", "cut", "yolo", "ocr", "face", "pose", "asrx"];
-    
-    for processor in processors {
-        sqlx::query(
-            r#"
-            INSERT INTO processor_results (job_id, video_id, processor, status)
-            VALUES ($1, $2, $3, 'pending')
-            ON CONFLICT (job_id, processor) DO NOTHING
-            "#
-        )
-        .bind(job_id)
-        .bind(video_id)
-        .bind(processor)
-        .execute(&pool)
-        .await?;
-
-        println!("Created processor result for {}: {}", processor, job_id);
-    }
-
-    println!("\n✅ Job creation completed successfully!");
-    println!("Job ID: {}", job_id);
-    println!("The worker should now pick up this job and start processing.");
-
-    Ok(())
-}
@@ -1,7 +0,0 @@
-- 1. Create monitor job
-INSERT INTO monitor_jobs (uuid, video_path, status)
-VALUES ('9760d0820f0cf9a7', '/Users/accusys/momentry/var/sftpgo/data/demo/ExaSAN PCIe series - Director Ou Yu-Zhi Shares His Experience.mp4', 'pending')
-RETURNING id;
-
-- Note: The job_id will be returned. Let's assume it's 18 for now.
-- We'll run these commands step by step.
@@ -1,150 +0,0 @@
-#!/usr/bin/env python3
-"""
-Debug ASR processing stages for large video.
-"""
-
-import os
-import sys
-import time
-import subprocess
-import tempfile
-import json
-from pathlib import Path
-
-
-def run_ffmpeg_extract(video_path, audio_path):
-    """Extract audio using ffmpeg."""
-    cmd = [
-        "ffmpeg",
-        "-i",
-        str(video_path),
-        "-vn",
-        "-acodec",
-        "pcm_s16le",
-        "-ar",
-        "16000",
-        "-ac",
-        "1",
-        "-y",
-        str(audio_path),
-    ]
-    print(f"Running ffmpeg: {' '.join(cmd)}")
-    start = time.time()
-    proc = subprocess.run(cmd, capture_output=True, text=True)
-    elapsed = time.time() - start
-    print(f"ffmpeg completed in {elapsed:.1f}s, return code: {proc.returncode}")
-    if proc.returncode != 0:
-        print(f"stderr: {proc.stderr[:500]}")
-    return proc.returncode == 0, elapsed
-
-
-def test_asr_stages(video_path):
-    """Test ASR stages step by step."""
-    video_path = Path(video_path)
-    print(f"Testing video: {video_path}")
-    print(f"Size: {video_path.stat().st_size / 1024 / 1024:.1f} MB")
-
-    # Stage 1: Check audio streams
-    print("\n=== Stage 1: Check audio streams ===")
-    cmd = [
-        "ffprobe",
-        "-v",
-        "error",
-        "-select_streams",
-        "a",
-        "-show_entries",
-        "stream=codec_name,channels,sample_rate,duration",
-        "-of",
-        "csv=p=0",
-        str(video_path),
-    ]
-    proc = subprocess.run(cmd, capture_output=True, text=True)
-    print(f"Audio streams: {proc.stdout.strip()}")
-
-    # Stage 2: Extract audio
-    print("\n=== Stage 2: Extract audio ===")
-    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
-        audio_path = f.name
-    try:
-        success, extract_time = run_ffmpeg_extract(video_path, audio_path)
-        if success:
-            print(f"Audio extracted to {audio_path}")
-            print(f"Audio size: {Path(audio_path).stat().st_size / 1024 / 1024:.1f} MB")
-        else:
-            print("Audio extraction failed")
-            os.unlink(audio_path)
-            return
-    except Exception as e:
-        print(f"Error extracting audio: {e}")
-        return
-
-    # Stage 3: Load faster_whisper model (just import)
-    print("\n=== Stage 3: Test faster_whisper import ===")
-    try:
-        start = time.time()
-        from faster_whisper import WhisperModel
-
-        elapsed = time.time() - start
-        print(f"Import faster_whisper: {elapsed:.1f}s")
-    except Exception as e:
-        print(f"Import failed: {e}")
-        os.unlink(audio_path)
-        return
-
-    # Stage 4: Transcribe a small segment (first 30 seconds)
-    print("\n=== Stage 4: Transcribe first 30 seconds ===")
-    try:
-        # Trim audio to first 30 seconds
-        trim_path = audio_path + ".trim.wav"
-        cmd = [
-            "ffmpeg",
-            "-i",
-            audio_path,
-            "-t",
-            "30",
-            "-acodec",
-            "pcm_s16le",
-            "-ar",
-            "16000",
-            "-ac",
-            "1",
-            "-y",
-            trim_path,
-        ]
-        subprocess.run(cmd, capture_output=True)
-
-        # Load model with small model
-        start = time.time()
-        model = WhisperModel("tiny", device="cpu", compute_type="int8")
-        load_time = time.time() - start
-        print(f"Model loaded in {load_time:.1f}s")
-
-        # Transcribe
-        start = time.time()
-        segments, info = model.transcribe(trim_path, beam_size=5)
-        segments = list(segments)  # Force processing
-        transcribe_time = time.time() - start
-        print(f"Transcription of 30s audio: {transcribe_time:.1f}s")
-        print(
-            f"Detected language: {info.language} with probability {info.language_probability}"
-        )
-        print(f"Segments found: {len(segments)}")
-
-        # Cleanup
-        os.unlink(trim_path)
-    except Exception as e:
-        print(f"Transcription test failed: {e}")
-        import traceback
-
-        traceback.print_exc()
-    finally:
-        os.unlink(audio_path)
-
-    print("\n=== Debug complete ===")
-
-
-if __name__ == "__main__":
-    if len(sys.argv) != 2:
-        print(f"Usage: {sys.argv[0]} <video_file>")
-        sys.exit(1)
-    test_asr_stages(sys.argv[1])
@@ -1,85 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import time
-
-print("Start")
-print("Importing faster_whisper...")
-try:
-    from faster_whisper import WhisperModel
-
-    print("Import successful")
-except Exception as e:
-    print(f"Import failed: {e}")
-    sys.exit(1)
-
-print("Loading model...")
-try:
-    model = WhisperModel("tiny", device="cpu", compute_type="int8")
-    print("Model loaded")
-except Exception as e:
-    print(f"Model load failed: {e}")
-    sys.exit(1)
-
-import subprocess
-
-print("Getting duration...")
-cmd = [
-    "ffprobe",
-    "-v",
-    "error",
-    "-show_entries",
-    "format=duration",
-    "-of",
-    "csv=p=0",
-    "/tmp/test_audio.wav",
-]
-result = subprocess.run(cmd, capture_output=True, text=True)
-print(f"ffprobe output: {result.stdout}")
-duration = float(result.stdout.strip())
-print(f"Duration: {duration}")
-
-# Extract first chunk
-print("Extracting first chunk...")
-chunk_path = "/tmp/debug_chunk.wav"
-cmd = [
-    "ffmpeg",
-    "-i",
-    "/tmp/test_audio.wav",
-    "-t",
-    "60",
-    "-acodec",
-    "pcm_s16le",
-    "-ar",
-    "16000",
-    "-ac",
-    "1",
-    "-y",
-    chunk_path,
-]
-result = subprocess.run(cmd, capture_output=True, text=True)
-print(f"ffmpeg return code: {result.returncode}")
-if result.returncode != 0:
-    print(f"stderr: {result.stderr[:200]}")
-
-import os
-
-print(f"Chunk exists: {os.path.exists(chunk_path)}")
-if os.path.exists(chunk_path):
-    print(f"Chunk size: {os.path.getsize(chunk_path)}")
-
-    print("Transcribing chunk...")
-    start = time.time()
-    try:
-        segments, info = model.transcribe(chunk_path, beam_size=5)
-        segments = list(segments)
-        elapsed = time.time() - start
-        print(f"Transcription succeeded in {elapsed}s, segments: {len(segments)}")
-    except Exception as e:
-        print(f"Transcription failed: {e}")
-        import traceback
-
-        traceback.print_exc()
-else:
-    print("Chunk not created")
-
-print("Script finished")
@@ -0,0 +1,516 @@
+<!-- module: identity -->
+<!-- description: Global identities — CRUD, detail, files, faces, bind, unbind, search -->
+<!-- depends: 01_auth -->
+
+## Global Identities
+
+### `GET /api/v1/identities`
+
+**Auth**: Required
+**Scope**: identity-level
+
+List all registered identities with pagination.
+
+#### Example
+
+```bash
+curl -s "$API/api/v1/identities?page=1&page_size=20" -H "X-API-Key: $KEY" | jq '{count, identities: [.identities[] | {name}]}'
+```
+
+---
+
+### `GET /api/v1/identity/:identity_uuid`
+
+**Auth**: Required
+**Scope**: identity-level
+
+Get detailed information for a specific identity, including metadata and TMDb references.
+
+#### Example
+
+```bash
+curl -s "$API/api/v1/identity/$IDENTITY_UUID" -H "X-API-Key: $KEY"
+```
+
+#### Response (200)
+
+```json
+{
+  "success": true,
+  "identity_uuid": "a9a901056d6b46ff92da0c3c1a57dff4",
+  "name": "Cary Grant",
+  "identity_type": "people",
+  "source": "tmdb",
+  "status": "confirmed",
+  "tmdb_id": 112,
+  "tmdb_profile": "{output}/identities/{identity_uuid}/profile.jpg",
+  "metadata": {},
+  "reference_data": {},
+  "created_at": "2026-05-16T12:00:00Z",
+  "updated_at": null
+}
+```
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `identity_uuid` | string | Identity identifier |
+| `name` | string | Identity name |
+| `identity_type` | string | `"people"` or null |
+| `source` | string | `.json`, `auto`, `tmdb`, `user_defined`, or `merged` |
+| `status` | string | `"confirmed"`, `"pending"`, or `"inactive"` |
+| `tmdb_id` | integer | TMDb person ID (only if source = tmdb) |
+| `tmdb_profile` | string | Local profile image path (`{output}/identities/{uuid}/profile.jpg`) |
+| `metadata` | object | Metadata JSON (tmdb_character, cast_order, etc.) |
+| `created_at` | string | Creation timestamp |
+
+---
+
+### `DELETE /api/v1/identity/:identity_uuid`
+
+**Auth**: Required
+**Scope**: identity-level
+
+Delete an identity permanently.
+
+---
+
+### `PATCH /api/v1/identity/:identity_uuid`
+
+**Auth**: Required
+**Scope**: identity-level
+
+Partially update an identity. Only provided fields are modified. The `name` field is a display label and may repeat across identities. Aliases for multilingual display are stored in `metadata.aliases` (see BCP 47 reference below).
+
+#### Request (JSON, all fields optional)
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `name` | string | New display name |
+| `metadata` | object | Merged into existing metadata. Use `"aliases"` key for locale-tagged names |
+| `status` | string | `"confirmed"`, `"pending"`, or `"skipped"` |
+| `identity_type` | string | `"people"`, `"brand"`, `"object"`, `"concept"`, etc. |
+
+#### Example
+
+```bash
+curl -s -X PATCH "$API/api/v1/identity/$IDENTITY_UUID" \
+  -H "X-API-Key: $KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "name": "John Smith",
+    "metadata": {
+      "aliases": [
+        {"locale": "en", "name": "John Smith"},
+        {"locale": "zh-TW", "name": "約翰·史密斯"},
+        {"locale": "ja", "name": "ジョン・スミス"}
+      ]
+    }
+  }'
+```
+
+#### Response (200)
+
+```json
+{
+  "success": true,
+  "identity_uuid": "a9a901056d6b46ff92da0c3c1a57dff4",
+  "updated_fields": ["name", "metadata"]
+}
+```
+
+#### Error Responses
+
+| HTTP | When |
+|------|------|
+| `400` | No fields to update or invalid UUID format |
+| `404` | Identity not found |
+
+---
+
+### `GET /api/v1/identity/:identity_uuid/files`
+
+**Auth**: Required
+**Scope**: identity-level
+
+Get all files where this identity appears. Returns per-file summary including face count, confidence, and appearance time range.
+
+#### Example
+
+```bash
+curl -s "$API/api/v1/identity/$IDENTITY_UUID/files" -H "X-API-Key: $KEY"
+```
+
+#### Response (200)
+
+```json
+{
+  "success": true,
+  "identity_uuid": "c3545906c82d4b66aa1d150bc02decce",
+  "total": 1,
+  "page": 1,
+  "page_size": 20,
+  "data": [
+    {
+      "file_uuid": "aeed71342a899fe4b4c57b7d41bcb692",
+      "file_name": "Charade (1963) Cary Grant & Audrey Hepburn.mp4",
+      "file_path": "/path/to/videos/Charade.mp4",
+      "status": "completed",
+      "face_count": 19695,
+      "speaker_count": 0,
+      "first_appearance": 206.76,
+      "last_appearance": 6756.68,
+      "confidence": 0.803
+    }
+  ]
+}
+```
+
+#### Response Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `file_uuid` | string | File identifier (full 32-char hex) |
+| `file_name` | string | Video file name |
+| `file_path` | string | Absolute path to video file |
+| `status` | string | Video processing status (`"completed"`, `"processing"`, etc.) |
+| `face_count` | int | Total face detections for this identity in this file |
+| `speaker_count` | int | Speaker segments (reserved, always `0`) |
+| `first_appearance` | float | First appearance time in seconds (computed from `frame_number / fps`) |
+| `last_appearance` | float | Last appearance time in seconds |
+| `confidence` | float | Average detection confidence |
+
+---
+
+### `GET /api/v1/identity/:identity_uuid/faces`
+
+**Auth**: Required
+**Scope**: identity-level
+
+Get all face detection records associated with this identity.
+
+#### Example
+
+```bash
+curl -s "$API/api/v1/identity/$IDENTITY_UUID/faces?page=1&page_size=20" -H "X-API-Key: $KEY"
+```
+
+#### Response (200)
+
+```json
+{
+  "success": true,
+  "identity_uuid": "c3545906c82d4b66aa1d150bc02decce",
+  "total": 19695,
+  "page": 1,
+  "page_size": 20,
+  "data": [
+    {
+      "id": 655704,
+      "file_uuid": "aeed71342a899fe4b4c57b7d41bcb692",
+      "frame_number": 5169,
+      "timestamp_secs": 206.76,
+      "face_id": "5169_0",
+      "bbox": {
+        "x": 706,
+        "y": 469,
+        "width": 618,
+        "height": 618
+      },
+      "confidence": 0.855
+    }
+  ]
+}
+```
+
+#### Response Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | int64 | Face detection record ID |
+| `file_uuid` | string | File where face was detected |
+| `frame_number` | int64 | Frame number (primary coordinate) |
+| `timestamp_secs` | float | Time in seconds (computed as `frame_number / fps`) |
+| `face_id` | string | Face ID (format: `{frame_number}_{detection_index}`) |
+| `bbox` | object | Bounding box |
+| `bbox.x` | float | Left coordinate |
+| `bbox.y` | float | Top coordinate |
+| `bbox.width` | float | Width in pixels |
+| `bbox.height` | float | Height in pixels |
+| `confidence` | float | Detection confidence (0.0–1.0) |
+
+---
+
+### `GET /api/v1/identity/:identity_uuid/chunks`
+
+**Auth**: Required
+**Scope**: identity-level
+
+Get all text chunks (sentences) spoken while this identity's face was on screen. Useful for finding what a person said.
+
+#### Example
+
+```bash
+curl -s "$API/api/v1/identity/$IDENTITY_UUID/chunks" -H "X-API-Key: $KEY"
+```
+
+#### Response (200)
+
+```json
+{
+  "success": true,
+  "identity_uuid": "a9a901056d6b46ff92da0c3c1a57dff4",
+  "data": [
+    {
+      "id": 0,
+      "file_uuid": "bd80fec92b0b6963d177a2c55bf713e2",
+      "chunk_id": "bd80fec92b0b6963d177a2c55bf713e2_2",
+      "chunk_type": "sentence",
+      "start_frame": 5103,
+      "end_frame": 5127,
+      "fps": 24.0,
+      "start_time": 212.64,
+      "end_time": 213.64,
+      "text_content": "[213s-214s] Cary Grant: \"Olá!\""
+    }
+  ]
+}
+```
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `file_uuid` | string | File identifier |
+| `chunk_id` | string | Sentence chunk identifier |
+| `start_frame` | integer | Frame-accurate start position |
+| `end_frame` | integer | Frame-accurate end position |
+| `fps` | float | Frames per second |
+| `start_time` | float | Start time in seconds |
+| `end_time` | float | End time in seconds |
+| `text_content` | string | Spoken text content |
+
+---
+
+### `POST /api/v1/identity/:identity_uuid/bind`
+
+**Auth**: Required
+**Scope**: identity-level
+
+Bind a face detection to an identity. Associates the face trace with the identity for future search and recognition.
+
+#### Request Parameters
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `file_uuid` | string | Yes | File where face is detected |
+| `face_id` | string | Yes | Face ID (format: `{frame}_{idx}`) |
+
+#### Example
+
+```bash
+curl -s -X POST "$API/api/v1/identity/$IDENTITY_UUID/bind" \
+  -H "X-API-Key: $KEY" \
+  -H "Content-Type: application/json" \
+  -d '{"file_uuid": "'"$FILE_UUID"'", "face_id": "1_5"}'
+```
+
+---
+
+### `POST /api/v1/identity/:identity_uuid/unbind`
+
+**Auth**: Required
+**Scope**: identity-level
+
+Unbind a face detection from an identity. Removes the identity association from the face record.
+
+---
+
+### `GET /api/v1/identities/search`
+
+**Auth**: Required
+**Scope**: identity-level
+
+Search identities by name (ILIKE search). Returns matching identity records.
+
+#### Example
+
+```bash
+curl -s "$API/api/v1/identities/search?q=Cary" -H "X-API-Key: $KEY"
+```
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `name` | string | Identity name |
+| `source` | string | Identity source |
+| `tmdb_id` | integer | TMDb ID (if source = tmdb) |
+| `file_uuid` | string | Associated file |
+
+---
+
+---
+
+### `POST /api/v1/identity/upload`
+
+**Auth**: Required
+**Scope**: identity-level
+
+Upload an identity.json file to create or update an identity. Accepts the same format as the identity.json files stored on disk.
+
+If an identity with the same `identity_uuid` already exists, it will be updated with the new values.
+
+#### Request
+
+The request body is an `IdentityFile` object:
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `identity_uuid` | string | Yes | Identity identifier |
+| `name` | string | Yes | Identity display name |
+| `identity_type` | string | No | `"people"` or null |
+| `source` | string | No | `.json`, `auto`, `tmdb`, `user_defined`, or `merged` |
+| `status` | string | No | `"confirmed"`, `"pending"`, or `"inactive"` |
+| `tmdb_id` | integer | No | TMDb person ID |
+| `tmdb_profile` | string | No | TMDb profile image URL |
+| `metadata` | object | No | Arbitrary metadata JSON |
+| `file_bindings` | array | No | Array of `{ file_uuid, trace_ids, face_count }` (informational) |
+
+#### Example
+
+```bash
+curl -s -X POST "$API/api/v1/identity/upload" \
+  -H "X-API-Key: $KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "version": 1,
+    "identity_uuid": "a9a901056d6b46ff92da0c3c1a57dff4",
+    "name": "Cary Grant",
+    "identity_type": "people",
+    "source": ".json",
+    "status": "confirmed",
+    "metadata": {},
+    "file_bindings": []
+  }'
+```
+
+#### Response (200)
+
+```json
+{
+  "success": true,
+  "identity_uuid": "a9a901056d6b46ff92da0c3c1a57dff4",
+  "name": "Cary Grant",
+  "message": "Identity uploaded successfully"
+}
+```
+
+---
+
+---
+
+### `POST /api/v1/identity/:identity_uuid/profile-image`
+
+**Auth**: Required
+**Scope**: identity-level
+
+Upload a profile image (JPEG or PNG) for an identity. The image is saved to `{output}/identities/{uuid}/profile.{ext}`.
+
+Uses `multipart/form-data` with field name `image`.
+
+#### Example
+
+```bash
+curl -s -X POST "$API/api/v1/identity/$IDENTITY_UUID/profile-image" \
+  -H "X-API-Key: $KEY" \
+  -F "image=@/path/to/photo.jpg"
+```
+
+#### Response (200)
+
+```json
+{
+  "success": true,
+  "identity_uuid": "a9a901056d6b46ff92da0c3c1a57dff4",
+  "path": "/path/to/output/identities/.../profile.jpg",
+  "message": "Profile image saved: profile.jpg"
+}
+```
+
+#### Error Responses
+
+| HTTP | When |
+|------|------|
+| `400` | Missing image field or unsupported format |
+| `404` | Identity not found |
+| `415` | Unsupported image type (use JPEG or PNG) |
+
+---
+
+### `GET /api/v1/identity/:identity_uuid/profile-image`
+
+**Auth**: Required
+**Scope**: identity-level
+
+Retrieve the profile image for an identity. Returns the raw image data with appropriate Content-Type header.
+
+```bash
+curl -s "$API/api/v1/identity/$IDENTITY_UUID/profile-image" \
+  -H "X-API-Key: $KEY" -o profile.jpg
+```
+
+| Response Header | Value |
+|----------------|-------|
+| `content-type` | `image/jpeg` or `image/png` |
+
+---
+
+## Alias System (BCP 47 Locale Tags)
+
+Identity aliases support multilingual display names. Aliases are stored in `metadata.aliases` as an array of `{locale, name}` objects.
+
+### BCP 47 Locale Tags Reference
+
+| Locale | Tag | Example |
+|--------|-----|---------|
+| English | `en` | John Smith |
+| Traditional Chinese | `zh-TW` | 約翰·史密斯 |
+| Simplified Chinese | `zh-CN` | 约翰·史密斯 |
+| Japanese | `ja` | ジョン・スミス |
+| Korean | `ko` | 존 스미스 |
+| Cantonese | `yue` | 約翰·史密夫 |
+| French | `fr` | John Smith (French spelling) |
+| Spanish | `es` | Juan Smith |
+| Arabic | `ar` | جون سميث |
+| Russian | `ru` | Джон Смит |
+| Thai | `th` | จอห์น สมิธ |
+
+BCP 47 is the IETF standard for language tags. Format: `language` (e.g. `en`, `ja`) or `language-Region` (e.g. `zh-TW`, `zh-CN`).
+
+### Frontend Display Logic
+
+```javascript
+function getDisplayName(identity, preferredLocale) {
+  const match = identity.metadata?.aliases?.find(a => a.locale === preferredLocale);
+  if (match) return match.name;
+  const lang = preferredLocale.split('-')[0];
+  const langMatch = identity.metadata?.aliases?.find(a => a.locale.startsWith(lang));
+  if (langMatch) return langMatch.name;
+  return identity.name;
+}
+```
+
+### Updating Aliases via PATCH
+
+```json
+PATCH /api/v1/identity/:identity_uuid
+{
+  "metadata": {
+    "aliases": [
+      {"locale": "en", "name": "John Smith"},
+      {"locale": "zh-TW", "name": "約翰·史密斯"}
+    ]
+  }
+}
+```
+
+---
+*Updated: 2026-05-22*
+
+
@@ -0,0 +1,317 @@
+<!-- module: media -->
+<!-- description: Video streaming & frame extraction -->
+<!-- depends: 01_auth -->
+
+## Video Streaming & Frame Extraction
+
+All video streaming endpoints support the following common query parameters:
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `mode` | string | No | `normal` | `normal` or `debug` (draws detection overlays) |
+| `audio` | string | No | `on` | `on` or `off` |
+
+---
+
+### `GET /api/v1/file/:file_uuid/video`
+
+Stream the full video file with range support for seeking.
+
+**Auth**: Required
+**Scope**: file-level
+
+#### Response
+
+- **200**: Video stream (`Content-Type` based on file extension)
+- **206**: Partial content (range request)
+- Supports `Range` header for seeking
+
+---
+
+### `GET /api/v1/file/:file_uuid/trace/:trace_id/video`
+
+Stream video with highlights for a specific face trace (follows a single person across frames with bounding box overlay).
+
+**Auth**: Required
+**Scope**: file-level
+
+---
+
+### `GET /api/v1/file/:file_uuid/trace/:trace_id/representative-face`
+
+Find the best single face to represent this trace. Uses a two-stage selection: SQL (area × confidence → top 10) then FFmpeg `blurdetect` (sharpness → pick the least blurry).
+
+**Auth**: Required
+**Scope**: file-level
+
+#### Example
+
+```bash
+curl -s "$API/api/v1/file/$FILE_UUID/trace/1939/representative-face" \
+  -H "X-API-Key: $KEY"
+```
+
+#### Response (200)
+
+```json
+{
+  "success": true,
+  "file_uuid": "aeed71342a899fe4b4c57b7d41bcb692",
+  "trace_id": 1939,
+  "face_count": 538,
+  "representative": {
+    "frame_number": 68193,
+    "timestamp_secs": 2727.72,
+    "bbox": { "x": 347, "y": 378, "width": 427, "height": 427 },
+    "confidence": 0.760,
+    "quality_score": 138516,
+    "blur_score": 9.46
+  }
+}
+```
+
+#### Response Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `trace_id` | integer | Face trace ID |
+| `face_count` | integer | Total face detections in this trace |
+| `representative.frame_number` | integer | Frame number of the selected face (primary coordinate) |
+| `representative.timestamp_secs` | float | Time in seconds (derived from `frame_number / fps`) |
+| `representative.bbox` | object | Bounding box `{x, y, width, height}` |
+| `representative.confidence` | float | Detection confidence (0.0–1.0) |
+| `representative.quality_score` | float | Pre-selection score (`area × confidence`) |
+| `representative.blur_score` | float | FFmpeg blurdetect result (lower = sharper) |
+
+#### Error Responses
+
+---
+
+### `GET /api/v1/file/:file_uuid/trace/:trace_id/thumbnail`
+
+Extract the best face image for a trace as JPEG (320×320). Internally selects the face using the same two-stage algorithm as `representative-face`, then crops via FFmpeg. The result is cacheable for 24 hours.
+
+**Auth**: Required
+**Scope**: file-level
+
+#### Example
+
+```bash
+curl -s "$API/api/v1/file/$FILE_UUID/trace/1939/thumbnail" \
+  -H "X-API-Key: $KEY" -o trace_1939_face.jpg
+```
+
+#### Response
+
+- **200**: `image/jpeg` binary data (320×320 cropped face)
+- **404**: File, trace not found, or no suitable face
+- **500**: FFmpeg or database error
+
+---
+
+### `GET /api/v1/file/:file_uuid/identities/:identity_uuid_a/co-occur-with/:identity_uuid_b`
+
+Find the first frame where two identities appear together, with representative face thumbnails for both.
+
+**Auth**: Required
+**Scope**: file-level
+
+#### Example
+
+```bash
+# Audrey Hepburn & Cary Grant 第一次同框
+curl -s "$API/api/v1/file/$FILE_UUID/identities/$AUDREY_UUID/co-occur-with/$CARY_UUID" \
+  -H "X-API-Key: $KEY" | jq '{identity_a: .identity_a.name, identity_b: .identity_b.name, first_frame: .first_cooccurrence.frame_number}'
+```
+
+#### Response (200)
+
+```json
+{
+  "success": true,
+  "file_uuid": "aeed71342a899fe4b4c57b7d41bcb692",
+  "identity_a": {
+    "identity_uuid": "c3545906-c82d-4b66-aa1d-150bc02decce",
+    "name": "Audrey Hepburn",
+    "trace_id": 920
+  },
+  "identity_b": {
+    "identity_uuid": "2b0ddefe-e2a9-4533-9308-b375594604d5",
+    "name": "Cary Grant",
+    "trace_id": 919
+  },
+  "first_cooccurrence": {
+    "frame_number": 38165,
+    "timestamp_secs": 1526.60,
+    "total_cooccurrence_frames": 3136,
+    "representative_face_a": {
+      "frame_number": 38199,
+      "bbox": { "x": 122, "y": 339, "width": 176, "height": 176 },
+      "confidence": 0.832,
+      "thumbnail_url": "/api/v1/file/aeed71342.../trace/920/thumbnail"
+    },
+    "representative_face_b": {
+      "frame_number": 38291,
+      "bbox": { "x": 511, "y": 315, "width": 192, "height": 192 },
+      "confidence": 0.791,
+      "thumbnail_url": "/api/v1/file/aeed71342.../trace/919/thumbnail"
+    }
+  }
+}
+```
+
+#### Response Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `identity_a.name` | string | First identity name |
+| `identity_b.name` | string | Second identity name |
+| `first_cooccurrence.frame_number` | int | First frame where both appear |
+| `first_cooccurrence.timestamp_secs` | float | Time in seconds |
+| `first_cooccurrence.total_cooccurrence_frames` | int | Total frames with both present |
+| `first_cooccurrence.representative_face_a/b` | object | Best face thumbnail data for each identity |
+
+#### Error Responses
+
+| HTTP | When |
+|------|------|
+| `404` | File or identity not found |
+| `404` | The two identities never co-occur in this file |
+| `500` | Database or FFmpeg error |
+
+### `GET /api/v1/file/:file_uuid/video/bbox`
+
+Stream video with bounding box overlay for all detected objects/faces.
+
+**Auth**: Required
+**Scope**: file-level
+
+Uses a built-in 5×7 bitmap font renderer to draw labels directly on video frames via FFmpeg `drawtext` filter.
+
+---
+
+### `GET /api/v1/file/:file_uuid/thumbnail`
+
+Extract a single frame from a video as JPEG image. Uses FFmpeg `select` filter.
+
+**Auth**: Required
+**Scope**: file-level
+
+#### Query Parameters
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `frame` | integer | Yes | — | Zero-based frame number to extract |
+| `x` | integer | No | — | Crop start X (left edge). Requires `y`, `w`, `h`. |
+| `y` | integer | No | — | Crop start Y (top edge). Requires `x`, `w`, `h`. |
+| `w` | integer | No | — | Crop width in pixels. Requires `x`, `y`, `h`. |
+| `h` | integer | No | — | Crop height in pixels. Requires `x`, `y`, `w`. |
+
+All four crop params (`x`, `y`, `w`, `h`) must be provided together or omitted.
+
+#### Example
+
+```bash
+# Extract frame 1000 (full frame)
+curl -s "$API/api/v1/file/bd80fec92b0b6963d177a2c55bf713e2/thumbnail?frame=1000" \
+  -H "Authorization: Bearer $JWT" -o frame_1000.jpg
+
+# Extract and crop face region (x=320, y=240, w=160, h=160)
+curl -s "$API/api/v1/file/bd80fec92b0b6963d177a2c55bf713e2/thumbnail?frame=1000&x=320&y=240&w=160&h=160" \
+  -H "Authorization: Bearer $JWT" -o face_crop.jpg
+```
+
+#### Response
+
+- **200**: `image/jpeg` binary data
+- **404**: File not found
+- **500**: FFmpeg error (e.g., frame number exceeds video duration)
+
+### `GET /api/v1/file/:file_uuid/clip`
+
+Extract a video clip (time range) as MPEG-TS stream. Uses FFmpeg `-ss` fast seek.
+
+**Auth**: Required
+**Scope**: file-level
+
+#### Query Parameters
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `start_frame` | integer | No* | — | Start frame (zero-based). **Frame-accurate** — use this for precision. |
+| `end_frame` | integer | No* | — | End frame (zero-based, inclusive). Requires `start_frame`. |
+| `start_time` | float | No* | — | Start time in seconds. Approximate (FPS-dependent). Fallback if frames not given. |
+| `end_time` | float | No* | — | End time in seconds. Approximate (FPS-dependent). Fallback if frames not given. |
+| `fps` | float | No | video FPS | Override frames-per-second for frame↔time calculation. Defaults to video's detected FPS. |
+| `mode` | string | No | `normal` | `normal` or `debug` (draws "CLIP" overlay) |
+| `audio` | string | No | `on` | `on` or `off` |
+
+Either (`start_frame`+`end_frame`) OR (`start_time`+`end_time`) must be provided.
+
+#### Example
+
+```bash
+# Clip by frame range (primary)
+curl -s "$API/api/v1/file/bd80fec92b0b6963d177a2c55bf713e2/clip?start_frame=0&end_frame=47" \
+  -H "Authorization: Bearer $JWT" -o clip.ts
+
+# Clip by time range (fallback)
+curl -s "$API/api/v1/file/bd80fec92b0b6963d177a2c55bf713e2/clip?start_time=30&end_time=45" \
+  -H "Authorization: Bearer $JWT" -o clip.ts
+```
+
+#### Response
+
+- **200**: `video/mp2t` MPEG-TS stream
+- **400**: Missing/invalid range parameters
+- **404**: File not found
+- **500**: FFmpeg error
+
+#### Technical Notes
+
+| Detail | Value |
+|--------|-------|
+| **Backend** | FFmpeg (`ffmpeg-full`) |
+| **Seek** | `-ss` before `-i` (fast keyframe seek) |
+| **Format** | MPEG-TS (`mpegts` muxer, pipe-safe) |
+| **Codec** | H.264 + AAC |
+| **Cache** | `Cache-Control: public, max-age=86400` (24h) |
+
+### Video vs Clip: Quality & Format Comparison
+
+Both endpoints support time range extraction, but serve different use cases:
+
+| Feature | `/video` | `/clip` |
+|---------|----------|---------|
+| **No params** | Streams full file (Range seek) | Returns 400 (params required) |
+| **HTTP Range** | ✅ Supported | ❌ Not supported |
+| **Encoding** | `-c copy` (zero encoding) | `-c:v libx264 -c:a aac` (re-encode) |
+| **Quality** | Original (bit-exact, zero loss) | Compressed (default CRF ≈ 23) |
+| **Format** | `video/mp4` | `video/mp2t` (MPEG-TS) |
+| **Speed** | Fast (no computation) | Slower (encoding required) |
+| **Frame control** | Time-based (`dur = (ef-sf)/fps`) | Precise (`-vframes`) |
+| **Debug mode** | ❌ | ✅ `mode=debug` overlay |
+| **Cache** | ❌ | ✅ `max-age=86400` |
+
+#### Usage Recommendation
+
+| Scenario | Use |
+|----------|-----|
+| Full video streaming / player seek | `/video` |
+| Quick preview clip (zero quality loss) | `/video?start_frame=...&end_frame=...` |
+| Debug frame verification / text overlay | `/clip?mode=debug` |
+| Precise frame count control | `/clip` |
+| CDN cacheable clip | `/clip` |
+
+---
+
+| Detail | Value |
+|--------|-------|
+| **Backend** | FFmpeg (`ffmpeg-full`) |
+| **Filter** | `select=eq(n\,FRAME)` to select frame, optional `crop=W:H:X:Y` |
+| **Output** | Single JPEG via pipe (`image2pipe`, `mjpeg` codec) |
+| **Cache** | `Cache-Control: public, max-age=86400` (24h) |
+| **Frame number** | Zero-based (`frame=0` = first frame of video) |
+
+---
+*Updated: 2026-05-19 12:49:24*
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+ "bumpalo",
+ "proc-macro2",
+ "quote",
+ "syn",
+ "wasm-bindgen-shared",
+]
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+[[package]]
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@@ -0,0 +1,18 @@
+[package]
+name = "doc_wasm"
+version = "0.1.0"
+edition = "2021"
+
+[lib]
+crate-type = ["cdylib", "rlib"]
+
+[dependencies]
+wasm-bindgen = "0.2"
+pulldown-cmark = "0.11"
+serde = { version = "1", features = ["derive"] }
+serde_json = "1"
+
+[profile.release]
+lto = true
+opt-level = "s"
+strip = true
@@ -0,0 +1,29 @@
+use wasm_bindgen::prelude::*;
+
+#[wasm_bindgen]
+pub fn render_markdown(md: &str) -> String {
+    let parser = pulldown_cmark::Parser::new(md);
+    let mut html = String::new();
+    pulldown_cmark::html::push_html(&mut html, parser);
+    // wrap tables
+    html = html.replace("<table>", "<table class=\"table\">");
+    html
+}
+
+#[wasm_bindgen]
+pub fn module_list() -> String {
+    serde_json::to_string(&[
+        ("01_auth", "安全認證", "Authentication"),
+        ("02_health", "健康檢查", "Health"),
+        ("03_register", "檔案註冊", "File Registration"),
+        ("04_lookup", "檔案屬性查詢", "File Lookup"),
+        ("05_process", "處理流程", "Processing"),
+        ("06_search", "搜尋功能", "Search"),
+        ("07_identity", "身份識別", "Identity"),
+        ("08_identity_agent", "智能身份綁定", "Smart Identity Binding"),
+        ("08_media", "串流與截圖", "Streaming & Thumbnails"),
+        ("09_tmdb", "TMDb 整合", "TMDb Integration"),
+        ("10_pipeline", "生產線", "Pipeline"),
+        ("12_agent", "智慧代理", "AI Agents"),
+    ]).unwrap_or_default()
+}
@@ -0,0 +1,133 @@
+# ASR Model Selection Report
+
+**Date:** 2026-05-10
+**Video:** Charade (1963), 113min
+**Test setup:** faster-whisper on M5 MacBook Pro (Apple Silicon, CPU int8)
+
+## Test Clips
+
+| Clip | Time range | Duration | Characteristics |
+|------|-----------|----------|-----------------|
+| A — Rapid | 25:40–28:40 | 3 min | Fast back-and-forth dialogue, Cary & Audrey |
+| B — Normal | 10:00–13:00 | 3 min | Normal conversation pace |
+| C — Complex | 73:20–76:20 | 3 min | Multi-person scene, background audio |
+
+## Test Matrix
+
+| Variable | Values |
+|----------|--------|
+| Model | tiny, base, small, medium, large-v3 |
+| VAD min_silence | 200ms, 500ms |
+| Beam size | 5 (fixed) |
+
+## Results Summary
+
+### Clip A — Rapid Dialogue
+
+| Model | VAD | Segments | Chars | Runtime | Δ chars vs best |
+|-------|-----|----------|-------|---------|-----------------|
+| tiny | 200 | **55** | **1618** | **4.8s** | — |
+| tiny | 500 | **59** | 1582 | **4.8s** | −36 |
+| base | 200 | 50 | 1543 | 9.7s | −75 |
+| base | 500 | 51 | 1547 | 11.6s | −71 |
+| small | 200 | 47 | 1538 | 15.0s | −80 |
+| small | 500 | 47 | 1538 | 14.5s | −80 |
+| medium | 200 | 45 | 1241 | 34.0s | −377 |
+| medium | 500 | 45 | 1241 | 34.9s | −377 |
+| large-v3 | 200 | 14 | 916 | 42.1s | −702 |
+| large-v3 | 500 | 14 | 916 | 42.0s | −702 |
+
+**Winner: tiny** — 55–59 segments, most text captured, 4.8s (3× faster than small)
+
+### Clip B — Normal Dialogue
+
+| Model | VAD | Segments | Chars | Runtime | Δ chars vs best |
+|-------|-----|----------|-------|---------|-----------------|
+| tiny | 200 | 57 | 1875 | 11.9s | −40 |
+| tiny | 500 | **59** | 1801 | 10.9s | −114 |
+| base | 200 | 23 | 1695 | **5.1s** | −220 |
+| base | 500 | 23 | 1695 | **5.1s** | −220 |
+| small | 200 | **62** | 1731 | 15.7s | −184 |
+| small | 500 | **62** | 1731 | 16.4s | −184 |
+| medium | 200 | 59 | 1758 | 44.9s | −157 |
+| medium | 500 | 59 | 1758 | 44.8s | −157 |
+| large-v3 | 200 | 32 | **1915** | 95.6s | — |
+| large-v3 | 500 | — | — | — | — (slow) |
+
+**Winner: small** — 62 segments (most), good balance of speed vs accuracy
+**Note:** large-v3 captured 1915 chars (most text) but at 95.6s (6× slower than small)
+
+### Clip C — Complex Scene
+
+| Model | VAD | Segments | Chars | Runtime | Δ chars vs best |
+|-------|-----|----------|-------|---------|-----------------|
+| tiny | 200 | 54 | 1817 | 12.2s | −336 |
+| tiny | 500 | 52 | 1788 | 10.5s | −365 |
+| base | 200 | 51 | 2018 | 10.1s | −135 |
+| base | 500 | 51 | 2006 | 9.2s | −147 |
+| small | 200 | **64** | 1902 | 22.5s | −251 |
+| small | 500 | 61 | **2041** | 21.2s | −112 |
+| medium | 200 | 57 | 2044 | 999.3s | −109 |
+| medium | 500 | — | — | — | — (hang) |
+| large-v3 | 200 | — | — | — | — (hang) |
+| large-v3 | 500 | — | — | — | — (hang) |
+
+**Winner: base** — 51 segments, 2018 chars, 9.2s fastest reliable
+**Note:** medium and large-v3 both hang/timeout on complex audio in this scene
+
+## Aggregate Scores
+
+Weighted ranking (higher = better, equal weight: segment count, char count, inverse runtime):
+
+| Model | Segments (avg) | Chars (avg) | Runtime (avg) | Score | Rank |
+|-------|---------------|-------------|---------------|-------|------|
+| **tiny** | 56.0 | 1730 | **9.2s** | **8.5** | 🥇 |
+| **small** | 54.7 | 1704 | 17.6s | **7.8** | 🥈 |
+| base | 41.5 | 1751 | 10.1s | 7.0 | 🥉 |
+| medium | 51.5 | 1627 | 339.6s | 3.5 | 4 |
+| large-v3 | 20.0 | 1249 | 68.8s | 2.0 | 5 |
+
+## VAD Comparison (200ms vs 500ms)
+
+Averaged across all models and clips:
+
+| VAD | Segments | Chars | Runtime |
+|-----|----------|-------|---------|
+| 200ms | 45.9 | 1683 | 86.1s |
+| 500ms | 46.6 | 1685 | 69.2s |
+
+**Difference:** Negligible. VAD 200ms vs 500ms produces essentially identical results across all models.
+
+## Conclusions
+
+### 1. Smaller is better for this use case
+
+Contrary to expectations, **tiny and small** consistently outperform medium and large-v3 on every metric for Charade's dialogue:
+
+| Metric | tiny | large-v3 | Δ |
+|--------|------|----------|---|
+| Segments/clip | 56 | 20 | **+180%** |
+| Text captured | 98% | 72% | **+26%** |
+| Speed | 9.2s | 68.8s | **7.5× faster** |
+
+### 2. Large models lose text, not gain it
+
+medium and large-v3 produce fewer, longer segments that **merge multiple utterances together**, resulting in less total text. This is the opposite of what we need for segment-level speaker diarization.
+
+### 3. VAD parameter has minimal impact
+
+Changing `min_silence_duration_ms` between 200 and 500 produces <2% difference in all metrics. The current default (500ms) is fine.
+
+### 4. Recommendation
+
+**Keep current model: faster-whisper small (VAD 500ms)**
+
+| Reason | Detail |
+|--------|--------|
+| Segment quality | 47–64 segs/clip, clean sentence boundaries |
+| Speed | 14–22s per 3-min clip (real-time 0.1×) |
+| Stability | Never hangs, consistent across all scenes |
+| Text capture | 90–98% of best model |
+| Current integration | Already production-tested |
+
+The missing text problem for rapid dialogue is not solvable by model size — even tiny captures more text than large-v3. The root cause is Whisper's **lack of speaker turn detection** in its segment boundary logic, which is what ASRX (ECAPA-TDNN) is meant to solve.
@@ -0,0 +1,133 @@
+# ASR Segmentation Enhancement Report
+
+**Date:** 2026-05-10
+**Movie:** Charade (1963), 113 min
+**Goal:** Fix merged-speaker segments in ASR output by detecting speaker change points within ASR segments.
+
+## Problem
+
+Whisper ASR produces segments at sentence boundaries, but during rapid back-and-forth dialogue (common in Charade), a single ASR segment may contain utterances from **multiple speakers**:
+
+```
+ASR segment [1550.0-1554.0] (4.0s):
+  "What's she saying now?"
+
+Actual dialogue:
+  1552.7: Audrey: "What's she saying now?"
+  1553.4: Cary:   "That she's innocent."
+```
+
+The old ASRX pipeline (ECAPA-TDNN on ASR boundaries) assigned one speaker per ASR segment, losing the turn boundary.
+
+## Solution: Sliding-Window Speaker Change Detection
+
+### Detection Method
+
+Instead of relying on ASR segment boundaries, we:
+
+1. **Slide a 1.5s window (0.75s stride)** across the entire audio
+2. **Extract ECAPA-TDNN 192D embeddings** per window (239 windows per 3 min of audio)
+3. **Classify each window** against reference centroids built from the full movie's known speaker assignments
+4. **Smooth** with a 3-window majority filter (eliminates single-window noise)
+5. **Detect change points** where the classified speaker changes between adjacent windows
+6. **Split** the original ASR segment at each change point
+
+### Reference Centroids
+
+Built from the existing 3417 ASRX embedding set:
+- **Cary Grant**: centroid from 1420 known segments
+- **Audrey Hepburn**: centroid from 1689 known segments
+- **Unknown**: centroid from 308 segments (background/minor characters)
+
+Classification uses cosine similarity to nearest centroid, giving ~0.8+ similarity for main characters.
+
+### Validation: Gender Classification
+
+Each speaker cluster was independently validated via gender classification:
+
+| Cluster | Assigned | Voice Gender | Confidence |
+|---------|----------|-------------|------------|
+| SPEAKER_0 | Audrey Hepburn | FEMALE | 0.71 |
+| SPEAKER_1 | Cary Grant | MALE | 0.71 |
+| SPEAKER_2 | Unknown | MIXED | — |
+
+2 small clusters (10 segs each) initially showed MALE voice → "Audrey" assignment. These were segments where a male voice speaks while Audrey is on screen (old face-based matching was wrong). The fine-grained segmentation correctly resolves these.
+
+### Results
+
+| Metric | Before (ASR) | After (Fine) | Change |
+|--------|-------------|-------------|--------|
+| Total segments | 3,417 | **4,188** | **+771 (+22.6%)** |
+| Cary Grant | 1,420 | **2,033** | +613 |
+| Audrey Hepburn | 1,689 | **1,658** | −31 |
+| Unknown | 308 | **497** | +189 |
+| Avg segment duration | 2.0s | **1.6s** | −20% |
+
+### Effect on Problem Zone (1544-1565s)
+
+```
+BEFORE — ASR segments (47 total for 3min clip):
+[1544.0-1546.0] "Who's that with the hat?"           → single speaker
+[1546.0-1548.0] "That's the policeman."                → single speaker
+[1548.0-1550.0] "He wants to arrest Judy for Punch."   → single speaker
+[1550.0-1554.0] "What's she saying now?"               → merged! multiple speakers
+[1554.0-1557.5] "That she's innocent. She didn't do it." → merged
+[1557.5-1560.7] "Oh, she did it all right."            → merged
+...
+
+AFTER — Fine segments (64 total for 3min clip):
+[1550.3-1551.0] "He wants to arrest Judy..."           → Audrey Hepburn
+[1552.7-1553.4] "What's she saying now?"                → Audrey Hepburn
+[1553.4-1554.2] "now? That"                              → Cary Grant
+[1554.2-1559.3] "That she's innocent. She didn't..."    → Cary Grant
+[1559.3-1560.5] "Oh, she did it all right."             → Audrey Hepburn
+[1560.5-1561.6] "right. I"                               → Cary Grant
+[1561.6-1562.8] "I believe her."                        → Cary Grant
+```
+
+12 long ASR segments (>3s) were detected; 78% were successfully split into multi-speaker groups.
+
+### Text Acquisition
+
+Split segments needed their own text (since the parent ASR segment's text covers a different time range). Three approaches were tested:
+
+1. **Proportional split** (failed): Split text by time ratio → produces broken words
+2. **Word-timestamp ASR** (partially succeeded): faster-whisper with `word_timestamps=True` → 87% coverage; remaining gaps from ASR word boundary mismatches
+3. **Per-segment ASR** (fallback): Individual faster-whisper on empty segments → filled remaining 13%
+
+Final result: **4,188/4,188 segments with text.**
+
+### Voice Embeddings
+
+ECAPA-TDNN 192D embeddings were extracted per segment:
+- Runtime: 63s for 4,188 segments
+- Stored in `asrx_fine.json` alongside segment metadata
+
+### Data Files
+
+| File | Size | Description |
+|------|------|-------------|
+| `asrx_fine.json` | ~45 MB | 4,188 fine segments + 4,188 embeddings |
+| `asrx_fine.json → segments[].speaker_name` | — | Centroid-matched identity |
+| `asrx_fine.json → segments[].speaker_id` | — | SPEAKER_0/1/2 |
+| `asrx_fine.json → segments[].text` | — | ASR text (word-timestamp mapped) |
+| `asrx_fine.json → embeddings[]` | — | 192D ECAPA-TDNN per segment |
+
+### Continued Limitations
+
+1. **Word boundary alignment**: Split segment text sometimes has ±1 word due to sliding-window vs. ASR boundary mismatch (cosmetic, not semantic)
+2. **ASR merge in silence zones**: Very short utterances (<0.5s) merged into adjacent segments
+3. **Background speakers**: Multiple background speakers grouped as "Unknown"
+
+### Pipeline Integration
+
+The `asrx_fine.json` file serves as the new ASRX output. The original `asr.json` (3,417 segments with text) remains the primary text source, while `asrx_fine.json` provides superior speaker diarization at 4,188 segments.
+
+Speaker assignments in DB `dev.chunks` metadata were updated with `fine_speaker_name` and `fine_speaker_id` fields. Qdrant collections `momentry_dev_v1`, `sentence_story`, `sentence_summary` payloads were batch-updated with new speaker_name/speaker_id.
+
+### Hardware & Performance
+
+- Machine: M5 MacBook Pro, 48GB, Apple Silicon
+- Model: faster-whisper small (int8 CPU)
+- Embedding: ECAPA-TDNN via SpeechBrain
+- Total processing time: ~5 min for the full 113-min movie
@@ -0,0 +1,255 @@
+# Charade 臉部匹配經驗總結
+
+## 背景
+
+Charade (1963) 影片 `a6fb22eebefaef17e62af874997c5944` 有 62,298 個人臉偵測結果，分布在 4,378 個 trace 中（TKG face tracker 輸出）。目標是將每張臉匹配到正確的 TMDb 演員 identity。
+
+## 問題
+
+### 1. Rust Pipeline (`face_agent.rs`) 的 Snowball 效應
+
+原始 pipeline 透過多輪 propagation 來匹配：
+- Seed embedding 匹配 → propagation rounds (2-10 輪)
+- 每輪把已匹配的 face 當作新 seed 繼續擴散
+- 結果：**Antonio Passalia 被匹配 18,821 張臉**（實際應 < 50）
+- 原因：propagation 會放大初始匹配中的假陽性
+
+### 2. Dev 資料庫污染
+
+`dev` schema 的 `identity_bindings` 表：
+- 所有 trace-type binding 的 `file_uuid` 都是 NULL（12,828 行）
+- 這些 binding 只對應已刪除的 CCBN 檔案 (`63acd3bb`)
+- **完全無法用於 sync 到 public schema**
+
+### 3. TMDb Seed Embedding 品質不均
+
+22/23 個 TMDb identity 有 face_embedding（Thomas Chelimsky 因無 TMDb 照片而缺少）。但這些 seed 來自單一 TMDb 照片，品質差異大：
+
+| Identity | Seed 品質 | 問題 |
+|----------|:---------:|:----:|
+| Audrey Hepburn | ✅ 高 | 特徵明顯，易區分 |
+| Cary Grant | ✅ 中 | 但 Charade 造型與 seed 照片有差異 |
+| Walter Matthau | ❌ 低 | Seed 照片與 Charade 形象差異大 |
+| Bernard Musson | ❌ 泛用 | 「典型白人男性」— seed 太泛用 |
+| Antonio Passalia | ❌ 泛用 | 同上 |
+
+## 解決方案演進
+
+### V1：直接 pgvector 比對 (threshold 0.50)
+
+```sql
+CROSS JOIN LATERAL (
+  SELECT i.id FROM identities i
+  WHERE 1 - (embedding <=> i.face_embedding) >= 0.50
+  ORDER BY 1 - (embedding <=> i.face_embedding) DESC LIMIT 1
+)
+```
+
+**結果**：17,066 匹配 (27.4%)
+- ✅ Audrey 9,550 (正確)
+- ✅ Antonio 降為 151 (不再 snowball)
+- ❌ Bernard Musson 847／Paul Bonifas 273 — generic seed 假陽性
+- ❌ trace-level 衝突（同一 trace 多個 identity）
+- ❌ Walter Matthau 僅 535（seed 不準導致 recall 低）
+
+### V2：Trace Conflict Cleanup
+
+在 V1 之後，對每個 conflict trace 做多數決 → 清除 minority identity。
+
+**結果**：移除 836 個污染臉
+- ✅ trace-level 衝突降為 0
+- ❌ Bernard Musson 仍保留 847（trace 內獨佔）
+- ❌ 無法解決 generic seed 的根本問題
+
+### V3：雙階段 Centroid Matching
+
+設計：
+
+```
+Phase 1: Seed matching @ 0.55 (stricter) → 乾淨 base set
+Phase 2: Centroid matching @ 0.45 → 用電影內平均臉擴張 recall
+```
+
+**結果**：27,375 匹配 (43.9%) → trace cleanup → 24,286 (39.0%)
+- ✅ Audrey 11,347 (+19%)
+- ✅ Cary Grant 3,107 (+56%)
+- ✅ Walter Matthau 1,200 (+124%) — centroid 修正 seed!
+- ❌ **Bernard Musson 2,903 (+243%)** — centroid 放大 generic seed
+- ❌ **Antonio Passalia 898 (+642%)** — 同上
+
+**教訓**：Generic seed 的 centroid 更泛用。Phase 2 的低 threshold 讓問題惡化。
+
+### V4：雙重驗證 (Dual Gate)
+
+在 V3 的 Phase 2 加上 seed_sim >= 0.40 條件：
+
+```
+centroid_sim >= 0.45 AND seed_sim >= 0.40
+```
+
+**結果**：23,023 匹配 → gap cleanup → trace cleanup → **22,548 (36.2%)**
+- ✅ Bernard / Paul / Antonio / Michel / Clément / Raoul / Roger 仍偏高但 avg_seed_sim 改善
+
+### V5（最終版）：排除 7 個 Generic Identity
+
+核心洞察：**與其過濾假陽性，不如不讓 generic seed 參賽**。
+
+只保留 11 個可靠的 TMDb identity，排除 7 個：
+- 排除：Bernard Musson · Paul Bonifas · Michel Thomass · Antonio Passalia · Clément Harari · Raoul Delfosse · Roger Trapp
+- 保留：Audrey · Cary · James Coburn · Jacques Marin · Walter Matthau · George Kennedy · Dominique Minot · Monte Landis · Stanley Donen · Ned Glass · Louis Viret
+
+流程：
+
+```
+1. Clear all assignments
+2. Phase 1 @ 0.55 — only against 11 identities
+3. Compute centroids
+4. Phase 2 — centroid>=0.45 AND seed>=0.40 (11 centroids)
+5. Ambiguity gate (top2 gap < 0.04 → NULL)
+6. Trace conflict cleanup
+```
+
+**最終結果**：
+
+| Identity | 最終 faces | traces | fpt | avg_sim |
+|----------|:----------:|:------:|:---:|:-------:|
+| Audrey Hepburn | 11,325 | 438 | 25.9 | 0.608 |
+| Cary Grant | **5,101** ≪ 大幅增加 | 269 | 19.0 | 0.497 |
+| James Coburn | 1,508 | 92 | 16.4 | 0.588 |
+| Jacques Marin | 1,438 | 84 | 17.1 | 0.631 |
+| Walter Matthau | 1,250 | 55 | 22.7 | 0.494 |
+| George Kennedy | 869 | 60 | 14.5 | 0.590 |
+| 排除的 7 個 | **0** ✅ | — | — | — |
+| Unassigned | 39,750 | — | — | — |
+
+**Cary Grant 從 3,107→5,101 (+64%)**：之前被 Bernard/Antonio 攔截的臉全部釋放。
+
+## 關鍵教訓
+
+### 1. Generic Seed 辨識
+
+可以透過以下指標辨識 generic seed：
+- **Phase 1 faces / traces 比例低**（< 5 fpt）
+- **被分配到大量短 trace**（表示非連續場景）
+- **avg_seed_sim 偏低但 face count 異常高**
+
+### 2. Propagation 是雙面刃
+
+Rust pipeline 的 propagation 可以增加 recall，但前提是 seed 要夠純。Generic seed + propagation = snowball。
+
+### 3. Seed 數量 vs 品質
+
+> 不是 identity 越多越好。11 個好 seed 勝過 22 個（含 7 個壞的）。
+
+壞 seed 會攔截好 seed 的配對。排除壞 seed 後，那些臉自然會配到正確的人。
+
+### 4. Centroid Matching 的適用條件
+
+Centroid matching 只有在以下情況才有效：
+- Centroid 來自高信賴的 Phase 1 配對（threshold >= 0.55）
+- Centroid 的 Phase 1 base set > 200 faces
+- 搭配 seed_sim dual gate 防止 centroid 飄移
+
+### 5. Trace Context 的重要性
+
+- 一個 trace = 同一人（face tracker 保證）
+- Trace-level conflict cleanup 是必要的後處理
+- 但無法解決 trace 層級以下（同一 trace 內）的 contamination
+
+## 可改進的方向
+
+### 短期
+
+1. **手動檢查 Cary Grant 的 5,101 faces**：avg_sim 0.497 偏低，部分可能是假陽性
+2. **補回已被排除的 identity**：對 Bernard Musson 等用更高 threshold（如 0.60 seed）只看能否 match 到少數高信賴臉
+3. **降低 Ambiguity Gate threshold**：從 0.04 降到 0.03 可再清除一批邊緣配對
+
+### 中期
+
+4. **多 seed 策略**：對每個 identity 用 3-5 張 TMDb 照片，取 centroid 作為 seed
+5. **場景約束**：利用 shot boundary 資訊限制跨場景的 identity 分配
+6. **雙向驗證**：同時用 face→identity 和 identity→trace 兩種方向互相驗證
+
+### 長期
+
+7. **取代 pgvector face-level matching**：改用 trace-level embedding（同一 trace 的所有 face 取平均），再對 trace 做 identity 匹配，減少 single-frame noise
+
+## SQL 核心語法
+
+### pgvector Nearest Neighbor
+
+```sql
+SELECT fd.id, m.identity_id
+FROM eligible fd
+CROSS JOIN LATERAL (
+  SELECT i.id FROM identities i
+  WHERE 1 - (fd.embedding::vector <=> i.face_embedding) >= {threshold}
+  ORDER BY 1 - (fd.embedding::vector <=> i.face_embedding) DESC
+  LIMIT 1
+) m
+```
+
+### Centroid 計算
+
+```sql
+CREATE TABLE centroids AS
+SELECT identity_id, AVG(embedding::vector) as centroid
+FROM face_detections
+WHERE file_uuid = '{uuid}' AND identity_id IS NOT NULL
+GROUP BY identity_id
+HAVING COUNT(*) >= 5;
+```
+
+### Trace Conflict Cleanup
+
+```sql
+WITH conflict_traces AS (
+  SELECT trace_id FROM face_detections
+  WHERE file_uuid = '{uuid}' AND identity_id IS NOT NULL
+  GROUP BY trace_id HAVING COUNT(DISTINCT identity_id) > 1
+),
+trace_majority AS (
+  SELECT DISTINCT ON (ct.trace_id) ct.trace_id, fd.identity_id
+  FROM conflict_traces ct
+  JOIN face_detections fd ON fd.trace_id = ct.trace_id
+  WHERE fd.file_uuid = '{uuid}' AND fd.identity_id IS NOT NULL
+  GROUP BY ct.trace_id, fd.identity_id
+  ORDER BY ct.trace_id, COUNT(*) DESC
+)
+UPDATE face_detections fd SET identity_id = NULL
+FROM trace_majority tm
+WHERE fd.file_uuid = '{uuid}' AND fd.trace_id = tm.trace_id
+  AND fd.identity_id != tm.identity_id;
+```
+
+### Ambiguity Gate
+
+```sql
+WITH all_sims AS (
+  SELECT fd.id, c.identity_id,
+         1 - (fd.embedding::vector <=> c.centroid) as sim
+  FROM face_detections fd
+  CROSS JOIN centroids c
+  WHERE fd.file_uuid = '{uuid}' AND fd.identity_id IS NOT NULL
+),
+ranked AS (
+  SELECT id, sim, LEAD(sim) OVER (PARTITION BY id ORDER BY sim DESC) as sim2
+  FROM all_sims
+),
+ambiguous AS (
+  SELECT id FROM ranked
+  WHERE rn = 1 AND sim - COALESCE(sim2, 0) < 0.04
+)
+UPDATE face_detections fd SET identity_id = NULL
+FROM ambiguous a WHERE fd.id = a.id;
+```
+
+## 資料庫備份
+
+每次關鍵操作都有備份：
+
+| Backup | Rows | 內容 |
+|--------|:----:|:------|
+| `fd_charade_bak` | 62,298 | 原始無 identity 的 Charade face_detections |
+| `fd_state_bak2` | 24,286 | V5 執行前的 assignment snapshot |
+| `wp_snippets_backup_20260601_11940.sql` | — | WordPress snippets 備份 |
@@ -0,0 +1,45 @@
+# 槍枝檢測模型 Charade 評估報告
+
+**Date:** 2026-05-10
+**模型:** YOLOv8n fine-tuned on Roboflow gun dataset (905 images)
+**Classes:** grenade (0), knife (1), pistol (2), rifle (3)
+**Weights:** `models/gun/gun_detector/weights/best.pt` (6MB)
+
+## 訓練
+
+- **Dataset**: 905 images, Roboflow CC BY 4.0
+- **Validation mAP50**: 0.813
+- **問題**: 訓練資料全為近距離槍枝特寫，與 Charade 電影中的中遠景畫面分布完全不同
+
+## Charade 測試結果
+
+### 系統掃描（24 取樣點 @ 每 300s）
+
+| 時間 | 類別 | 信心 | 判定 |
+|------|------|------|------|
+| t=600s | pistol×2, rifle | 0.16–0.30 | ❌ FP |
+| t=1200s | knife | 0.37 | ❌ FP |
+| t=1800s | pistol | 0.19 | ❌ FP |
+| t=2400s | knife | 0.18 | ❌ FP |
+| t=3000s | pistol | 0.16 | ❌ FP |
+| t=5400s | pistol×2 | 0.45, 0.17 | ❌ FP（郵票被誤判為槍） |
+| t=6600s | grenade | 0.22 | ❌ FP |
+
+### 密集掃描（ASR trigger）
+
+在 ASR dialogue 提到 "gun" 的時間點附近跑 gun detector，找到 5 個 pistol/gun 觸發（3188s / 5461s / 6309s / 6377s / 6479s），confidence 0.300-0.387。
+
+**結果：全部為 false positive。** 訓練效果非常不好 — 模型在電影中遠景畫面完全失效。
+
+## 結論
+
+1. 訓練資料與推論場景 distribution mismatch 嚴重
+2. 905 張 Roboflow 近距離特寫 → Charade 的中遠景手持/部分遮蔽槍枝 → 模型無法泛化
+3. 建議：收集電影真實槍枝畫面（200-500 張動作片片段）重新訓練
+4. 在此之前，槍枝搜尋只能靠 ASR dialogue keyword matching + 人工確認
+
+## 相關檔案
+
+- `models/gun/gun_detector/weights/best.pt` — 模型權重（效果不佳）
+- `output_dev/gun_detections/` — 偵測截圖（全部 FP）
+- `scripts/object_search_agent.py` — 整合搜尋 agent（gun detector 偵測結果僅供參考）
@@ -0,0 +1,73 @@
+# Gun Detector Scan Report — YOLOv8n on Charade (1963)
+
+**Date:** 2026-05-10
+**Model:** `models/gun/gun_detector/weights/best.pt`
+**Base:** YOLOv8n fine-tuned on Roboflow gun dataset (905 images)
+**Classes:** grenade, knife, pistol, rifle
+**Scan script:** `scripts/gun_detector_scan.py`
+
+## Scan Method
+
+- **121 scan points**: 2 ASR "gun" mentions + 114 fixed intervals (60s) + 5 original hit timestamps
+- **Per point**: scan ±30 frames at every 3rd frame = ~20 frames per point
+- **Total frames processed**: ~2,420
+- **Runtime**: ~2 min
+
+## Results
+
+| Class | Detections | Top Confidence |
+|-------|-----------|---------------|
+| pistol | **82** | 0.887 |
+| rifle | 55 | 0.822 |
+| grenade | 35 | 0.797 |
+| knife | 38 | 0.810 |
+| **Total** | **210** (after dedup) | — |
+
+## Original 5 Pistol Timestamps
+
+| Timestamp | Original | This Scan | Delta |
+|-----------|----------|-----------|-------|
+| 3188s (53:08) | pistol 0.387 | ✅ **0.474** | +22% |
+| 5461s (91:01) | pistol 0.355 | ✅ **0.346** | −3% |
+| 6309s (1:45:09) | pistol 0.374 | ❌ Not found | — |
+| 6377s (1:46:17) | gun 0.316 | ✅ **0.757** | +140% |
+| 6479s (1:47:59) | pistol 0.300 | ✅ **0.815** | +172% |
+
+## Top Pistol Detections
+
+| Time | Confidence | Image |
+|------|-----------|-------|
+| 84:00 (5040s) | **0.887** | `5040s_pistol_0.887.jpg` |
+| 90:00 (5400s) | **0.816** | `5400s_pistol_0.816.jpg` |
+| 108:00 (6480s) | **0.815** | `6480s_pistol_0.815.jpg` |
+| 48:59 (2939s) | **0.805** | `2939s_pistol_0.805.jpg` |
+| 53:07 (3187s) | **0.474** | `3187s_pistol_0.474.jpg` |
+| 91:00 (5459s) | **0.346** | `5459s_pistol_0.346.jpg` |
+
+## Analysis
+
+### Model Performance
+
+Compared to the original evaluation (May 7, 24 sample points, all FP):
+
+- This scan found **significantly more detections** (210 vs 7)
+- Confidence values are **much higher** (0.887 vs 0.45 max)
+- 4/5 original pistol timestamps recovered
+
+### Cautions
+
+1. **Training data mismatch**: Model was trained on 905 close-up gun photos, NOT movie frames. High confidence ≠ real gun.
+2. **Stamp false positive confirmed**: t=5400s (identified in original eval as stamp → pistol) continues to fire at 0.816
+3. **Pattern suggests overconfidence**: Many detections at regular intervals (every 60s, same objects) suggest the model is detecting non-gun objects with high confidence
+
+### Verified Findings
+
+The original 5 pistol images from the gun_detections/ directory (3188s, 5461s, 6309s, 6377s, 6479s) were all produced by the same YOLOv8n model. The user previously stated that none of these have been confirmed as real guns.
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `output_dev/gun_detections/gun_detections.json` | All 210 deduped detections |
+| `output_dev/gun_detections/*.jpg` | Annotated screenshots (one per detection) |
+| `scripts/gun_detector_scan.py` | Scan script (reproducible) |
@@ -0,0 +1,50 @@
+# M4 / M5 協作協議
+
+## 核心原則：檔案是 source of truth
+
+所有 processor 的產出是 `{uuid}.{processor}.json` 檔案。
+**檔案存在 = 處理完成**，優先於 DB 或 Redis 的任何狀態記錄。
+
+## 絕對禁止
+
+### 1. 不可刪除已存在的輸出檔
+- 任何 `{uuid}.{processor}.*` 檔案，無論是 `.json`、`.json.tmp`、`.json.partial`、`.json.err`
+- 一律不允許 `rm`、`unlink`、`delete`
+- 唯一例外：明確的人工指令 `rm` / `Delete this file`
+
+### 2. 不可覆蓋已存在的輸出檔
+- 重新執行 processor 前，必須先 **copy（非 rename）** 加上時間戳備份
+- 備份命名：`{uuid}.{processor}.{timestamp}.{original_extension}`
+- 若備份名已存在，跳過（不覆蓋不 counter）
+- 原檔保留不動
+
+### 3. 不可跨域操作
+- M4 只能在 M4 機器（Mac Mini）上操作
+- M5 只能在 M5 機器（MacBook Pro）上操作
+- 禁止任何跨機器的檔案操作或 cleanup
+
+## 重跑 processor 的正確流程
+
+1. Worker 檢查 `{uuid}.{processor}.json` 是否存在
+2. **存在 → 跳過**（無論 DB/Redis 狀態）
+3. 不存在 → copy 備份既有 `{uuid}.{processor}.*` → 執行 processor
+4. Processor 輸出寫入 `.tmp` → 完成後 rename 為 `.json`
+
+## 例外處理
+
+| 狀態 | 行為 |
+|------|------|
+| `.json` 存在 | 跳過，視為完成 |
+| `.json.tmp` 存在（無 `.json`） | 視為未完成，備份後重跑 |
+| `.json.partial` 存在（無 `.json`） | 視為未完成，備份後重跑 |
+| `.json.err` 存在（無 `.json`） | 視為未完成，備份後重跑 |
+| Process 被 kill（SIGKILL） | partial 存為 `.json.partial`（非 `.json`） |
+
+## 違規後果
+
+2026-05-09 事故：M4 release 打包未含 .json → 跨域操作 → M5 cleanup 誤刪 asr.json
+→ 導致 ASR 需重跑（完整電影約 1.5hr）
+→ YOLO 需重跑
+→ 損失已完成的 pipeline 進度
+
+此類違規不可再發生。
@@ -0,0 +1,31 @@
+# M4 Release Incident — 2026-05-09
+
+## Summary
+
+M4 在進行 release 打包作業時，未依照計畫包含 output `.json` 檔案，僅在 database 中保留 records。此外 M4 違反操作邊界進入 M5 管轄範圍，M5 執行 cleanup 時將已完成的 `asr.json` 一併刪除。
+
+## Impact
+
+| 檔案 | 狀態 | 說明 |
+|------|------|------|
+| `{uuid}.asr.json` | ❌ 遺失 | 已完成的 ASR 輸出被 M5 cleanup 誤刪 |
+| `{uuid}.yolo.json` | ❌ 損毀 | JSON parse error，需重跑 |
+| DB records | ⚠️ 不一致 | processor_results 狀態與實際檔案不符 |
+
+## Root Cause
+
+1. **M4 release 打包遺漏**: Release 流程未將 `.json` 輸出檔納入打包範圍，只保留了 DB。
+2. **M4 越界操作**: M4 在 M5 的目錄/範圍內執行操作，違反開發隔離原則。
+3. **M5 cleanup 誤刪**: M5 的 cleanup 機制未預期 M4 的產出，將 `asr.json` 視為無用檔案清除。
+
+## 處理
+
+- ASR: 重跑中（asr_processor.py，完整電影約 6780s）
+- YOLO: 重跑中（yolo_processor.py）
+- 已修改 worker 邏輯：開機後以 `.json` 檔案存在為 source of truth，不再僅依賴 DB/Redis 狀態
+
+## 預防措施
+
+- Release 流程需明確定義 deliverables 包含 `.json` 檔案
+- M4/M5 操作邊界需嚴格遵守，禁止跨域操作
+- Cleanup 機制應先確認檔案是否為有效 processor output
@@ -0,0 +1,77 @@
+# M4 vs M5 Max Comparison
+
+## Hardware
+
+| Spec | M4 (Mac Mini) | M5 (MacBook Pro) |
+|------|--------------|-------------------|
+| **Model** | Mac Mini (M4) | MacBook Pro (M5 Max) |
+| **Hostname** | `accusys-Mac-mini-M4-2.local` | `Accusyss-MacBook-Pro.local` |
+| **macOS** | 26.4.1 (Sequoia) | 26.4.1 (Sequoia) |
+| **RAM** | 16 GB | **48 GB** |
+| **CPU Cores** | 10 | **18** |
+| **Disk** | 2TB (est.) | **1.8TB (12GB used, 97% free)** |
+| **Network** | 192.168.110.210, 192.168.110.200 | 192.168.110.201, 192.168.31.182 |
+
+## Installed Services
+
+| Service | M4 | M5 |
+|---------|-----|------|
+| **PostgreSQL** | 18.1 (Homebrew) | **18.3 (Source build)** |
+| **pgvector** | Homebrew | **0.8.2 (Source build)** |
+| **Redis** | 8.4.0 (Homebrew) | **7.4.3 (Source build)** |
+| **Qdrant** | Homebrew/pre-built | **1.17.1 (Source build, `cargo`)** |
+| **MongoDB** | Homebrew | 8.2.7 (Homebrew) |
+| **MariaDB** | ✗ via brew | **12.2.2 (Homebrew, for WordPress)** |
+| **PHP** | ✗ via brew | **8.5.5 (Homebrew, WordPress ext. ✅)** |
+| **SFTPGo** | Pre-built binary | **2.7.1 (Source build, patched dep)** |
+| **FFmpeg** | 8.1 (Homebrew) | **8.1.1 (Homebrew)** |
+| **OpenCode** | 1.14.39 | **1.14.39** |
+| **Gemma4 LLM** | ✗ (not enough RAM) | **31B Q5_K_M @ 8081** |
+
+## Build Approach
+
+| Aspect | M4 | M5 |
+|--------|-----|-----|
+| **PostgreSQL** | `brew install postgresql@18` | `./configure && make && make install` |
+| **Redis** | `brew install redis` | `make && cp src/redis-server ~/redis/bin/` |
+| **Qdrant** | `brew install qdrant` | `cargo build --release --bin qdrant` (from GitHub) |
+| **SFTPGo** | `brew install sftpgo` | `git clone && go build` (patched `go-m1cpu`) |
+| **Philosophy** | Mixed (Homebrew + binary) | **Source-first** (GitHub source, checksums recorded) |
+
+## Data Migration (M4 → M5)
+
+| Data | Size | Status |
+|------|------|--------|
+| **Database (dev schema)** | 837MB dump | ✅ Restored (16 tables) |
+| **Video file** | 2.2GB | ✅ Transferred |
+| **output_dev JSON** | 2.9GB (462 files) | ✅ Transferred |
+| **output JSON** | 65MB (2523 files) | ✅ Transferred |
+| **Configs** | small | ✅ Transferred |
+
+## Database Row Counts (M5)
+
+| Table | Rows |
+|-------|------|
+| `pre_chunks` | 494,339 |
+| `face_detections` | 6,211 |
+| `tkg_nodes` | 2,414 |
+| `identity_bindings` | 2,347 |
+| `tkg_edges` | 1,320 |
+
+## Key Differences
+
+### 1. RAM (16GB vs 48GB)
+- **M4 (16GB)**: Cannot run Gemma4 31B LLM locally. Memory pressure during concurrent pipeline processing.
+- **M5 (48GB)**: Can run Gemma4 31B (Q5_K_M, ~20GB) + databases + playground simultaneously. 
+
+### 2. Build Philosophy
+- **M4**: Quick setup via Homebrew bottles (pre-compiled).
+- **M5**: **Source-first** — every service built from GitHub/official source. `SHA256` checksums recorded. Dependencies patched as needed (SFTPGo `go-m1cpu`).
+
+### 3. Unique M5 Services
+- **MariaDB + PHP**: Installed for WordPress/marcom portal development.
+- **Gemma4 LLM**: Running on port 8081, accessible for RAG/identity clustering.
+- **OpenCode**: Configured with Gemma4 provider for AI-assisted development.
+
+### 4. Data Freshness
+- M5 is a **snapshot** of M4's state at 2026-05-06 (commit `bac6c2d`). Changes made on M4 after sync date must be re-synced.
@@ -0,0 +1,259 @@
+# M5 Dev Environment Setup Log
+
+**Machine**: M5 MacBook Pro (MacOS 26.4.1, Apple M5 Max, 48GB)
+**User**: accusys (admin group, sudo with password)
+**Date**: 2026-05-06
+**Setup by**: OpenCode
+
+---
+
+## 1. Source Code
+
+| Item | Detail |
+|------|--------|
+| Repo | `https://gitea.momentry.ddns.net/warren/momentry_core.git` |
+| Branch | `main` |
+| Commit | `bac6c2d` (feat: identity clustering V3.0) |
+| Sync method | rsync from M4 (192.168.110.210) |
+| Path | `~/momentry_core_0.1/` |
+
+---
+
+## 2. Installed Services
+
+### 2.1 PostgreSQL 18.3
+
+| Field | Value |
+|-------|-------|
+| **Source** | [https://ftp.postgresql.org/pub/source/v18.3/postgresql-18.3.tar.gz](https://ftp.postgresql.org/pub/source/v18.3/postgresql-18.3.tar.gz) |
+| **GitHub** | [https://github.com/postgresql/postgresql](https://github.com/postgresql/postgresql) |
+| **Build method** | Manual `./configure && make && make install` |
+| **Prefix** | `~/pgsql/18.3/` |
+| **Data dir** | `~/pgsql/data/` |
+| **Port** | 5432 |
+| **Version** | PostgreSQL 18.3 |
+| **SHA256** | `ab04939aafdb9e8487c2f13dda91e6a4a7f4c83368f5bedd23ee4ad1fda64afb` |
+| **Start command** | `pg_ctl -D ~/pgsql/data -l ~/pgsql/pg.log start` |
+| **Configure flags** | `--prefix=$HOME/pgsql/18.3 --with-uuid=e2fs --with-icu --with-openssl` |
+| **Build date** | 2026-05-06 |
+| **Notes** | `--with-uuid=e2fs` used (requires Homebrew `e2fsprogs`). macOS built-in UUID not detected by configure. |
+
+### 2.2 pgvector 0.8.2
+
+| Field | Value |
+|-------|-------|
+| **Source** | [https://github.com/pgvector/pgvector](https://github.com/pgvector/pgvector) |
+| **Version** | v0.8.2 |
+| **Build method** | `git clone && make && make install` |
+| **SHA256** | `65dec31ec078d60ee9d8e1dac59be8a41edf8c79bf380cd0093691b0afd257a8` |
+| **Build date** | 2026-05-06 |
+| **Notes** | Built against PostgreSQL 18.3 source installation |
+
+### 2.3 Redis 7.4.3
+
+| Field | Value |
+|-------|-------|
+| **Source** | [https://github.com/redis/redis/archive/refs/tags/7.4.3.tar.gz](https://github.com/redis/redis/archive/refs/tags/7.4.3.tar.gz) |
+| **GitHub** | [https://github.com/redis/redis](https://github.com/redis/redis) |
+| **Version** | 7.4.3 |
+| **Build method** | `make -j$(sysctl -n hw.ncpu)` |
+| **Binary path** | `~/redis/bin/redis-server` |
+| **Port** | 6379 |
+| **SHA256** | `87b6a9ea145c56c1ace724acbb9906b7be4abddd44041545adf44ce9f4d0a615` |
+| **Start command** | `redis-server --daemonize yes --port 6379` |
+| **Build date** | 2026-05-06 |
+
+### 2.4 Qdrant 1.17.1
+
+| Field | Value |
+|-------|-------|
+| **Source** | [https://github.com/qdrant/qdrant.git](https://github.com/qdrant/qdrant.git) |
+| **Version** | v1.17.1 |
+| **Build method** | `cargo build --release --bin qdrant` |
+| **Binary path** | `~/momentry_core_0.1/services/qdrant/target/release/qdrant` |
+| **Storage dir** | `~/qdrant_storage` |
+| **Port** | 6333 (HTTP), 6334 (gRPC) |
+| **SHA256** | `8f8aa63840a0f948b43f9b95f784ace69595892de5dc581bb66bd62fd86d6c66` |
+| **Build date** | 2026-05-06 |
+| **Config** | `~/qdrant_config.yaml` |
+| **Start command** | `qdrant --config-path ~/qdrant_config.yaml &` |
+| **Build deps** | protoc (Homebrew protobuf), cmake |
+
+### 2.5 MongoDB 8.2.7
+
+| Field | Value |
+|-------|-------|
+| **Source** | Homebrew `mongodb/brew/mongodb-community` |
+| **Version** | 8.2.7 |
+| **Port** | 27017 |
+| **Start command** | `brew services start mongodb/brew/mongodb-community` |
+| **Install date** | 2026-05-06 |
+
+### 2.6 MariaDB 12.2.2
+
+| Field | Value |
+|-------|-------|
+| **Source** | Homebrew `mariadb` |
+| **Version** | 12.2.2-MariaDB |
+| **Port** | 3306 |
+| **Start command** | `brew services start mariadb` |
+| **Install date** | 2026-05-06 |
+
+### 2.7 PHP 8.5.5
+
+| Field | Value |
+|-------|-------|
+| **Source** | Homebrew `php` |
+| **Version** | 8.5.5 |
+| **WordPress extensions** | mysqli, pdo_mysql, gd, xml, mbstring, curl, zip, json, intl, bcmath, gmp, openssl |
+| **Start command** | `brew services start php` |
+| **Install date** | 2026-05-06 |
+
+### 2.8 FFmpeg / FFprobe 8.1.1
+
+| Field | Value |
+|-------|-------|
+| **Source** | Homebrew `ffmpeg` |
+| **Version** | 8.1.1 |
+| **SHA256** | `00d01197255300c02122c783dd0126a9e7f47d6c6a19faafae2e6610efd071d3` |
+| **Install date** | 2026-05-06 |
+
+### 2.9 SFTPGo 2.7.1
+
+| Field | Value |
+|-------|-------|
+| **Source** | [https://github.com/drakkan/sftpgo.git](https://github.com/drakkan/sftpgo.git) |
+| **Version** | v2.7.1 |
+| **Build method** | `git clone && go build -o sftpgo_bin ./` |
+| **Binary path** | `~/momentry_core_0.1/services/sftpgo_bin` |
+| **SHA256** | `550b6653f8f2cd7c58620e128e85be571a6702c79cf374824ad9b420ca039db1` |
+| **Build date** | 2026-05-06 |
+| **Patch** | Upgraded `go-m1cpu` from v0.2.0 → v0.2.1 to fix SIGTRAP crash on macOS 26.4.1 |
+| **Notes** | Pre-built binary from GitHub releases crashed with `go-m1cpu` cgo compatibility issue. Source build with patched dependency resolved. |
+
+### 2.10 OpenCode 1.14.39
+
+| Field | Value |
+|-------|-------|
+| **Source** | [https://opencode.ai/install](https://opencode.ai/install) |
+| **Version** | 1.14.39 |
+| **Binary path** | `~/.opencode/bin/opencode` |
+| **SHA256** | `def4a786c257bd6a965e46a2b069802496681b9eea20261d7d1b55629af3d1da` |
+| **Install date** | 2026-05-06 |
+
+### 2.11 Python 3.11 + Packages
+
+| Field | Value |
+|-------|-------|
+| **Source** | Homebrew `python@3.11` |
+| **Version** | 3.11.15 |
+| **Path** | `/opt/homebrew/bin/python3.11` |
+| **Key packages** | coremltools, opencv-python, numpy, psycopg2, torch, transformers, whisperx, etc. |
+| **Requirements** | `~/momentry_core_0.1/requirements.txt` |
+| **Install date** | 2026-05-06 |
+| **FaceNet model** | `models/facenet512.mlpackage` (512D CoreML, loads OK) |
+
+### 2.12 Build Tools
+
+| Tool | Version | Source |
+|------|---------|--------|
+| Rust | 1.95.0 | rustup (pre-installed) |
+| Go | 1.26.2 | Homebrew `go` |
+| cmake | 4.3.2 | Homebrew `cmake` |
+| pkg-config | - | Homebrew `pkg-config` |
+
+---
+
+## 3. Momentry Configuration
+
+### 3.1 Environment Files
+
+| File | Purpose |
+|------|---------|
+| `.env` | Production config (port 3002) |
+| `.env.development` | Development config (port 3003) |
+
+Key settings:
+- `DATABASE_URL=postgres://accusys@localhost:5432/momentry`
+- `REDIS_URL=redis://:accusys@localhost:6379`
+- `DATABASE_SCHEMA=dev`
+- `MOMENTRY_SERVER_PORT=3003` (dev) / `3002` (prod)
+- `MOMENTRY_API_KEY=muser_test_apikey`
+- `MOMENTRY_PYTHON_PATH=/opt/homebrew/bin/python3.11`
+- `MOMENTRY_SCRIPTS_DIR=/Users/accusys/momentry_core_0.1/scripts`
+
+### 3.2 Database Tables Created
+
+| Table | Created by |
+|-------|-----------|
+| `dev.videos` | Manual SQL |
+| `dev.chunks` | Manual SQL |
+| `dev.monitor_jobs` | Manual SQL |
+| `dev.processor_results` | Manual SQL |
+| `dev.talents` | Manual SQL |
+| `dev.identity_bindings` | Manual SQL |
+| `dev.api_keys` | Manual SQL |
+
+### 3.3 API Key
+
+- Key: `muser_test_apikey`
+- Hash (SHA256): `3f2fa16e44ff74267786fdf979b9c33dac0cad515282e4937a0776756a61e821`
+- Status: active
+
+---
+
+## 4. Running Services (Verified)
+
+| Service | Port | Status |
+|---------|------|--------|
+| PostgreSQL | 5432 | ✅ |
+| Redis | 6379 | ✅ |
+| Qdrant | 6333 | ✅ |
+| MongoDB | 27017 | ✅ |
+| MariaDB | 3306 | ✅ |
+| Momentry Playground | 3003 | ✅ |
+| Gemma4 LLM | 8081 | ✅ (pre-installed) |
+
+---
+
+## 5. PATH Configuration
+
+`.zshrc`:
+```zsh
+export PATH="/opt/homebrew/bin:/opt/homebrew/opt/postgresql@18/bin:$HOME/.opencode/bin:$PATH"
+```
+
+Also available:
+- `$HOME/pgsql/18.3/bin` — source-built PostgreSQL tools
+- `$HOME/redis/bin` — source-built Redis
+- `$HOME/.cargo/bin` — Rust/Cargo tools
+
+---
+
+## 6. M5 End-to-End Test Results (Charade Full Movie)
+
+Run date: 2026-05-06 20:38-20:57
+
+| Stage | Time | Result |
+|-------|------|--------|
+| **Swift_face** (Vision ANE detection) | 867s (14.5 min) | 3999 frames (interval=30) |
+| **CoreML FaceNet** (512D embedding) | 271s (4.5 min) | 6186 face embeddings |
+| **Face tracker** (scene-cut aware) | ~30s | 1538 traces |
+| **DB store** | ~5s | 6186 detections in `dev.face_detections` |
+| **Total** | ~19 min | 1 long video (412k frames, 2.2GB) |
+
+**Scene-cut effect**: 1538 traces (vs 379 without scene-cut reset in M4 data). Scene boundaries correctly split traces.
+
+**Models used**:
+- Face detection: Apple Vision (ANE) via `swift_face`
+- Face embedding: CoreML FaceNet 512D via `facenet512.mlpackage`
+- Text embedding: `mxbai-embed-large` (1024D) via Ollama
+
+---
+
+## 7. Known Issues
+
+1. **Momentry API status `degraded`**: Expected on fresh setup. Some cache/processing dependencies not fully initialized.
+2. **SFTPGo startup requires config**: Binary built from source, needs config file for production use.
+3. **Migration scripts not all run**: Base tables created manually. Some migration files (017+) reference tables/columns that need verification.
+4. **OpenCode config**: `~/.config/opencode/config.json` not yet configured for M5 Gemma4 provider.
@@ -0,0 +1,94 @@
+# Non-Human Sound Detection — Tool Selection Report
+
+**Date:** 2026-05-10
+**Movie:** Charade (1963), 113 min
+**Audio:** 16kHz mono WAV
+**Goal:** Detect non-human sound events (gunshots, impacts, doors, music, etc.)
+
+## Tested Approaches
+
+### Approach A: AST AudioSet (HuggingFace)
+
+| Item | Detail |
+|------|--------|
+| Model | `MIT/ast-finetuned-audioset-10-10-0.4593` |
+| Method | Audio Spectrogram Transformer, fine-tuned on AudioSet-2M (527 classes) |
+| Dependencies | `transformers`, `torch` ✅ (no torchcodec needed) |
+| Load time | ~1s on M5 |
+| Inference time | ~0.5s per 3-second clip (805k params, float32) |
+| Accuracy | Good — correctly distinguishes speech vs. door vs. music |
+
+**Test results on Charade:**
+
+| Time | Energy-based said | AST AudioSet said | Verdict |
+|------|------------------|-------------------|---------|
+| 0:10 | — | Environmental noise (26%) | Background noise, plausible |
+| 10:32 | Gunshot candidate (43x) | **Speech (76%)** | ✅ AST correct |
+| 57:00 | Gunshot candidate (49x) | **Door (62%) + Slam (5%)** | ✅ AST correct |
+| 65:13 | Gunshot candidate (50x) | **Speech (58%)** | ✅ AST correct |
+| 85:12 | Gunshot candidate (39x) | **Speech (68%)** | ✅ AST correct |
+
+**Conclusion**: Energy-based impulse detection has **100% false positive rate** for gunshot detection. AST AudioSet correctly classifies all candidates as non-gunshot.
+
+### Approach B: Custom Energy + Spectral Features
+
+| Item | Detail |
+|------|--------|
+| Method | RMS energy + spectral centroid + sub-band energy ratios |
+| Speed | ~3s for full 113-min movie (every 10th window) |
+| Accuracy | Poor — cannot distinguish gunshot from speech, door, music |
+| Result | 1 "gunshot_candidate" from 453 test windows; all false positives on verification |
+
+**Conclusion**: Useful as a **coarse pre-filter** (Stage 1), not as a standalone classifier.
+
+## Two-Stage Design
+
+```
+Stage 1 (Energy filter, ~1 min):
+  Full audio → sliding window RMS + centroid → ~200 candidate windows
+                    |
+                    v
+Stage 2 (AST classifier, ~2 min):
+  Extract 3-sec audio for each candidate → AST AudioSet classification
+                    |
+                    v
+  Non-speech events: gunshot, explosion, door slam, music, etc.
+```
+
+Estimated processing: ~3 min for full movie (vs. 75 min for full AST scan)
+
+## Key AudioSet Classes Relevant to Charade
+
+| Class | AudioSet ID | Relevance |
+|-------|-------------|-----------|
+| Gunshot, gunfire | 402 | **Primary target** |
+| Explosion | 400 | Hand grenade in plot |
+| Door slams | 404 | Scenes at hotel, apartment |
+| Music | 130-133 | Background score |
+| Speech | 0-3 | Already handled by ASR |
+| Vehicle | 100-110 | Car sounds in Paris chase |
+| Glass break | 424 | Window breaking scene |
+
+## Actor-voice gender mismatches (resolved by fine-grained ASRX)
+
+During the speaker mapping work, 20 segments where the old face→TMDb assignment said "Audrey Hepburn" but the new ASRX voice embedding clearly said "MALE". These segments were verified via video clips and confirmed to be scenes where:
+
+1. A male speaker (Cary Grant or other) is speaking while Audrey Hepburn's face is on screen
+2. The old pipeline incorrectly assigned the speaker name based on face identity
+3. The fine-grained sliding window approach correctly resolves these
+
+The 20 segments were from SPEAKER_5 (10 segs) and SPEAKER_9 (10 segs), both of which mapped to MALE voice clusters. These were re-assigned to "Cary Grant" or "Unknown" as appropriate.
+
+## Recommendations
+
+| Approach | Speed | Accuracy | Best for |
+|----------|-------|----------|----------|
+| Energy pre-filter | ✅ 1 min | ❌ Low | Stage 1: candidate selection |
+| AST AudioSet | ⚠️ 2 min | ✅ High | Stage 2: event classification |
+| Full AST scan | ❌ 75 min | ✅ High | N/A — two-stage is better |
+
+**Design**: Two-stage pipeline: energy pre-filter → AST classifier
+**Implementation path**:
+1. Write `scripts/non_human_sound_detector.py` with the two-stage design
+2. Output `{uuid}.sound_events.json` with typed events
+3. Integrate into the sound_event_detector framework
@@ -0,0 +1,150 @@
+# Phase 1 Completion Report — v2 (fine-grained ASRX)
+
+**File**: Charade (1963) Cary Grant & Audrey Hepburn
+**UUID**: `aeed71342a899fe4b4c57b7d41bcb692`
+**Date**: 2026-05-10
+**System**: M5 (MacBook Pro, 48GB, Apple Silicon)
+
+---
+
+## 1. Processor Outputs
+
+| File | Size | Description |
+|------|------|-------------|
+| `asr.json` | 413KB | 3,417 segments, full movie coverage (Whisper small) |
+| `asrx.json` | **18MB** | **4,188 segments** (fine-grained, ECAPA-TDNN) |
+| `asrx_fine.json` | 45MB | 4,188 fine segments + voice embeddings (intermediate) |
+| `cut.json` | 329KB | 2,260 scenes |
+| `yolo.json` | 181MB | 169,625 frames with object detections |
+| `face.json` | **106MB** | 4,550 frames, 5,910 faces @ 8Hz (CoreML 512D) |
+| `face_traced.json` | 110MB | Traced faces with 423 identity traces |
+| `lip.json` | 492KB | Lip openness analysis |
+| `ocr.json` | 277KB | 606 OCR frames |
+| `pose.json` | 26MB | 4,211 pose frames |
+| `scene.json` | 403B | Scene classification |
+
+## 2. Pipeline 8-Stage Checklist
+
+| Stage | Status | Detail |
+|-------|--------|--------|
+| ASR | ✅ | 3,417 segments, last end 6,773s (100%) |
+| ASRX | ✅ | **4,188 segments** (fine-grained, 10→3 speakers mapped) |
+| Sentence Chunks | ✅ | **4,188 sentence chunks** with yolo_objects + face_ids |
+| Vectorization | ✅ | 4,188 Qdrant (768D), all 3 collections updated |
+| Face Trace | ✅ | 423 traces, 11,820 detections @ 8Hz |
+| TKG Graph | ✅ | 498 nodes, 1,617 edges |
+| Trace Chunks | ✅ | 423 trace chunks |
+| Phase 1 Release | ✅ | 3.0GB package |
+
+## 3. Speaker Identification
+
+### ASRX Enhancement (3417 → 4188 segments)
+
+The original Whisper ASR merges rapid back-and-forth dialogue into single segments. A sliding-window ECAPA-TDNN approach was developed to detect speaker change points within each ASR segment:
+
+1. **Sliding window**: 1.5s window, 0.75s stride across full audio
+2. **ECAPA-TDNN 192D embedding** per window
+3. **Classification** against reference centroids (Cary Grant, Audrey Hepburn, Unknown)
+4. **Majority-vote smoothing** over 3 adjacent windows
+5. **Change point detection** where classified speaker changes
+6. **Split** original ASR segment at each change point
+
+**Result**: 3,417 → **4,188 segments** (+771, +22.6%). Validated via gender classification (ECAPA-TDNN → 92.3% agreement with character identity).
+
+### Speaker Mapping (Centroid-based)
+
+| Speaker ID | Name | Segments | Duration | Voice Gender |
+|------------|------|----------|----------|-------------|
+| SPEAKER_0 | Audrey Hepburn | 1,658 | 2,786s | FEMALE |
+| SPEAKER_1 | Cary Grant | 2,033 | 3,962s | MALE |
+| SPEAKER_2 | Unknown (minor) | 497 | 806s | MIXED |
+
+Method: Reference centroids built from 3,107 known segments (1,420 Cary + 1,689 Audrey). Each fine segment classified by cosine similarity to nearest centroid. No cross-contamination between speaker clusters.
+
+### Gender Validation
+
+Two small clusters (SPEAKER_5: 10 segs, SPEAKER_9: 10 segs) initially showed MALE voice → Audrey assignment. Video clip verification confirmed these are segments where a male voice speaks while Audrey is on screen (old face-based matching was incorrect). The fine-grained segmentation correctly resolves these.
+
+## 4. Sentence Chunks — Full Migration
+
+All 4,188 fine segments were written to `dev.chunks` with complete data per chunk:
+
+| Chunk Field | Value | Source |
+|-------------|-------|--------|
+| `start_time`/`end_time` | Fine segment boundaries | `asrx_fine.json` |
+| `start_frame`/`end_frame` | time × 25fps | Calculated |
+| `content` | `{data: {text, text_normalized}, rule: rule_1}` | ASR text |
+| `metadata.yolo_objects` | Dedup class names in frame range | `pre_chunks(yolo)` |
+| `metadata.face_ids` | Trace IDs in frame range | `face_detections` |
+| `metadata.speaker_name` | Centroid-matched identity | `asrx_fine.json` |
+
+- 4,158/4,188 chunks have YOLO objects (avg 3-5 object classes)
+- 398/4,188 chunks have face IDs (face data covers first ~12 min only)
+
+### Parent/Story Chunks
+
+| Metric | Before (v1) | After (v2) |
+|--------|-------------|------------|
+| Children per parent | 15 (fixed) | 15 (fixed) |
+| Total parents | 228 | **280** |
+| LLM summaries | 228 (Gemma4) | **280** (Gemma4, regenerated) |
+| Qdrant stories | 456 pts | **560 pts** |
+
+## 5. Qdrant Vector Collections
+
+| Collection | Dims | Points | Content | Status |
+|-----------|------|--------|---------|--------|
+| `momentry_dev_v1` | 768 | **4,188** | Sentence chunk embeddings (EmbeddingGemma) | ✅ |
+| `momentry_dev_stories` | 768 | **560** | 280 dialogue + 280 LLM summary | ✅ |
+| `momentry_dev_faces` | 512 | 5,910 | Face embeddings (8Hz CoreML) | ✅ |
+| `momentry_dev_voice` | 192 | **4,188** | Voice embeddings (ECAPA-TDNN) | ✅ |
+| `sentence_story` | 768 | **4,188** | Sentence template with speaker | ✅ |
+| `sentence_summary` | 768 | **4,188** | Context-aware LLM sentence summary | ✅ |
+
+## 6. ASR Model Selection
+
+A comprehensive benchmark (5 models × 2 VAD settings × 3 test clips = 30 runs) showed:
+
+| Model | Segments | Chars | Runtime | Verdict |
+|-------|----------|-------|---------|---------|
+| tiny | 56 avg | 1,730 | **9.2s** | Most segments, best text capture |
+| **small** | **55 avg** | **1,704** | **17.6s** | **Best balance (current)** |
+| base | 42 avg | 1,751 | 10.1s | Good but fewer segments |
+| medium | 52 avg | 1,627 | 339.6s | Slow, loses text |
+| large-v3 | 20 avg | 1,249 | 68.8s | **Worst**: merges utterances, loses 26% text |
+
+**Conclusion**: Keep `faster-whisper small (VAD 500ms)`. The missing-text problem is not solvable by model size — even tiny captures more text than large-v3. Root cause is Whisper's lack of speaker turn detection in segment boundary logic, which is solved by the sliding-window ASRX approach above.
+
+## 7. Release Package
+
+| Component | Size |
+|-----------|------|
+| `output_json/` | 13 processor files |
+| `chunks.csv` | 3.2MB |
+| `vectors.csv` | 58MB |
+| `identities.csv` | 1MB |
+| `schema.sql` | 30KB |
+| Qdrant snapshots (5 collections) | ~3GB |
+| `RELEASE_INFO.txt` | Metadata |
+| **Total** | **~3.0GB** |
+
+## 8. Key Technical Decisions
+
+| Decision | Rationale |
+|----------|-----------|
+| Sliding window 1.5s/0.75s | Optimal balance: captures turn boundaries without over-splitting |
+| Centroid-based classification | 0.8+ similarity, no retraining needed, 100% consistent |
+| Word-timestamp ASR for text | Re-run with `word_timestamps=True`, 87% coverage; remaining 13% → per-segment ASR fallback |
+| Fixed 15 children/parent | Maintains Phase 1 design consistency |
+| `yolo_objects` dedup | Only class names stored per chunk (not per-frame) |
+| `face_ids` via `trace_id` | `face_id` column is NULL in DB; `trace_id` is the actual identifier |
+| Keep ASR small model | Benchmarked 5 models; larger models lose text, not gain it |
+| `app.run(threaded=True)` | Dashboard v2: single-threaded Flask was blocking on subprocess calls |
+
+## 9. Phase 2 Preparation
+
+Pending for Phase 2:
+- Rule 3 scene chunking (cut-based parent chunks)
+- 5W1H Agent (LLM-generated scene summaries)
+- Full pipeline + 5W1H release packaging
+- Source separation (Demucs/HPSS) for overlapping speech scenarios
@@ -0,0 +1,63 @@
+# Phase 1 Release Checklist
+
+**UUID**: `aeed71342a899fe4b4c57b7d41bcb692`
+**Model**: v2 (fine-grained ASRX, 4,188 segments)
+**Date**: 2026-05-10
+
+## 1. Processor Outputs
+
+- [x] `asr.json` — faster-whisper small, 3,417 segments
+- [x] `asrx.json` — ECAPA-TDNN fine-grained, 4,188 segments
+- [x] `cut.json` — 2,260 scene cuts
+- [x] `yolo.json` — 169,625 frames, object detections
+- [x] `face.json` — 4,550 frames, 5,910 faces @ 8Hz
+- [x] `face_traced.json` — 423 traced identities
+- [x] `lip.json` — Lip openness per ASRX segment
+- [x] `ocr.json` — 606 OCR frames
+- [x] `pose.json` — 4,211 pose frames
+- [x] `scene.json` — Scene classification
+
+## 2. Pipeline Stages
+
+- [x] ASR: 3,417 segments, full movie
+- [x] ASRX: 4,188 segments (fine-grained), 3 speakers
+- [x] Sentence chunks: 4,188 in `dev.chunks`
+- [x] Vectorization: 4,188 in Qdrant `momentry_dev_v1`
+- [x] Face trace: 423 traces, 11,820 detections
+- [x] TKG: 498 nodes, 1,617 edges
+- [x] Trace chunks: 423 in `dev.chunks`
+- [x] All 8 stages passing
+
+## 3. Qdrant Collections
+
+- [x] `momentry_dev_v1` — 4,188 pts, 768D (EmbeddingGemma)
+- [x] `momentry_dev_stories` — 560 pts, 768D (280 dialogue + 280 summary)
+- [x] `momentry_dev_faces` — 5,910 pts, 512D (CoreML FaceNet)
+- [x] `momentry_dev_voice` — 4,188 pts, 192D (ECAPA-TDNN)
+- [x] `sentence_story` — 4,188 pts, 768D (sentence template)
+- [x] `sentence_summary` — 4,188 pts, 768D (context-aware LLM)
+
+## 4. Database (dev.chunks)
+
+- [x] Sentence chunks: 4,188 with speaker_name, speaker_id
+- [x] Story chunks: 280 with LLM summaries
+- [x] Cut chunks: 1,130
+- [x] Trace chunks: 423
+- [x] YOLO objects in metadata: 4,158/4,188
+- [x] Face IDs in metadata: 398/4,188
+- [x] Parent-child relationships set
+
+## 5. Speaker Mapping
+
+- [x] SPEAKER_0 → Audrey Hepburn (1,658 segs, gender FEMALE ✅)
+- [x] SPEAKER_1 → Cary Grant (2,033 segs, gender MALE ✅)
+- [x] SPEAKER_2 → Unknown (497 segs, minor characters)
+- [x] Voice embeddings validated via gender classification
+
+## 6. Release Package
+
+- [x] Phase 1 release packaged at `release/phase1/latest/`
+- [x] Qdrant snapshots for all 5 collections
+- [x] `chunks.csv`, `vectors.csv`, `identities.csv` exported
+- [x] `schema.sql` from PostgreSQL
+- [x] Dashboard v2 running at port 5050
@@ -0,0 +1,134 @@
+# Processor 產出機制檢討
+
+## 三層機制定義
+
+### 1. 中斷接續（Interruption Resume）
+Process 被殺掉後，重啟時能接續進度。
+**現狀**: 大部分 processor 有 `.tmp` → `.partial` 保護，但重跑時從頭開始。
+
+### 2. 補充機制（Supplement）
+完成度不足時，只補沒做完的部分，不重跑整個。
+**現狀**: 全部從頭跑，無補充。
+
+### 3. 糾錯機制（Error Correction）
+輸出檔損毀時能自動偵測並修復。
+**現狀**: file-existence check 只檢查檔案存在，不檢查內容是否有效。
+
+---
+
+## Processor 逐一檢討
+
+### ASR
+| 面向 | 現狀 | 問題 |
+|------|------|------|
+| 中斷接續 | ✅ `.tmp` → `.partial`（executor） | ✅ OK |
+| 補充機制 | ❌ 每次從頭跑 | 若跑到 50% 被殺，下次從 0% 開始 |
+| 糾錯機制 | ❌ 不驗證內容 | file-existence check 看到 `.json` 存在就跳過，不管內容 |
+| Pipe | ✅ executor.run() | ✅ |
+| Timeout | ✅ 已移除（None） | ✅ |
+
+**改善方案**:
+- 補充：ASR 重跑時掃描 existing `.json` 或 `.partial`，找出最後 segment 的 `end_time`，傳入 `--resume-from` 給 Python script
+- 糾錯：file-existence check 對 `.json` 做 `serde_json::from_str` 驗證，無效 → 視為不存在
+
+### ASRX
+| 面向 | 現狀 | 問題 |
+|------|------|------|
+| 中斷接續 | ❌ **不用 executor**，直接寫 `.json` | 被殺掉時留下壞檔 |
+| 補充機制 | ❌ 同 ASR | 依賴 ASR，ASR 不完整 ASRX 也不能跑 |
+| 糾錯機制 | ❌ 不驗證內容 | 同上 |
+| Pipe | ❌ **raw Command**，沒有 `.tmp` 保護 | 緊急 |
+| Timeout | ⚠️ 7200s hardcode | 應改為 None（同 ASR） |
+
+**改善方案**:
+- **最優先**: 改為使用 `executor.run()`，獲得 `.tmp` 保護
+- 其他同 ASR
+
+### YOLO
+| 面向 | 現狀 | 問題 |
+|------|------|------|
+| 中斷接續 | ✅  executor `.tmp` | ✅ |
+| 補充機制 | ❌ 從頭跑 | 若跑到 frame 100,000 被殺，下次從 frame 0 |
+| 糾錯機制 | ❌ 不驗證內容 | yolo.json 之前就是壞的但 file check 跳過 |
+
+**改善方案**:
+- 補充：掃描 `.partial` 的最後 frame，傳入 `--resume-frame` 給 Python script
+- 糾錯：file-existence check 對 `.json` 做 JSON parse 驗證
+
+### FACE / POSE / OCR
+| 面向 | 現狀 | 問題 |
+|------|------|------|
+| 中斷接續 | ✅ executor `.tmp` | ✅ |
+| 補充機制 | ❌ 從頭跑 | 同 YOLO |
+| 糾錯機制 | ❌ 不驗證內容 | 同 YOLO |
+
+**改善方案**: 同 YOLO
+
+### CUT
+| 面向 | 現狀 | 問題 |
+|------|------|------|
+| 中斷接續 | ✅ executor `.tmp` | ✅ |
+| 補充機制 | ✅ register 階段已完成，直接載入 | ✅ |
+| 糾錯機制 | ❌ 不驗證內容 | 同 YOLO |
+
+**改善方案**: 糾錯即可
+
+### SCENE
+| 面向 | 現狀 | 問題 |
+|------|------|------|
+| 中斷接續 | ✅ **最完整**：檢查 `.err`/`.json`/`.tmp` 三種狀態 | ✅ |
+| 補充機制 | ❌ 從頭跑 | ✅（scene 很快） |
+| 糾錯機制 | ⚠️ 有檢查 `.err` | ✅ |
+
+### VISUAL_CHUNK
+| 面向 | 現狀 | 問題 |
+|------|------|------|
+| 中斷接續 | ✅ executor `.tmp` | ✅ |
+| 補充機制 | ❌ | ❌ |
+| 糾錯機制 | ❌ **錯誤被吞掉**（回傳空結果） | 應回報 error 而非靜默失敗 |
+
+**改善方案**: 不要吞錯誤，讓 error 往上傳
+
+### STORY
+| 面向 | 現狀 | 問題 |
+|------|------|------|
+| 中斷接續 | ✅ executor `.tmp` | ✅ |
+| 補充機制 | ❌ | ❌ |
+| 糾錯機制 | ❌ | ❌ |
+
+---
+
+## 優先級
+
+### P0 — 立即修復
+
+1. **ASRX 改用 executor.run()**
+   - 檔案：`src/core/processor/asrx.rs`
+   - 獲得 `.tmp` 保護、SIGKILL process group、`.partial` 保留
+   - 移除 hardcode timeout
+
+### P1 — 糾錯機制
+
+2. **File-existence check 加入 JSON 驗證**
+   - 檔案：`src/worker/job_worker.rs`
+   - 在 `output_path.exists()` 之後，對 `.json` 做 `serde_json::from_str::<Value>`
+   - 若 parse 失敗 → 不 skip，當作檔案不存在繼續跑
+   - 若 parse 成功但內容空（無 segments/frames）→ 當不完整
+
+### P2 — 補充機制
+
+3. **ASR resume-from 補充**
+   - 檔案：`src/core/processor/asr.rs` + `scripts/asr_processor.py`
+   - Rust 端發現 `.partial` 存在，讀取最後 segment 的 end_time
+   - 傳入 `--resume-from {time}` 給 Python script
+   - Python script 跳過 `--resume-from` 之前的音訊
+
+4. **YOLO/Face/Pose resume-frame 補充**
+   - 檔案：各 processor.rs + 對應 Python script
+   - 掃描 `.partial` 中的最後 frame_number
+   - 傳入 `--resume-frame {frame}` 給 Python script
+
+### P3 — 其他
+
+5. **VisualChunk 不吞錯誤**
+6. **Executor SIGTERM → SIGKILL 兩段式關閉**
@@ -0,0 +1,81 @@
+# Release Packaging Design
+
+三類包：**開發系統升級包** + **生產系統升級包** + **檔案內容包**，完全獨立。
+
+## 1. 開發系統升級包 (System/Dev)
+
+給 playground（port 3003, dev schema）使用。
+
+```
+release/system/dev/{version}/
+├── RELEASE_INFO.txt
+├── source.tar.gz           ← Rust + scripts source code
+├── .env.development        ← DATABASE_SCHEMA=dev, port 3003
+├── schema_dev.sql          ← dev schema DDL
+├── scripts/
+│   ├── pipeline_status.py
+│   ├── generate_asr1.py
+│   ├── apply_asr_corrections.py
+│   ├── clean_sentence_text.py
+│   └── import_file_package.py    ← 匯入檔案內容包
+├── test/
+│   └── api_test.sh
+└── migration/
+    └── {prev}_to_{version}.sql
+```
+
+升級：覆蓋 code + 執行 migration → `cargo build --bin momentry_playground` → 重啟 3003
+
+## 2. 生產系統升級包 (System/Prod)
+
+給 production（port 3002, public schema）使用。
+
+```
+release/system/prod/{version}/
+├── RELEASE_INFO.txt
+├── source.tar.gz           ← Rust + scripts source code
+├── .env                    ← DATABASE_SCHEMA=public, port 3002
+├── schema_public.sql       ← public schema DDL
+├── scripts/ (same as dev)
+├── test/
+│   └── api_test.sh
+└── migration/
+    └── {prev}_to_{version}.sql
+```
+
+## 3. 檔案內容包 (File)
+
+一個影片的完整資料，開發與生產環境共用。
+
+```
+release/files/{file_uuid}/{version}/
+├── metadata.json            ← Registration info
+├── RELEASE_INFO.txt
+├── processors/              ← output_dev/{uuid}.*.json
+│   ├── asr.json
+│   ├── asrx.json
+│   ├── asr-1.json
+│   ├── yolo.json
+│   ├── face.json
+│   ├── pose.json
+│   ├── ocr.json
+│   ├── cut.json
+│   └── scene.json
+├── face_detections.csv      ← 該檔案的所有 face detections
+├── identities.csv           ← 關聯的 identities
+├── tkg_nodes.csv            ← TKG nodes
+├── tkg_edges.csv            ← TKG edges
+├── qdrant/                  ← Qdrant snapshots for this file
+│   ├── momentry_dev_v1.snapshot
+│   ├── sentence_story.snapshot
+│   └── ...
+└── RELEASE_INFO.txt
+```
+
+### 匯入流程
+
+```
+1. POST /api/v1/files/register  → 取得 file_uuid
+2. python3 scripts/import_file_package.py --uuid {uuid} --package path/
+3. 檔案狀態更新為「已註冊已處理」
+```
@@ -0,0 +1,240 @@
+# Momentry Model — 分階段交付
+
+## 核心架構
+
+```
+Pipeline (training)
+  │  每個 processor 產出 .json
+  │  Rule 1/3 Ingestion → chunks + embeddings
+  ▼
+momentry model for {video}      ← 每部影片 = 一個 model
+  │  release/phase1/latest/
+  │  release/phase2/latest/
+  ▼
+momentry core (inference engine)  ← Rust API server
+  │  momentry_playground (dev)
+  │  momentry (production)
+  ▼
+Search / Query / Identity APIs
+```
+
+- **Pipeline** = training phase：影片 → processor output → chunks → embeddings
+- **Model** = 每部影片的產出 package（output_json + chunks + vectors）
+- **Engine** = momentry core，吃 model 提供 API（search, trace, identity）
+
+每個影片可有多個 model 版本，命名保留升級空間：
+
+| Model 版本 | Qdrant Collection | 內容 | 觸發時機 |
+|-----------|------------------|------|---------|
+| `{uuid}_v1` | `momentry_dev_v1` | sentence chunk embedding（base） | ASR + ASRX + Rule 1 完成 |
+| `{uuid}_v2` | `momentry_dev_v2` | 完整 pipeline + 5W1H | 全部完成 |
+| `{uuid}_v3` | `momentry_dev_v3` | object identity + custom detector | v2 + object instance matching 完成 |
+
+各版本共存不覆蓋。
+
+## 階段劃分
+
+### Phase 1：Sentence Chunk Embedding（base model）
+
+**觸發時機**: ASR + ASRX 完成 + Rule 1 Ingestion + vectorize 完成
+
+**交付內容**:
+- `{uuid}.asr.json`
+- `{uuid}.asrx.json`
+- chunks（chunk_type = 'sentence'）
+- chunk_vectors（sentence embedding）
+
+**用途**: 終端使用者可進行語意搜尋
+
+### Phase 2：完整 Pipeline（v2 model）
+
+**觸發時機**: 全部 processor 完成 + Rule 3 Ingestion + 5W1H Agent
+
+**交付內容**:
+- Phase 1 全部內容
+- 所有 `{uuid}.*.json`（cut, yolo, face, pose, ocr, ...）
+- chunks（chunk_type = 'cut', 'visual', 'trace', 'story'）
+- chunk_vectors（summary embedding）
+- identities / identity_bindings / face_detections
+
+**用途**: 完整搜尋 + 摘要 + 人物識別
+
+---
+
+## Worker Pipeline
+
+```
+ASR 完成 → ASRX 完成
+  ↓
+Rule 1 Ingestion (sentence chunks)
+  ↓
+vectorize_chunks (sentence embedding)
+  ↓
+📦 Phase 1 release  ───→ release/phase1/latest/  (base model)
+  ↓
+其他 processors 繼續 (yolo, face, pose, ocr, ...)
+  ↓
+Rule 3 Ingestion + 5W1H Agent
+  ↓
+📦 Phase 2 release  ───→ release/phase2/latest/  (full model)
+```
+
+## 產出目錄結構
+
+```
+release/
+├── phase1/
+│   ├── {version}_{timestamp}/
+│   │   ├── output_json/      ← 所有已完成的 .json
+│   │   ├── chunks.csv        ← sentence chunks
+│   │   ├── vectors.csv       ← sentence embeddings
+│   │   ├── schema.sql        ← chunks table DDL
+│   │   └── RELEASE_INFO.txt
+│   └── latest → {version}_{timestamp}
+│
+└── phase2/
+    ├── {version}_{timestamp}/
+    │   ├── output_json/      ← 所有 .json
+    │   ├── chunks.csv        ← 所有 chunks
+    │   ├── vectors.csv       ← 所有 embeddings
+    │   ├── identities.csv    ← 人物身分
+    │   ├── schema.sql        ← 完整 schema
+    │   └── RELEASE_INFO.txt
+    └── latest → {version}_{timestamp}
+```
+
+## momentry model vs momentry core
+
+| | momentry model | momentry core |
+|---|---|---|
+| 類比 | 訓練好的 weights | inference engine |
+| 內容 | `.json` + chunks + vectors | Rust binary |
+| 生命週期 | 每部影片產出一個 | 一個 binary 服務所有影片 |
+| 版本 | `{uuid}_v1`（base） / `{uuid}_v2` / `{uuid}_v3` | `momentry_playground` / `momentry` |
+| 交付對象 | 終端使用者 | 部署工程師 |
+
+---
+
+## Wiki 機制：每個 model 都可被調整
+
+每個 momentry model（`{uuid}_v1` / `v2` / `v3`）不只是唯讀的產出，而是可透過 wiki 機制持續改善。
+
+### 與傳統 RAG 的區別
+
+| | 傳統 RAG | momentry wiki |
+|---|---|---|
+| 知識儲存 | vector DB（ephemeral） | model package（permanent） |
+| 修正方式 | query 時 LLM 決定是否採用 | 使用者/Agent 直接編輯 |
+| 修正持久性 | ❌ 下次 query 就消失 | ✅ 寫入 model，版本化保存 |
+| 模型改進 | 無（僅改變 prompt） | 下次 version bump 時合併為 ground truth |
+| 協作方式 | 單向（retrieve → generate） | 雙向（編輯 → 合併 → 改進） |
+| 離線可用 | ❌ 需 vector DB + LLM | ✅ 離線查閱 wiki 目錄 |
+
+**momentry wiki 不是 RAG 的替代品，而是 model 的生命週期管理機制。**
+
+### 概念
+
+```
+momentry model (release package)
+  ├── output_json/          ← 唯讀，processor 產出
+  ├── chunks.csv            ← 唯讀，ingestion 產出
+  ├── vectors.csv           ← 唯讀，embedding 產出
+  └── wiki/                 ← 可編輯，使用者貢獻知識
+        ├── identities.json   ← "trace 5 = Audrey Hepburn"
+        ├── objects.json      ← "object 42 = 郵票 #1"
+        ├── corrections.json  ← "ASR 'Hello' → 'Halo'"
+        └── changelog.json    ← 編輯歷史
+```
+
+### 資料流向
+
+```
+使用者/Agent 編輯 wiki
+        ↓
+  DB wiki_entries + wiki_revisions 寫入
+        ↓
+  下次 release 打包時 merge 進 model
+        ↓
+  TKG label 更新 (tkg_nodes.label)
+        ↓
+  新版 model version bump
+```
+
+### 與 TKG 的關係
+
+wiki 的 identity 和 object 標註會回寫到 TKG node label：
+```
+(face_trace:5) label="Audrey Hepburn"     ← wiki 編輯
+(object_instance:42) label="郵票 #1"       ← wiki 編輯
+```
+
+這些編輯累積後，可做為下一版 model training 的 ground truth。
+
+### 實作方向
+
+**DB 層** — 新 table `wiki_entries` + `wiki_revisions`：
+```sql
+wiki_entries (target_type, target_id, title, body, summary, status, version, file_uuid)
+wiki_revisions (entry_id, version, title, body, summary, change_summary, edited_by)
+```
+
+**API 層** — CRUD + 版本歷史：
+```
+GET    /api/v1/wiki/{target_type}/{target_id}
+PUT    /api/v1/wiki/{target_type}/{target_id}
+GET    /api/v1/wiki/{target_type}/{target_id}/revisions
+POST   /api/v1/wiki/search
+```
+
+**打包層** — `release_pack.py` 加入 wiki 匯出，與 model 共存
+
+---
+
+## Phase 3：Object Identity（v3 model）
+
+### 目標
+
+從影片中提取關鍵物體（郵票、手槍、信封、放大鏡...），對同類物體做 instance-level 的跨畫面追蹤與辨識，達到類似 face trace 的效果 — 不只是 detect class，還能區分「這一張郵票」vs「那一張郵票」。
+
+### 現狀問題
+
+1. **COCO 80 類不包含關鍵物體** — 郵票、手槍、信封、放大鏡等不在 COCO 資料集中
+2. **YOLOv5nano 偵測率低** — 即使是 COCO 類別（knife, cell phone）在 nano 模型上 recall 不足
+3. **無 object instance matching** — 目前只有 frame-level detection，沒有跨 frame 的物體追蹤
+
+### 技術方向
+
+```
+YOLOv8m/OWL-ViT → 改善 detection coverage
+        ↓
+ Object Tracker (IoU + embedding，類似 face tracker)
+        ↓
+ object_trace → TKG CO_OCCURS_WITH edges
+        ↓
+ object identity → 同物體跨場景辨識
+```
+
+| 方向 | 方法 | 效果 |
+|------|------|------|
+| Model upgrade | `yolov5nu` → `yolov8s.pt` / `yolov8m.pt` | COCO recall 提升 |
+| Custom fine-tune | 收集 stamps/guns 資料 fine-tune YOLO | 可偵測非 COCO 物件 |
+| Zero-shot | OWL-ViT / Grounding DINO by text prompt | 不用 training，但速度慢 |
+| Object trace | IoU + embedding 跨 frame 匹配 | instance-level 追蹤 |
+| Object identity | clustering 跨場景辨識同一物體 | 可在全片搜尋「這把槍」 |
+
+### 與 TKG 整合
+
+```
+face_trace -[:CO_OCCURS_WITH]-> object_instance:5  (這把槍)
+face_trace -[:CO_OCCURS_WITH]-> object_instance:42 (這張郵票)
+
+查詢: "Audrey Hepburn 拿這把槍的畫面"
+→ face_trace:5 -[:SPEAKS_AS]-> SPEAKER_0
+→ face_trace:5 -[:CO_OCCURS_WITH]-> object_instance:5
+```
+
+### 交付順序
+
+1. YOLO model upgrade（低難度，立即見效）
+2. Object tracker（中難度，參考 face tracker 實作）
+3. Custom fine-tune / zero-shot（高難度，需資料或新模型）
@@ -0,0 +1,101 @@
+# Trace Search API 設計
+
+## 概念
+
+trace 是一種 chunk。
+
+現有的 chunk_type: `cut`, `sentence`, `visual`, `story`
+新增 chunk_type: `trace`
+
+每個 trace（人物跨 frame 追蹤軌跡）就是一個時間區間 + 區間內的 ASR text。
+跟其他 chunk 完全一樣，只是切分維度不同：
+- cut chunk = 鏡頭切換
+- sentence chunk = 語句邊界
+- visual chunk = 畫面物體組合
+- **trace chunk = 人物出現區間 + 當下 spoken text**
+
+這樣 trace 可以直接放進現有的 `chunks` 表，共用 embedding、搜尋、Qdrant sync 整套機制，不需要任何新 table。
+
+## chunks 表現有結構
+
+```sql
+chunks (
+    id, file_uuid, chunk_type,                   -- 'trace' 新增
+    start_frame, end_frame, start_time, end_time,
+    text_content,                                 -- trace 區間的 ASR text
+    embedding,                                    -- text_content 的 pgvector
+    metadata JSONB,                               -- { trace_id, face_count, identity_id, identity_name }
+    ...
+)
+```
+
+## 資料產生流程（worker 擴充）
+
+在 face processing +  `store_traced_faces.py` 完成後：
+
+1. 查詢 `face_detections` 聚合每個 trace 的 `MIN(frame)`, `MAX(frame)`, `COUNT(*)`
+2. 對每個 trace，查詢 `pre_chunks WHERE processor_type='asr'` 中與 trace time range 重疊的 text
+3. 彙整 text → EmbeddingGemma 產生 `embedding`
+4. 寫入 `chunks`（`chunk_type='trace'`），metadata 含 `trace_id`, `face_count`, `identity_id`
+5. embedding 自動進 Qdrant（與既有 chunk 同一 collection）
+
+## Search API 擴充
+
+Universal Search 的 `types` 原本就支援 `"chunk"`。
+在 chunk 搜尋中過濾 `chunk_type = 'trace'` 即可。
+
+**Request**：
+```json
+{
+  "query": "open the door",
+  "types": ["chunk"],
+  "filters": { "chunk_type": "trace" },
+  "uuid": "aeed71342a899fe4b4c57b7d41bcb692",
+  "page": 1,
+  "page_size": 20
+}
+```
+
+**Response**（與既有 Chunk result 相同）：
+```json
+{
+  "type": "chunk",
+  "chunk_id": "chunk_42",
+  "chunk_type": "trace",
+  "start_frame": 45200, "end_frame": 45900,
+  "start_time": 1808.0, "end_time": 1836.0,
+  "score": 0.87,
+  "text": "Open the door. Come on, hurry up.",
+  "metadata": {
+    "trace_id": 5,
+    "face_count": 42,
+    "identity_name": "Audrey Hepburn"
+  }
+}
+```
+
+完全沿用既有的 `SearchResult::Chunk` variant，不用新增 enum variant。
+
+### 搜尋語法
+
+```sql
+SELECT c.*
+FROM dev.chunks c
+WHERE c.file_uuid = $1
+  AND c.chunk_type = 'trace'
+  AND c.embedding IS NOT NULL
+ORDER BY c.embedding <=> $2
+LIMIT $3;
+```
+
+## 總結
+
+| 項目 | 作法 |
+|------|------|
+| 新 table | ❌ 不需要 |
+| 新 enum variant | ❌ 不需要 |
+| SearchResult 改動 | ❌ 不需要 |
+| chunk_type 新增 | ✅ `'trace'` |
+| worker 擴充 | ✅ 產生 trace chunk (face done 後) |
+| SearchFilters 擴充 | ✅ 加 `chunk_type` filter |
+| Qdrant | ✅ 自動（既有 chunk collection） |
@@ -0,0 +1,201 @@
+# Momentry Eye API Reference
+
+**Vision Agent** — Multi-model zero-shot object detection service.
+Port: `5052` | Resource IDs: `eye-gdino`, `eye-paligemma`
+
+---
+
+## Models
+
+| Model | ID | Params | Size | Confidence | Speed | License |
+|-------|-----|--------|------|------------|-------|---------|
+| Grounding DINO | `grounding-dino` | 232M | 891MB | ✅ 0-1 score | ~340ms | Apache 2.0 |
+| PaliGemma 3B | `paligemma` | 2,923M | ~3GB | ❌ no score | ~80ms | Gemma license |
+
+## Endpoints
+
+### `GET /health`
+
+System status and loaded models.
+
+```bash
+curl localhost:5052/health
+```
+
+Response:
+```json
+{
+  "status": "ok",
+  "models_loaded": ["grounding-dino"],
+  "models_available": ["grounding-dino", "paligemma"],
+  "device": "mps",
+  "port": 5052
+}
+```
+
+### `GET /models`
+
+List available models with specs.
+
+```bash
+curl localhost:5052/models
+```
+
+### `POST /detect`
+
+Detect objects in a single video frame.
+
+```bash
+curl localhost:5052/detect \
+  -H "Content-Type: application/json" \
+  -d '{"time":5461, "prompt":"gun", "model":"grounding-dino"}'
+```
+
+**Parameters:**
+
+| Param | Type | Default | Description |
+|-------|------|---------|-------------|
+| `uuid` | string | `aeed71342a...` | Video file UUID |
+| `time` | float | `0` | Timestamp in seconds |
+| `prompt` | string | `"gun"` | Object to detect |
+| `model` | string | `"grounding-dino"` | Model: `grounding-dino`, `paligemma`, or `fusion` |
+| `threshold` | float | `0.1` | Minimum confidence (GDINO only) |
+| `weights` | object | — | Fusion weights, e.g. `{"grounding-dino":0.6,"paligemma":0.4}` |
+
+**Fusion mode** runs both models and combines results with weighted scoring. Default weights: GDINO 0.6, PaliGemma 0.4.
+
+```bash
+# Fusion: run both models, combine results
+curl localhost:5052/detect \
+  -d '{"time":206, "prompt":"water gun", "model":"fusion"}'
+
+# Custom fusion weights
+curl localhost:5052/detect \
+  -d '{"time":206, "prompt":"gun", "model":"fusion",
+       "weights":{"grounding-dino":0.5,"paligemma":0.5}}'
+```
+
+**Response:**
+
+```json
+{
+  "model": "grounding-dino",
+  "detections": [
+    {"bbox": [726.2, 567.4, 969.0, 694.6], "score": 0.476, "label": "gun"},
+    {"bbox": [686.7, 567.0, 969.6, 918.3], "score": 0.262, "label": "gun"}
+  ],
+  "time_ms": 345.2,
+  "n_detections": 2,
+  "shot_url": "/shots/aeed7134_5461s_gun_grounding-dino.jpg"
+}
+```
+
+**Fusion response** also includes `per_model` (detections per model) and `fusion` (deduplicated combined list with `fused_score`).
+
+### `POST /search`
+
+Search across a time range.
+
+```bash
+# Natural language query
+curl localhost:5052/search \
+  -d '{"query":"find the gun", "range":"5400-5600", "interval":10}'
+```
+
+**Parameters:**
+
+| Param | Type | Default | Description |
+|-------|------|---------|-------------|
+| `query` | string | `"find the gun"` | Natural language query (parsed to extract object) |
+| `target` | string | — | `file_uuid:chunk_id` or `file_uuid:trace_id` — resolves to time range |
+| `range` | string | `"0-6780"` | Manual time range |
+| `interval` | int | `30` | Scan interval in seconds |
+| `model` | string | `"grounding-dino"` | Detection model |
+| `threshold` | float | `0.15` | Minimum confidence |
+
+**Target resolution:**
+
+| Format | Example | Resolves to |
+|--------|---------|-------------|
+| `file_uuid:chunk_id` | `uuid:uuid_story_90` | Chunk's time range |
+| `file_uuid:trace_id` | `uuid:trace_5` | Trace's time range |
+| `file_uuid:chunk_index` | `uuid:500` | Chunk index 500's range |
+
+```bash
+# Using target
+curl localhost:5052/search \
+  -d '{"target":"aeed71342...:aeed71342..._story_90", "query":"gun"}'
+
+# Using trace
+curl localhost:5052/search \
+  -d '{"target":"aeed71342...:trace_5", "query":"person"}'
+```
+
+### `POST /multimodal`
+
+Multi-modal search across sentence chunks — combines ASR text match + visual confirmation.
+
+```bash
+# Search for Jean-Louis: ASR match + GDINO child detection
+curl localhost:5052/multimodal \
+  -d '{"keyword":"Jean-Louis", "prompt":"child"}'
+
+# Search trace chunks visually (no ASR)
+curl localhost:5052/multimodal \
+  -d '{"keyword":"", "prompt":"person", "chunk_type":"trace", "range":"3500-4000"}'
+```
+
+**Parameters:**
+
+| Param | Type | Default | Description |
+|-------|------|---------|-------------|
+| `keyword` | string | — | ASR keyword to search in sentence text |
+| `prompt` | string | same as keyword | Visual prompt for GDINO |
+| `chunk_type` | string | `"sentence"` | `sentence`, `trace`, `story`, `cut` |
+| `target` | string | — | Specific chunk target |
+| `range` | string | `"0-6780"` | Time range (for non-sentence chunks) |
+| `threshold` | float | `0.15` | Visual detection threshold |
+
+### `GET /shots/<filename>`
+
+Retrieve annotated detection images.
+
+```bash
+curl -o result.jpg localhost:5052/shots/aeed7134_5461s_gun_grounding-dino.jpg
+```
+
+## Object Detection Performance Summary
+
+| Object type | Size in frame | GDINO | PaliGemma | Best prompt |
+|-------------|--------------|-------|-----------|-------------|
+| Gun (realistic) | 15-30% | ✅ 0.36-0.67 | ✅ | `pistol` / `handgun` |
+| Water gun (toy) | 15-31% | ❌ 0 | ✅ | `water gun` (PaliGemma) |
+| Child (Jean-Louis) | 30-60% | ⚠️ 0.3-0.9 | ❌ | `child` (high FP on adults) |
+| Stamp | <5% | ❌ FP | ❌ | — |
+| Passport | <10% | ❌ FP | ❌ | — |
+| Magnifying glass | <5% | ❌ FP | ❌ | — |
+| Cup / Bottle | 5-15% | ✅ 0.3-0.5 | — | `cup` / `bottle` |
+| Cell phone | 5-10% | ✅ 0.3-0.5 | — | `cell phone` |
+
+## Resource Registration
+
+On startup, the agent auto-registers as resources in `dev.resources`:
+
+| Resource ID | Type | Status |
+|-------------|------|--------|
+| `eye-gdino` | `vision_model` | `online` |
+| `eye-paligemma` | `vision_model` | `online` |
+
+Heartbeat updates every 60 seconds. Discover via:
+
+```sql
+SELECT * FROM dev.resources WHERE resource_type = 'vision_model';
+```
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `scripts/vision_agent.py` | Vision Agent server (port 5052) |
+| `output_dev/vision_shots/` | Annotated detection screenshots |
+| `docs/ZERO_SHOT_DETECTION_RESEARCH.md` | Full model research report |
@@ -0,0 +1,190 @@
+# Zero-Shot Object Detection Model Research Report
+
+**Date:** 2026-05-10
+**Goal:** Evaluate models for detecting arbitrary objects in Charade (1963)
+**System:** M5 MacBook Pro (Apple Silicon MPS, 48GB)
+
+---
+
+## Tested Models
+
+| Model | Params | Size | Resolution | Type | License |
+|-------|--------|------|------------|------|---------|
+| YOLOv8n fine-tune (gun) | 3.2M | 6MB | 640px | Closed-set (4 classes) | AGPL-3.0 |
+| OWL-ViT base | 109M | 586MB | 384px | Zero-shot | Apache 2.0 |
+| **Grounding DINO Base** | **232M** | **891MB** | **384px** | **Zero-shot** | **Apache 2.0** |
+| Grounding DINO Large | 232M | 895MB | 384px | Zero-shot | Apache 2.0 |
+| Florence-2 Base | 231M | ~3GB | 384px | Zero-shot (generative) | MIT |
+| Florence-2 Large | 776M | ~6GB | 384px | Zero-shot (generative) | MIT |
+| PaliGemma 3B mix-224 | 2,923M | ~3GB | 224px | Zero-shot (generative) | Gemma license |
+| PaliGemma 3B mix-448 | 2,923M | ~6GB | 448px | Zero-shot (generative) | Gemma license |
+
+## Detection Performance on Charade
+
+### Large Objects (gun)
+
+| Model | 8 timepoints | Best confidence | Runtime |
+|-------|-------------|----------------|---------|
+| YOLOv8n fine-tune | ❌ 0/5 (all FP) | 0.45 (stamp→pistol) | 0.03s |
+| OWL-ViT | ❌ 2/8 | 0.054 | 3.4s |
+| **Grounding DINO Base** | **✅ 8/8** | **0.499** | **0.33s** |
+| PaliGemma 3B mix-224 | ✅ 3/8 (gun), 3/8 overall | 0.499 | 0.5-3s |
+
+### Small Objects (stamp, passport, magnifying glass)
+
+| Model | Stamp | Passport | Magnifying glass |
+|-------|-------|----------|-----------------|
+| Grounding DINO Base | ❌ FP (~0.3) | ❌ FP (~0.4) | ❌ FP (~0.3-0.5) |
+| PaliGemma 3B mix-224 | ❌ no det | ❌ no det | not tested |
+| PaliGemma 3B mix-448 | ❌ (not tested) | ❌ (not tested) | ❌ (not tested) |
+
+**All models fail on objects smaller than ~50px at native 1920x1080 resolution.**
+
+### Other Objects
+
+| Object | YOLO COCO | Grounding DINO | Notes |
+|--------|-----------|----------------|-------|
+| knife | ✅ 368 frames | ✅ 84 hits | Small but detectable |
+| cup | ✅ | ✅ 13 hits | Moderate size |
+| bottle | ✅ | ✅ 12 hits | Moderate size |
+| cell phone | ✅ | ✅ 5 hits | Hand-held |
+| book | ✅ | ✅ 3 hits | Hand-held |
+| car | ✅ | ✅ 9 hits | Large object |
+| tie | ✅ | ✅ 139 hits | On-person (worn, not held) |
+
+## Detailed Model Analysis
+
+### Grounding DINO Base (Recommended)
+
+**Scores:** Detection confidence 0.1-0.5 (typical for zero-shot)
+
+**Timing per frame (MPS):**
+| Component | Time | % of total |
+|-----------|------|------------|
+| Processor (text+image) | 17ms | 5% |
+| Model inference | 310ms | 93% |
+| Post-processing | 5ms | 2% |
+| **Total** | **331ms** | **100%** |
+
+**Multi-prompt batching:** 8 prompts in 335ms (42ms/prompt vs 309ms single)
+
+**Memory:** ~1GB (MPS)
+
+**License:** Apache 2.0 — fully commercial, no restrictions
+
+### Grounding DINO Large
+
+**Result:** Identical weights to Base. The GitHub "7-dataset" checkpoint is the same 3-dataset version as HuggingFace. The actual 7-dataset version (56.7 AP) was never released.
+
+**Verdict: Do not use.** Base is identical and simpler.
+
+### OWL-ViT
+
+**Result:** Almost useless for this task. Max confidence 0.054. Detect only 2/8 timepoints.
+
+**Verdict: Do not use.**
+
+### Florence-2
+
+**Issue:** `prepare_inputs_for_generation` bug in current transformers version. Cannot run inference without patching model code.
+
+**Task format:** Uses task tokens (`<OD>`) instead of arbitrary text prompts. Cannot do "detect gun" directly — uses generic object detection.
+
+**Verdict: Cannot use in current environment.**
+
+### PaliGemma
+
+**Result:** Works for gun detection (3/8) but misses small objects entirely.
+
+**Key limitation:** No confidence score output (generative model). Either outputs bbox or nothing.
+
+**Issues:**
+- 224px variant: Too low resolution for small objects
+- 448px variant: 6GB download, suspected better for detail but untested
+- Gemma license may restrict commercial use vs Apache 2.0
+
+**Verdict: Inferior to Grounding DINO for this use case.**
+
+### YOLOv8n Fine-tune (Gun Detector)
+
+| Dataset | 905 images (Roboflow CC BY 4.0) |
+| Classes | grenade, knife, pistol, rifle |
+| Validation mAP50 | 0.813 |
+| Charade FP rate | **100%** (all false positives) |
+
+**Root cause:** Training images are close-up gun photos; Charade has distant/partial guns. Distribution mismatch makes this model unusable.
+
+**Verdict: Requires completely new training dataset.**
+
+## Root Cause Analysis: Small Object Failure
+
+### Grounding DINO's Resolution Limit
+
+Grounding DINO processes images at **384×384px**. At this resolution:
+
+```
+1920px frame → 384px input (5:1 reduction)
+A 50×50px object → 10×10px at 384px → only ~1 patch token
+```
+
+For comparison:
+- **Gun** at 200×200px (close-up) → 40×40px → still detectable
+- **Stamp** at 30×30px → 6×6px → lost in downsampling
+- **Passport** at 80×120px → 16×24px → barely visible
+- **Magnifying glass** at 40×40px → 8×8px → lost
+
+### Potential Solutions
+
+| Solution | Pros | Cons | Feasibility |
+|----------|------|------|-------------|
+| **Crop + zoom** on person region | Leverages existing YOLO person detections | Requires two-stage pipeline | ✅ High |
+| **PaliGemma 448px** | 448px native (36% more detail) | 6GB, requires download | ⚠️ Medium |
+| **YOLO fine-tune on stamps** | Fast inference (6MB) | Need 200+ training images | ⚠️ Medium |
+| **Grounding DINO + tiling** | Split image into tiles, run per tile | 4-9x slower | ⚠️ Medium |
+| **Florence-2 448px** | Higher resolution | Bug in transformers | ❌ Low |
+
+## Hand-Held Object Detection Feasibility
+
+### Available Data Sources
+
+| Source | Type | Coverage | Usefulness |
+|--------|------|----------|------------|
+| YOLO `pre_chunks` | Object detections | 169,625 frames | ✅ Every frame |
+| Pose `pre_chunks` | Body keypoints (left_wrist, right_wrist) | 4,269 frames | ✅ Hand location |
+| Grounding DINO | Zero-shot classification | On-demand | ✅ Object ID |
+| ASR dialogue | Text mentions | 4,188 chunks | ✅ "holding a gun" |
+
+### Approach: YOLO + Pose + Grounding DINO
+
+```
+Frame
+  → YOLO: Find person + objects
+  → Pose: Find wrist keypoints
+  → Check: Object bbox overlaps with hand region (wrist ±100px)
+  → Grounding DINO: Verify object class
+```
+
+### Known Limitations
+
+1. **Pose frame alignment:** Pose data (4,269 frames) doesn't always overlap with YOLO data at the same frame
+2. **Object proximity ≠ holding:** YOLO objects near hands may be background, not held
+3. **Small object blind spot:** Stamps, magnifying glasses at hand positions are too small to detect
+
+## Recommendations
+
+| Priority | Action | Rationale |
+|----------|--------|-----------|
+| 1 | Use Grounding DINO Base (Apache 2.0) | Best zero-shot detector, proven on guns, clean license |
+| 2 | Two-stage pipeline for small objects | YOLO person box → crop → upscale → Grounding DINO |
+| 3 | Pose wrist alignment for hand-held confirmation | Reduce false positives by requiring hand proximity |
+| 4 | Replace Grounding DINO "Large" ref with Base | Large is identical weights, no benefit |
+
+## Appendix: License Summary
+
+| Model | License | Commercial Use | Requires |
+|-------|---------|---------------|----------|
+| Grounding DINO | **Apache 2.0** | ✅ Yes | NOTICE file |
+| OWL-ViT | Apache 2.0 | ✅ Yes | NOTICE file |
+| PaliGemma | Gemma license | ⚠️ Needs review | Google ToS |
+| Florence-2 | MIT | ✅ Yes | Copyright notice |
+| YOLOv8 | AGPL-3.0 | ⚠️ Needs license | Open source or paid |
@@ -0,0 +1,49 @@
+# Zero-Shot Gun Detection Test Plan
+
+**Date:** 2026-05-10
+**Goal:** Compare OWL-ViT vs Grounding DINO for detecting guns in Charade (1963)
+
+## Models
+
+| Model | Source | Type |
+|-------|--------|------|
+| `google/owlvit-base-patch32` | HuggingFace | Zero-shot object detection |
+| `IDEA-Research/grounding-dino-base` | HuggingFace | Zero-shot object detection |
+
+## Test Timepoints (8)
+
+| Time | Label | Source |
+|------|-------|--------|
+| 2646s (44:06) | 2646s | ASR: "He has a gun" |
+| 3188s (53:08) | 3188s | Original detection |
+| 3697s (61:37) | 3697s | ASR: "Where's your gun" |
+| 5341s (89:01) | 5341s | ASR: "He already killed 3 men" |
+| 5461s (91:01) | 5461s | Original detection |
+| 6309s (1:45:09) | 6309s | Original detection |
+| 6377s (1:46:17) | 6377s | Original detection |
+| 6479s (1:47:59) | 6479s | Original detection |
+
+## Prompts
+
+`"gun"`, `"pistol"`, `"rifle"`, `"weapon"`
+
+## Matrix
+
+8 timepoints × 2 models × 4 prompts = 64 inferences
+
+## Output
+
+| File | Description |
+|------|-------------|
+| `output_dev/zero_shot_test/*.jpg` | Annotated screenshots |
+| `output_dev/zero_shot_test/zero_shot_results.json` | Detection results |
+| `scripts/zero_shot_gun_test.py` | Test script |
+
+## Success Criteria
+
+| Level | Criteria |
+|-------|----------|
+| Excellent | Finds real gun with confidence > 0.5 |
+| Good | Finds real gun with confidence < 0.5 |
+| Limited | Finds guns but many false positives |
+| Failed | All false positives |
@@ -0,0 +1,67 @@
+# Zero-Shot Gun Detection Test Report
+
+**Date:** 2026-05-10
+**Goal:** Compare OWL-ViT vs Grounding DINO for detecting guns in Charade (1963)
+
+## Test Setup
+
+| Model | Prompts | Timepoints | Total inferences |
+|-------|---------|------------|-----------------|
+| `google/owlvit-base-patch32` | gun, pistol, rifle, weapon | 8 | 32 |
+| `IDEA-Research/grounding-dino-base` | gun, pistol, rifle, weapon | 8 | 32 |
+
+## Results
+
+| Model | Timepoints with detections | Total detections | Best confidence | Runtime |
+|-------|---------------------------|-----------------|-----------------|---------|
+| OWL-ViT | 2/8 | 2 | 0.054 | 1.5s |
+| **Grounding DINO** | **8/8** | **109** | **0.186** | 11.5s |
+
+## Grounding DINO — Per Timepoint
+
+| Time | Source | Best prompt | Best confidence | Found? |
+|------|--------|-------------|-----------------|--------|
+| 2646s (44:06) | ASR: "He has a gun" | gun | 0.082 | ✅ |
+| **3188s (53:08)** | **Original pistol** | **gun** | **0.149** | **✅** |
+| 3697s (61:37) | ASR: "Where's your gun" | gun | 0.159 | ✅ |
+| 5341s (89:01) | ASR: "He already killed 3 men" | gun | 0.074 | ✅ |
+| **5461s (91:01)** | **Original pistol** | **gun** | **0.186** | **✅** |
+| **6309s (1:45:09)** | **Original pistol** | **gun** | **0.077** | **✅** |
+| **6377s (1:46:17)** | **Original gun** | **weapon** | **0.118** | **✅** |
+| **6479s (1:47:59)** | **Original pistol** | **gun** | **0.060** | **✅** |
+
+### Original 5 Pistol Frames
+
+| Frame | OWL-ViT | Grounding DINO | Verdict |
+|-------|---------|----------------|---------|
+| 3188s | Not found | ✅ Found (0.149) | ✅ |
+| 5461s | Not found | ✅ Found (0.186) | ✅ |
+| 6309s | Not found | ✅ Found (0.077) | ✅ |
+| 6377s | Not found | ✅ Found (0.118) | ✅ |
+| 6479s | Not found | ✅ Found (0.060) | ✅ |
+
+## Analysis
+
+### OWL-ViT
+- Almost completely failed: only 2 detections at 0.05 confidence
+- Not suitable for this task
+
+### Grounding DINO
+- **Found all 8 timepoints**, including all 5 original pistol frames
+- Best prompt is consistently `"gun"` (6/8 timepoints)
+- Confidence range: 0.060 - 0.186 (typical for zero-shot detection)
+- Higher confidence correlates with user-confirmed detections
+
+### Key Finding
+The 5 original pistol frames were produced by **Grounding DINO** (not YOLOv8n). The model was downloaded from HuggingFace at 15:43-15:44 on May 9, and the screenshots were generated at 15:49 — confirming OWL-ViT was tested first (failed) and then Grounding DINO was tested (succeeded).
+
+## Integration
+
+Grounding DINO has been integrated into `object_search_agent.py` as `--source zero_shot`:
+```
+python3 scripts/object_search_agent.py --keyword gun --source zero_shot
+```
+
+## Screenshots
+
+All 64 annotated screenshots saved to `output_dev/zero_shot_test/*.jpg`
@@ -0,0 +1,115 @@
+# Zero-Shot vs Fine-Tune 物件偵測模型選型報告
+
+**Date:** 2026-05-10
+**Goal:** 在 Charade (1963) 中搜尋非 COCO 物件（槍枝、郵票、信封等）
+**System:** M5 MacBook Pro (Apple Silicon MPS)
+
+## 動機
+
+YOLOv8 COCO 只有 80 類，不包含 gun、stamp、envelope 等 Charade 核心物件。需要找到能在電影中搜尋任意物件的方法。
+
+## 候選方案
+
+| 方案 | 方法 | 訓練資料 | 開發成本 |
+|------|------|---------|---------|
+| A. YOLOv8n fine-tune | Fine-tune on gun dataset | 需收集 500+ 張標註圖片 | 高 |
+| B. OWL-ViT zero-shot | Vision-language pretraining | 無須訓練 | 低 |
+| C. Grounding DINO zero-shot | Vision-language pretraining | 無須訓練 | 低 |
+
+## 模型大小與效能
+
+| Model | 磁碟 | 參數 | 推論時間 (MPS) | 單幀能耗 | 模型類別 |
+|-------|------|------|---------------|---------|---------|
+| YOLOv8n | **6MB** | **3.2M** | **0.03s** | **~0.5J** | 封閉集（80 類） |
+| OWL-ViT | 586MB | 109M | 3.4s | ~50J | 開放集（zero-shot） |
+| **Grounding DINO** | **891MB** | **172M** | **4.3s** | **~65J** | **開放集（zero-shot）** |
+
+## Charade 實測結果
+
+| Model | 8 時間點命中 | 5 個原始 pistol | 最佳 confidence | 推論時間 | 模型大小 |
+|-------|-------------|-----------------|----------------|---------|---------|
+| YOLOv8n COCO | ❌ N/A（無 gun class） | — | — | 0.03s | 6MB |
+| YOLOv8n fine-tune | 7/7 FP | ❌ 全部 FP | 0.45（郵票誤判） | 0.03s | 6MB |
+| OWL-ViT | 2/8 | ❌ 0/5 | 0.054 | 3.4s | 586MB |
+| **Grounding DINO Base** | **31/32** | **✅ 5/5** | **0.672** | **11.6s** | **891MB** |
+| **Grounding DINO Large** | **32/32** | **✅ 5/5** | **1.000** | **50.1s** | **895MB** |
+
+### Base vs Large 比較
+
+| 指標 | Base (3 datasets) | Large (7 datasets) |
+|------|------------------|-------------------|
+| 平均最佳 confidence | 0.384 | **1.000** |
+| 總偵測數 | 333 | **28,800** |
+| COCO zero-shot AP | 48.4 | **56.7** |
+| 推論時間 (MPS) | 11.6s | 50.1s |
+| Edge 部署 | 較可行 | 較困難 |
+
+### 結論
+
+**效能優先選擇：Grounding DINO Large** — 所有 8 個時間點 confidence 1.000，零漏檢。犧牲推論速度但 detection 品質大幅超越 Base 版。
+
+**Edge 部署選擇：Grounding DINO Base** — 體積相近但推論快 4.3x，適合資源受限裝置。
+
+### 關鍵結論
+
+1. **YOLOv8n fine-tune 完全失敗** — 905 張 Roboflow 近距離特寫與 Charade 中遠景畫面分布 mismatch，訓練無法泛化
+2. **OWL-ViT 幾乎無效** — 對電影中的小物體辨識能力不足
+3. **Grounding DINO 成功** — 5/5 找回 pistol frames，所有 ASR gun mention 時間點也命中
+
+## Grounding DINO 優缺點
+
+### 優點
+- **零樣本搜尋**：任何 COCO 以外的物件直接用文字 prompt 搜尋
+- **延伸性**：同一模型可搜尋 gun、stamp、envelope、knife、hat 等任意物件
+- **無須訓練**：不需要收集標註資料或 fine-tune
+- **Apache 2.0 License**：可商用
+
+### 缺點
+- **體積大**：891MB（vs YOLOv8n 的 6MB）
+- **推論慢**：4.3s/frame（vs YOLOv8n 的 0.03s）
+- **不適合 real-time**：edge device 上無法做即時偵測，只適合離線掃描
+
+## Edge AI 部署考量
+
+| 項目標題 | YOLOv8n | Grounding DINO |
+|---------|---------|---------------|
+| 模型大小 | 6MB ✅ | 891MB ⚠️ |
+| RAM 需求 | ~100MB | ~2.5GB |
+| 推論時間 | 30ms | 4.3s |
+| 單幀能耗 | ~0.5J | ~65J |
+| 搜尋類別數 | 80（固定） | 無限（文字 prompt） |
+| 電池影響（1000 幀） | ~500J | ~65,000J |
+
+### 建議策略
+
+```
+離線掃描（Server/Gateway）：
+  用 Grounding DINO 對全片建立物件索引
+  → 耗時但可接受（113 min 電影約 2-3 小時）
+
+即時查詢（Edge Device）：
+  查詢時只跑 Grounding DINO 在該 timepoint → 4s/次
+  → 查詢體驗還可接受
+```
+
+## 整合狀態
+
+- ✅ Grounding DINO 測試通過
+- ✅ 整合進 `scripts/object_search_agent.py`（`--source zero_shot`）
+- ✅ 測試計畫：`docs/ZERO_SHOT_GUN_TEST_PLAN.md`
+- ✅ 測試報告：`docs/ZERO_SHOT_GUN_TEST_REPORT.md`
+
+## License 聲明
+
+Grounding DINO 採用 Apache 2.0 License，可商用。
+產品若 bundle 此模型，需附 `NOTICE` 檔案：
+
+```
+Momentry
+Copyright 2026 Accusys
+
+This product includes software developed by IDEA Research:
+- Grounding DINO (https://github.com/IDEA-Research/GroundingDINO)
+  Copyright 2023 IDEA Research
+  Licensed under Apache 2.0 (https://www.apache.org/licenses/LICENSE-2.0)
+```
@@ -1,442 +0,0 @@
-# People API 设计方案 (marcom 需求等效映射)
-
-**日期**: 2026-04-28  
-**状态**: 设计阶段  
-**目的**: 根据 marcom 团队需求，在符合现有架构的前提下提供等效 API
-
---
-
-## 设计原则
-
-1. **遵循 RESTful 规范**: 使用标准 HTTP 方法 (GET, POST, PATCH, DELETE)
-2. **统一路径前缀**: `/api/v1/people`
-3. **响应格式统一**: `{ success: bool, message: string, data: any }`
-4. **向后兼容**: 现有 API 保持不变，新 API 扩展功能
-5. **符合 Identity 系统**: 与 `identities` 表和 `identity_bindings` 表集成
-
---
-
-## API 对照表
-
-### 1. GET /people/candidates (候选人物)
-
-**marcom 需求**: 获取待确认的人物候选列表
-
-**等效 API**: 
-```
-GET /api/v1/people/candidates?file_uuid={uuid}&limit={n}
-```
-
-**功能**:
- 返回待确认的人物身份候选
- 包含 face cluster、speaker cluster 的匹配建议
- 状态: `pending`, `suggested`, `unmatched`
-
-**响应示例**:
-```json
-{
-  "success": true,
-  "message": "Found 15 candidates",
-  "data": {
-    "candidates": [
-      {
-        "candidate_id": "face_cluster_1",
-        "type": "face",
-        "suggested_identity": {
-          "id": 123,
-          "name": "张曼玉",
-          "confidence": 0.92
-        },
-        "appearance_count": 45,
-        "status": "pending"
-      }
-    ],
-    "total": 15
-  }
-}
-```
-
-**实现**: 扩展现有 `/api/v1/people/suggest`
-
---
-
-### 2. GET /people (人物列表)
-
-**marcom 需求**: 获取所有人物列表
-
-**等效 API**: 
-```
-GET /api/v1/people?file_uuid={uuid}&limit={n}&offset={n}&status={status}
-```
-
-**功能**:
- 返回人物身份列表
- 支持按 file_uuid 筛选
- 支持分页
- 支持按状态筛选 (confirmed, pending, all)
-
-**响应示例**:
-```json
-{
-  "success": true,
-  "message": "Found 8 persons",
-  "data": {
-    "persons": [
-      {
-        "identity_id": "Person_17",
-        "name": "张曼玉",
-        "appearance_count": 45,
-        "total_duration": 350.2,
-        "is_confirmed": true
-      }
-    ],
-    "total": 8
-  }
-}
-```
-
-**实现**: 现有 `/api/v1/people/list` 已支持
-
---
-
-### 3. GET /people/{identity_id} (人物详情)
-
-**marcom 需求**: 获取人物详情
-
-**等效 API**: 
-```
-GET /api/v1/people/{identity_id}?file_uuid={uuid}
-```
-
-**功能**:
- 返回人物详细信息
- 包含出场时间线
- 包含关联的 face/speaker
- 包含缩略图
-
-**响应示例**:
-```json
-{
-  "success": true,
-  "data": {
-    "identity_id": "Person_17",
-    "name": "张曼玉",
-    "face_identity_id": 123,
-    "speaker_id": "SPEAKER_00",
-    "appearance_count": 45,
-    "total_duration": 350.2,
-    "first_appearance_time": 10.5,
-    "last_appearance_time": 360.2,
-    "timeline": [...],
-    "thumbnails": [...]
-  }
-}
-```
-
-**实现**: 现有 `/api/v1/people/:person_id` 已支持
-
---
-
-### 4. POST /people (创建人物)
-
-**marcom 需求**: 手动创建新人物
-
-**等效 API**: 
-```
-POST /api/v1/people
-Body: { "name": "张曼玉", "file_uuid": "xxx", "metadata": {...} }
-```
-
-**功能**:
- 创建新人物身份
- 关联到指定视频
- 支持添加 metadata (角色名、演员名等)
-
-**响应示例**:
-```json
-{
-  "success": true,
-  "message": "Person created",
-  "data": {
-    "identity_id": "Person_99",
-    "name": "张曼玉",
-    "file_uuid": "xxx"
-  }
-}
-```
-
-**实现**: 需新增，参考 `CreatePersonIdentityRequest`
-
---
-
-### 5. PATCH /people/{identity_id} (更新人物)
-
-**marcom 需求**: 更新人物信息
-
-**等效 API**: 
-```
-PATCH /api/v1/people/{identity_id}
-Body: { "name": "新名字", "is_confirmed": true, "metadata": {...} }
-```
-
-**功能**:
- 更新人物名称
- 确认人物身份
- 更新 metadata
-
-**实现**: 现有 `/api/v1/people/:person_id` (PATCH) 已支持
-
---
-
-### 6. POST /people/merge (合并人物)
-
-**marcom 需求**: 合并多个人物为一个
-
-**等效 API**: 
-```
-POST /api/v1/people/merge
-Body: { 
-  "target_identity_id": "Person_17",
-  "source_identity_ids": ["Person_18", "Person_19"]
-}
-```
-
-**功能**:
- 合并多个人物身份
- 转移所有出场记录
- 更新统计数据
-
-**实现**: 现有 `/api/v1/people/merge` 已支持
-
---
-
-### 7. POST /people/skip (跳过人物)
-
-**marcom 需求**: 跳过某个候选人物（不处理）
-
-**等效 API**: 
-```
-POST /api/v1/people/skip
-Body: { "candidate_id": "face_cluster_2", "reason": "非人物" }
-```
-
-**功能**:
- 标记候选为"已跳过"
- 记录跳过原因
- 不创建人物身份
-
-**响应示例**:
-```json
-{
-  "success": true,
-  "message": "Candidate skipped",
-  "data": {
-    "candidate_id": "face_cluster_2",
-    "status": "skipped",
-    "reason": "非人物"
-  }
-}
-```
-
-**实现**: 需新增，扩展候选管理功能
-
---
-
-### 8. POST /people/{identity_id}/remove-face (移除人脸)
-
-**marcom 需求**: 从人物身份中移除特定人脸绑定
-
-**等效 API**: 
-```
-POST /api/v1/people/{identity_id}/unbind
-Body: { "binding_type": "face", "binding_value": "face_123" }
-```
-
-**功能**:
- 解绑人脸与人物身份的关联
- 人脸回到候选状态
- 更新人物出场统计
-
-**响应示例**:
-```json
-{
-  "success": true,
-  "message": "Face unbound",
-  "data": {
-    "identity_id": "Person_17",
-    "unbound_face": "face_123",
-    "updated_appearance_count": 42
-  }
-}
-```
-
-**实现**: 需新增，参考现有 `UnbindIdentityRequest`
-
---
-
-### 9. POST /people/split-face (分离人脸)
-
-**marcom 需求**: 将人脸从现有人物分离为新人物
-
-**等效 API**: 
-```
-POST /api/v1/people/split
-Body: { 
-  "source_identity_id": "Person_17",
-  "face_ids": ["face_123", "face_124"],
-  "new_identity_name": "新人物"
-}
-```
-
-**功能**:
- 从现有人物分离指定人脸
- 创建新人物身份
- 转移出场记录
-
-**实现**: 现有 `/api/v1/people/:person_id/split` 部分支持
-
---
-
-### 10. GET /people/{identity_id}/resolve (解决冲突)
-
-**marcom 需求**: 获取人物的冲突/歧义信息
-
-**等效 API**: 
-```
-GET /api/v1/people/{identity_id}/conflicts
-```
-
-**功能**:
- 返回人物身份的潜在冲突
- 显示相似人脸/声音的匹配
- 提供解决方案建议
-
-**响应示例**:
-```json
-{
-  "success": true,
-  "data": {
-    "identity_id": "Person_17",
-    "conflicts": [
-      {
-        "type": "similar_face",
-        "conflicting_identity": "Person_18",
-        "similarity": 0.85,
-        "suggestion": "merge"
-      }
-    ],
-    "resolution_options": ["merge", "keep_separate", "skip"]
-  }
-}
-```
-
-**实现**: 需新增
-
---
-
-### 11. POST /search (搜索)
-
-**marcom 需求**: 搜索人物
-
-**等效 API**: 
-```
-POST /api/v1/people/search
-Body: { 
-  "query": "张",
-  "filters": { "type": "people", "file_uuid": "xxx" },
-  "limit": 20
-}
-```
-
-**功能**:
- 搜索人物身份
- 支持按名称、类型、视频筛选
- 返回匹配结果
-
-**实现**: 现有 `/api/v1/identities/search` 已支持，建议扩展
-
---
-
-### 12. GET /people/status (人物状态)
-
-**marcom 需求**: 获取人物处理状态统计
-
-**等效 API**: 
-```
-GET /api/v1/people/status?file_uuid={uuid}
-```
-
-**功能**:
- 返回人物处理统计
- 待确认数量、已确认数量、跳过数量
- 合并历史
-
-**响应示例**:
-```json
-{
-  "success": true,
-  "data": {
-    "file_uuid": "xxx",
-    "total_candidates": 15,
-    "confirmed": 8,
-    "pending": 5,
-    "skipped": 2,
-    "merge_count": 3,
-    "split_count": 1
-  }
-}
-```
-
-**实现**: 需新增
-
---
-
-## 实现优先级
-
-| 优先级 | API | 状态 | 预估工时 |
-|--------|-----|------|----------|
-| **P0** | GET /people | ✅ 已有 | 0h |
-| **P0** | GET /people/{identity_id} | ✅ 已有 | 0h |
-| **P0** | PATCH /people/{identity_id} | ✅ 已有 | 0h |
-| **P0** | POST /people/merge | ✅ 已有 | 0h |
-| **P1** | GET /people/candidates | ⚠️ 扩展 | 2h |
-| **P1** | POST /people | ❌ 新增 | 2h |
-| **P1** | POST /people/search | ⚠️ 扩展 | 1h |
-| **P2** | POST /people/skip | ❌ 新增 | 2h |
-| **P2** | POST /people/{identity_id}/unbind | ❌ 新增 | 2h |
-| **P2** | POST /people/split | ⚠️ 扩展 | 1h |
-| **P2** | GET /people/{identity_id}/conflicts | ❌ 新增 | 3h |
-| **P2** | GET /people/status | ❌ 新增 | 2h |
-
-**总预估**: ~13h (P1+P2)
-
---
-
-## 数据库表需求
-
-现有表结构支持大部分需求，可能需要扩展：
-
-```sql
-- 建议新增: candidates 表 (候选管理)
-CREATE TABLE person_candidates (
-    id BIGSERIAL PRIMARY KEY,
-    file_uuid VARCHAR(36) NOT NULL,
-    candidate_type VARCHAR(20),  -- 'face', 'speaker'
-    candidate_id VARCHAR(50),    -- 'face_cluster_1', 'speaker_2'
-    suggested_identity_id BIGINT,
-    confidence FLOAT,
-    status VARCHAR(20),          -- 'pending', 'confirmed', 'skipped'
-    skip_reason TEXT,
-    created_at TIMESTAMP,
-    updated_at TIMESTAMP
-);
-```
-
---
-
-## 参考文档
-
- `docs_v1.0/ARCHITECTURE/MOMENTRY_CORE_ARCHITECTURE_V2.md` - Identity 系统设计
- `docs_v1.0/ARCHITECTURE/PERSON_IDENTITY_INTEGRATION.md` - Person Identity 整合
- `src/api/person_identity.rs` - 现有 API 实现
- `src/api/identity_binding.rs` - 身份绑定 API
@@ -1,699 +0,0 @@
-# Momentry Core API Documentation v1.0.0
-
-## Overview
-Momentry Core is a digital asset management system with video analysis, RAG, and face recognition capabilities. This document covers all API endpoints available in v1.0.0.
-
-**Base URL**: `http://<host>:<port>`
- Production: Port 3002
- Development (Playground): Port 3003
-
-**Authentication**: All protected routes require API key validation via `X-API-Key` header.
-
---
-
-## API Classification
-
-The API is organized into 7 categories:
-
-| Category | Prefix | Description |
-|----------|--------|-------------|
-| **Health & Auth** | `/health`, `/api/v1/auth` | System health, authentication |
-| **Asset Management** | `/api/v1/register`, `/api/v1/files`, `/api/v1/assets` | File registration, probing, processing |
-| **Search** | `/api/v1/search`, `/api/v1/n8n` | Text, hybrid, visual, and n8n search |
-| **Video Details** | `/api/v1/videos`, `/api/v1/progress` | Video listing, details, chunks |
-| **Identity & Binding** | `/api/v1/identities`, `/api/v1/signals` | Face/speaker identity management |
-| **Jobs & Rules** | `/api/v1/jobs`, `/api/v1/rules` | Processing job monitoring |
-| **Stats & Config** | `/api/v1/stats`, `/api/v1/config` | System statistics, configuration |
-
---
-
-## 1. Health & Authentication
-
-### `GET /health`
-Basic health check.
-
-**Response**:
-```json
-{
-  "status": "ok",
-  "version": "v1.0.0",
-  "uptime_ms": 12345
-}
-```
-
-### `GET /health/detailed`
-Detailed health check with service status (PostgreSQL, Redis, Qdrant, MongoDB).
-
-**Response**:
-```json
-{
-  "status": "ok",
-  "version": "v1.0.0",
-  "uptime_ms": 12345,
-  "services": {
-    "postgres": { "status": "ok", "latency_ms": 5 },
-    "redis": { "status": "ok", "latency_ms": 2 },
-    "qdrant": { "status": "ok", "latency_ms": 10 },
-    "mongodb": { "status": "ok", "latency_ms": 8 }
-  }
-}
-```
-
-### `POST /api/v1/auth/login`
-Authenticate and obtain API key.
-
-**Request**:
-```json
-{
-  "username": "demo",
-  "password": "demo"
-}
-```
-
-**Response**:
-```json
-{
-  "success": true,
-  "message": "Login successful",
-  "api_key": "muser_test_001",
-  "user": { "username": "demo" }
-}
-```
-
-### `POST /api/v1/auth/logout`
-Logout session.
-
-**Response**:
-```json
-{ "success": true }
-```
-
---
-
-## 2. Asset Management
-
-### `POST /api/v1/register`
-Register a video file (legacy path-based).
-
-**Request**:
-```json
-{ "path": "./demo/video.mp4" }
-```
-
-**Response**:
-```json
-{
-  "file_uuid": "384b0ff44aaaa1f1",
-  "file_id": 1,
-  "job_id": 1,
-  "file_name": "video.mp4",
-  "duration": 120.5,
-  "width": 1920,
-  "height": 1080,
-  "already_exists": false
-}
-```
-
-### `POST /api/v1/files/register`
-Register a file with full metadata (recommended). Supports move detection.
-
-**Request**:
-```json
-{
-  "file_path": "/Users/accusys/momentry/var/sftpgo/data/demo/video.mp4",
-  "user_id": null
-}
-```
-
-**Response**:
-```json
-{
-  "success": true,
-  "file_uuid": "384b0ff44aaaa1f1",
-  "file_name": "video.mp4",
-  "file_path": "/Users/accusys/momentry/var/sftpgo/data/demo/video.mp4",
-  "file_type": "video",
-  "duration": 120.5,
-  "width": 1920,
-  "height": 1080,
-  "fps": 30.0,
-  "total_frames": 3615,
-  "registration_time": null,
-  "already_exists": false,
-  "message": "File registered successfully"
-}
-```
-
-### `GET /api/v1/files/scan`
-Scan filesystem for unregistered files.
-
-### `POST /api/v1/unregister`
-Unregister a video file.
-
-**Request**:
-```json
-{ "uuid": "384b0ff44aaaa1f1" }
-```
-
-### `POST /api/v1/probe`
-Probe a video file for metadata.
-
-**Request**:
-```json
-{ "path": "./demo/video.mp4" }
-```
-
-**Response**:
-```json
-{
-  "uuid": "384b0ff44aaaa1f1",
-  "file_name": "video.mp4",
-  "duration": 120.5,
-  "width": 1920,
-  "height": 1080,
-  "fps": 30.0,
-  "cached": true,
-  "format": { ... },
-  "streams": [ ... ]
-}
-```
-
-### `GET /api/v1/assets/:uuid/probe`
-Probe a video by UUID.
-
-### `POST /api/v1/assets/:uuid/process`
-Trigger processing pipeline for an asset.
-
-**Request**:
-```json
-{
-  "processors": ["asr", "cut", "yolo", "ocr", "face", "pose", "asrx", "visual_chunk"]
-}
-```
-
-**Response**:
-```json
-{
-  "job_id": 1,
-  "asset_uuid": "384b0ff44aaaa1f1",
-  "status": "PENDING",
-  "message": "Processing triggered for video.mp4"
-}
-```
-
-### `GET /api/v1/assets/:uuid/status`
-Get asset processing status with frame progress.
-
-**Response**:
-```json
-{
-  "uuid": "384b0ff44aaaa1f1",
-  "file_name": "video.mp4",
-  "registration_time": "2026-04-30T10:00:00Z",
-  "processing_status": "processing",
-  "current_job_id": "abc-123",
-  "frame_progress": {
-    "total_frames": 3615,
-    "processed_frames": 1200,
-    "progress_percent": 33.2
-  }
-}
-```
-
---
-
-## 3. Search
-
-### `POST /api/v1/search`
-Vector/smart search across chunks.
-
-**Request**:
-```json
-{
-  "query": "person talking about AI",
-  "mode": "smart",
-  "uuid": "384b0ff44aaaa1f1",
-  "limit": 10
-}
-```
-
-**Response**:
-```json
-{
-  "results": [
-    {
-      "uuid": "384b0ff44aaaa1f1",
-      "chunk_id": "chunk_1",
-      "chunk_type": "sentence",
-      "start_time": 10.5,
-      "end_time": 15.2,
-      "text": "AI is transforming...",
-      "score": 0.85
-    }
-  ],
-  "query": "person talking about AI"
-}
-```
-
-### `POST /api/v1/search/hybrid`
-Hybrid search (vector + BM25).
-
-**Request**:
-```json
-{
-  "query": "search term",
-  "limit": 10,
-  "uuid": "384b0ff44aaaa1f1",
-  "vector_weight": 0.7,
-  "bm25_weight": 0.3
-}
-```
-
-### `POST /api/v1/search/bm25`
-BM25 full-text search.
-
-### `POST /api/v1/search/visual`
-Search visual chunks by criteria.
-
-**Request**:
-```json
-{
-  "uuid": "384b0ff44aaaa1f1",
-  "criteria": {
-    "object_class": "person",
-    "min_count": 1
-  }
-}
-```
-
-### `POST /api/v1/search/visual/class`
-Search by object class.
-
-**Request**:
-```json
-{
-  "uuid": "384b0ff44aaaa1f1",
-  "object_class": "person",
-  "min_count": 1,
-  "max_count": null
-}
-```
-
-### `POST /api/v1/search/visual/density`
-Search by object density.
-
-**Request**:
-```json
-{
-  "uuid": "384b0ff44aaaa1f1",
-  "min_density": 0.5,
-  "max_density": null
-}
-```
-
-### `POST /api/v1/search/visual/combination`
-Search by object combination.
-
-**Request**:
-```json
-{
-  "uuid": "384b0ff44aaaa1f1",
-  "combination": [["person", 2], ["car", 1]]
-}
-```
-
-### `POST /api/v1/search/visual/stats`
-Get visual chunk statistics.
-
-**Request**:
-```json
-{ "uuid": "384b0ff44aaaa1f1" }
-```
-
-### `POST /api/v1/n8n/search`
-Search via n8n integration.
-
-### `POST /api/v1/n8n/search/bm25`
-BM25 search via n8n.
-
-### `POST /api/v1/n8n/search/hybrid`
-Hybrid search via n8n.
-
-### `POST /api/v1/n8n/search/smart`
-Smart search via n8n.
-
---
-
-## 4. Video Details
-
-### `GET /api/v1/videos`
-List all registered videos with pagination.
-
-**Query Parameters**:
- `page`: Page number (default: 1)
- `page_size`: Items per page (default: 20)
- `status`: Filter by status
- `q`: Search query
- `uuid`: Filter by UUID
-
-**Response**:
-```json
-{
-  "files": [
-    {
-      "file_uuid": "384b0ff44aaaa1f1",
-      "file_path": "/path/to/video.mp4",
-      "file_name": "video.mp4",
-      "file_type": "video",
-      "duration": 120.5,
-      "width": 1920,
-      "height": 1080,
-      "status": "completed",
-      "created_at": "2026-04-30T10:00:00Z",
-      "file_size": 52428800,
-      "total_frames": 3615
-    }
-  ],
-  "count": 1,
-  "page": 1,
-  "page_size": 20
-}
-```
-
-### `DELETE /api/v1/videos/:uuid`
-Delete a video and all associated data (faces, chunks, processor results).
-
-**Response**:
-```json
-{
-  "success": true,
-  "message": "File 384b0ff44aaaa1f1 unregistered successfully...",
-  "file_uuid": "384b0ff44aaaa1f1",
-  "deleted_face_detections": 150,
-  "deleted_processor_results": 8,
-  "deleted_chunks": 45
-}
-```
-
-### `GET /api/v1/videos/:uuid/details`
-Get detailed chunk information.
-
-**Query Parameters**:
- `chunk_id`: Specific chunk ID (required)
- `parent_id`: Parent chunk ID
-
-**Response**:
-```json
-{
-  "uuid": "384b0ff44aaaa1f1",
-  "chunk_id": "chunk_1",
-  "chunk_type": "sentence",
-  "frame_range": {
-    "start_frame": 315,
-    "end_frame": 456,
-    "duration_frames": 141,
-    "fps": 30.0
-  },
-  "reference_time": {
-    "start": 10.5,
-    "end": 15.2
-  },
-  "text_content": "AI is transforming...",
-  "summary_text": "Discussion about AI impact",
-  "speaker_ids": ["SPEAKER_0"],
-  "person_ids": ["face_100"]
-}
-```
-
-### `GET /api/v1/videos/:uuid/pre_chunks`
-List pre-processor chunks.
-
-**Query Parameters**:
- `processor_type`: Filter by processor (asr, yolo, face, etc.)
- `page`: Page number
- `page_size`: Items per page
-
-### `GET /api/v1/progress/:uuid`
-Get processing progress for a video.
-
---
-
-## 5. Identity & Binding
-
-### `POST /api/v1/identities/from-face`
-Register a global identity from face.json with multi-angle reference vectors.
-
-**Request**:
-```json
-{
-  "face_json_path": "/path/to/face.json",
-  "identity_name": "John Doe",
-  "schema": "dev"
-}
-```
-
-### `POST /api/v1/identities/from-person`
-Register identity from a person in a video.
-
-**Request**:
-```json
-{
-  "file_uuid": "384b0ff44aaaa1f1",
-  "person_id": "person_1",
-  "identity_name": "John Doe"
-}
-```
-
-### `GET /api/v1/identities`
-List all global identities.
-
-**Query Parameters**:
- `page`: Page number
- `page_size`: Items per page
-
-### `GET /api/v1/faces/candidates`
-List unbound face candidates.
-
-**Query Parameters**:
- `file_uuid`: Filter by file
- `min_confidence`: Minimum confidence (default: 0.5)
- `page`, `page_size`: Pagination
-
-### `GET /api/v1/identities/:identity_id/faces`
-Get all faces for an identity.
-
-### `GET /api/v1/faces/:face_id/thumbnail`
-Get face thumbnail image (JPEG).
-
-### `POST /api/v1/identities/bind`
-Bind a face/speaker to an identity.
-
-**Request**:
-```json
-{
-  "identity_id": 1,
-  "binding_type": "face",
-  "binding_value": "face_100",
-  "source": "manual"
-}
-```
-
-### `POST /api/v1/identities/unbind`
-Unbind an identity.
-
-**Request**:
-```json
-{
-  "binding_type": "face",
-  "binding_value": "face_100"
-}
-```
-
-### `GET /api/v1/identity/:binding_type/:binding_value`
-Get identity info by binding.
-
-### `GET /api/v1/signals/unbound`
-List unbound signals.
-
-**Query Parameters**:
- `uuid`: File UUID
- `binding_type`: "face" or "speaker"
-
-### `GET /api/v1/signals/:uuid/:binding_type/:binding_value/timeline`
-Get signal timeline (all chunks for a face/speaker).
-
-### `POST /api/v1/identities/suggest-av`
-Suggest audio-visual bindings based on temporal overlap.
-
-**Request**:
-```json
-{
-  "file_uuid": "384b0ff44aaaa1f1",
-  "overlap_threshold": 0.6
-}
-```
-
---
-
-## 6. Jobs & Rules
-
-### `GET /api/v1/jobs`
-List all monitor jobs.
-
-**Query Parameters**:
- `page`, `page_size`: Pagination
- `status`: Filter by status
-
-### `GET /api/v1/jobs/:job_id`
-Get job details with processor information.
-
-**Response**:
-```json
-{
-  "job_id": "1",
-  "asset_uuid": "384b0ff44aaaa1f1",
-  "rule": "default",
-  "status": "RUNNING",
-  "current_processor_id": "asr",
-  "frame_progress": {
-    "total_frames": 3615,
-    "processed_frames": 1200,
-    "progress_percent": 33.2
-  }
-}
-```
-
-### `GET /api/v1/rules/:rule/status`
-Get rule status with active jobs.
-
---
-
-## 7. Stats & Configuration
-
-### `GET /api/v1/stats/ingest`
-Get ingestion statistics.
-
-**Response**:
-```json
-{
-  "total_videos": 50,
-  "total_chunks": 1200,
-  "sentence_chunks": 800,
-  "cut_chunks": 300,
-  "time_chunks": 100,
-  "searchable_chunks": 1150,
-  "chunks_with_visual": 450,
-  "chunks_with_summary": 200,
-  "pending_videos": 5
-}
-```
-
-### `GET /api/v1/stats/sftpgo`
-Get SFTPGo status and registered videos.
-
-### `GET /api/v1/stats/inference`
-Check inference engine health (Ollama, llama-server).
-
-**Response**:
-```json
-{
-  "ollama": {
-    "engine": "Ollama",
-    "model": "nomic-embed-text",
-    "status": "ok",
-    "latency_ms": 15
-  },
-  "llama_server": {
-    "engine": "llama-server",
-    "model": "gemma4_e4b_q5",
-    "status": "ok",
-    "latency_ms": 25
-  }
-}
-```
-
-### `POST /api/v1/config/cache`
-Toggle MongoDB cache.
-
-**Request**:
-```json
-{ "enabled": false }
-```
-
-**Response**:
-```json
-{
-  "success": true,
-  "cache_enabled": false,
-  "message": "Cache disabled"
-}
-```
-
---
-
-## API Usage Patterns
-
-### 1. List Pattern
-```
-GET /api/v1/videos?page=1&page_size=20
-```
- Supports pagination
- Optional filters via query parameters
- Returns `{ items: [...], count, page, page_size }`
-
-### 2. Detail Pattern
-```
-GET /api/v1/videos/:uuid/details?chunk_id=chunk_1
-```
- Path parameter for resource identifier
- Query parameters for sub-resource selection
- Returns detailed object with nested structures
-
-### 3. Operation Pattern
-```
-POST /api/v1/assets/:uuid/process
-```
- Action-oriented endpoint
- Request body contains operation parameters
- Returns operation status and job ID
-
-### 4. Application Pattern
-```
-POST /api/v1/identities/bind
-POST /api/v1/identities/suggest-av
-```
- Complex workflows with multiple steps
- Often involve external services (Python scripts, FFmpeg)
- Return comprehensive results with metadata
-
---
-
-## Error Responses
-
-| Status Code | Description |
-|-------------|-------------|
-| `400` | Bad Request - Invalid parameters |
-| `404` | Not Found - Resource doesn't exist |
-| `500` | Internal Server Error - Database/service failure |
-
---
-
-## V4.0 Architecture Notes
-
-### Key Changes from V3.x
- `video_uuid` → `file_uuid` (terminology update)
- `person_identities` table **removed**
- Face → Identity direct binding (no intermediate person_id)
- 28 person_id APIs removed (except register/bind)
- Chunk binding auto via time alignment
-
-### Identity Model
-```
-Face Detection → Identity (direct binding)
-Speaker Detection → Identity (direct binding)
-```
-
-### Processing Pipeline
-```
-Register → Probe → ASR → CUT → YOLO → OCR → Face → Pose → ASRX → Visual Chunk
-```
@@ -0,0 +1,177 @@
+# API Dictionary v1.0.0
+
+58 endpoints across 10 modules. Auth: `X-API-Key` header or `Authorization: Bearer <key>`.
+
+## API Design Principle
+
+Every path segment after the resource ID is a **verb** — an action on that resource.
+
+```
+/api/v1/{entity}/{id}/{action}
+            ↑        ↑      ↑
+           實體      ID    動作
+```
+
+**Primary entities**: `file`/`files`, `identity`/`identities`
+
+```
+/api/v1/file/:file_uuid       ← 檔案資源
+  /video                      → 播放影片（動詞）
+  /video/bbox                 → 播放含框（動詞）
+  /thumbnail                  → 取縮圖（動詞）
+  /process                    → 啟動處理（動詞）
+  /probe                      → 探測（動詞）
+  /chunks                     → 列出段落（動詞）
+  /identities                 → 列出身分（動詞）
+  /face_trace/sortby          → 列出追蹤/排序（動詞）
+  /trace/:trace_id/faces      → 列出偵測（動詞）
+
+/api/v1/identity/:identity_uuid
+  /bind                       → 綁定（動詞）
+  /unbind                     → 解綁（動詞）
+  /files                      → 列出檔案（動詞）
+  /chunks                     → 列出段落（動詞）
+
+/api/v1/search/universal      → 搜尋（動詞）
+/api/v1/search/smart          → 智慧搜尋（動詞）
+```
+
+**Naming conventions**:
+- 全域唯一資源 ID → `uuid`（`file_uuid`, `identity_uuid`）
+- 單一實體下唯一 ID → `id`（`trace_id`, `chunk_id`, `face_id`）
+- 路徑尾端 → 動詞（`/video`, `/chunks`, `/bind`）
+- 集合列表 → **複數**（`/files`, `/identities`, `/resources`, `/faces`）
+- 單一資源操作 → **單數**（`/file/:file_uuid`, `/identity/:identity_uuid`）
+
+## Legend
+
+- `→` direction of data flow
+- `POST` typically requires JSON body
+- All endpoints return JSON unless noted
+
+---
+
+| # | Method | Route | Description |
+|---|--------|-------|-------------|
+| 1 | GET | `/health` | Server health (ok/degraded) |
+| 2 | GET | `/health/detailed` | Per-service health + latency |
+| 3 | POST | `/api/v1/auth/login` | Username/password → API key |
+| 4 | POST | `/api/v1/auth/logout` | Invalidate session |
+| 5 | GET | `/api/v1/stats/ingest` | Ingest statistics |
+| 6 | GET | `/api/v1/stats/sftpgo` | SFTPGo service status |
+| 7 | GET | `/api/v1/stats/inference` | LLM/embedding health |
+| 8 | POST | `/api/v1/files/register` | Register video file → file_uuid |
+| 9 | POST | `/api/v1/unregister` | Unregister file(s): by `file_uuid` or pattern match on `file_path`+`pattern` |
+| 10 | GET | `/api/v1/files/scan` | Scan directory for new files |
+| 11 | GET | `/api/v1/file/:file_uuid/probe` | ffprobe metadata |
+| 12 | POST | `/api/v1/file/:file_uuid/process` | Start processing pipeline |
+| 13 | GET | `/api/v1/file/:file_uuid/chunks` | List pre-chunks for file |
+| 14 | GET | `/api/v1/progress/:file_uuid` | Processing progress |
+| 15 | GET | `/api/v1/jobs` | List monitor jobs (filterable by status) |
+| 16 | POST | `/api/v1/config/cache` | Toggle Redis cache |
+| 17 | POST | `/api/v1/config/auto-pipeline` | Toggle auto-pipeline on register |
+| 18 | POST | `/api/v1/config/watcher-auto-register` | Toggle watcher auto-register |
+| 17 | POST | `/api/v1/search/visual` | Search visual chunks |
+| 18 | POST | `/api/v1/search/visual/class` | Search by object class |
+| 19 | POST | `/api/v1/search/visual/density` | Search by spatial density |
+| 20 | POST | `/api/v1/search/visual/combination` | Combined visual search |
+| 21 | POST | `/api/v1/search/visual/stats` | Visual chunk statistics |
+
+## File/Identity (identity_api.rs)
+
+| # | Method | Route | Description |
+|---|--------|-------|-------------|
+| 22 | GET | `/api/v1/files` | List registered files (paginated) |
+| 23 | GET | `/api/v1/file/:file_uuid` | Single file detail |
+| 24 | GET | `/api/v1/file/:file_uuid/identities` | Identities in this file |
+| 25 | GET | `/api/v1/identities` | List all identities |
+| 26 | POST | `/api/v1/identity` | Register new identity |
+| 27 | GET | `/api/v1/identity/:identity_uuid` | Identity detail |
+| 28 | DELETE | `/api/v1/identity/:identity_uuid` | Delete identity |
+| 29 | GET | `/api/v1/identity/:identity_uuid/files` | Files for an identity |
+| 30 | GET | `/api/v1/identity/:identity_uuid/chunks` | Chunks for an identity |
+| 31 | POST | `/api/v1/resource/register` | Register processing resource |
+| 32 | POST | `/api/v1/resource/heartbeat` | Resource heartbeat |
+| 33 | GET | `/api/v1/resources` | List all resources |
+
+## Identity Binding (identity_binding.rs)
+
+| # | Method | Route | Description |
+|---|--------|-------|-------------|
+| 34 | POST | `/api/v1/identity/:identity_uuid/bind` | Bind face → identity |
+| 35 | POST | `/api/v1/identity/:identity_uuid/unbind` | Unbind face from identity |
+| 36 | POST | `/api/v1/identity/:identity_uuid/mergeinto` | Merge identity :identity_uuid → target |
+
+## Face Candidates (identities.rs)
+
+| # | Method | Route | Description |
+|---|--------|-------|-------------|
+| 37 | GET | `/api/v1/faces/candidates` | Unbound face gallery (paginated) |
+
+## Search (search.rs + universal_search.rs)
+
+| # | Method | Route | Description |
+|---|--------|-------|-------------|
+| 38 | POST | `/api/v1/search/smart` | Semantic search (EmbeddingGemma + pgvector) |
+| 39 | POST | `/api/v1/search/universal` | BM25 keyword search (requires file_uuid) |
+| 40 | POST | `/api/v1/search/frames` | Frame-level search |
+
+## Trace (trace_agent_api.rs)
+
+| # | Method | Route | Description |
+|---|--------|-------|-------------|
+| 41 | POST | `/api/v1/file/:file_uuid/face_trace/sortby` | List traces (sorted/filtered) |
+| 42 | GET | `/api/v1/file/:file_uuid/trace/:trace_id/faces` | Single trace detections + interpolation |
+
+## Media (media_api.rs)
+
+| # | Method | Route | Description |
+|---|--------|-------|-------------|
+| 43 | GET | `/api/v1/file/:file_uuid/thumbnail` | Frame JPEG (optional crop via `?frame=&x=&y=&w=&h=`) |
+| 44 | GET | `/api/v1/file/:file_uuid/video` | Raw video stream (`?start_time=&end_time=` in seconds) |
+| 45 | GET | `/api/v1/file/:file_uuid/video/bbox` | Bbox overlay video (`?start_frame=&end_frame=&duration=` frame numbers) |
+| 46 | GET | `/api/v1/file/:file_uuid/trace/:trace_id/video` | Trace clip (`?mode=normal\|debug&padding=`) |
+
+## Identity Delete
+
+| # | Method | Route | Description |
+|---|--------|-------|-------------|
+| 47 | DELETE | `/api/v1/identity/:identity_uuid` | Delete identity + unbind all faces |
+
+## Agents (agent_api.rs + five_w1h_agent_api.rs + identity_agent_api.rs)
+
+| # | Method | Route | Description |
+|---|--------|-------|-------------|
+| 48 | POST | `/api/v1/agents/translate` | AI text translation |
+| 49 | POST | `/api/v1/agents/5w1h/analyze` | Single chunk 5W1H analysis |
+| 50 | POST | `/api/v1/agents/5w1h/batch` | Batch 5W1H analysis |
+| 51 | GET | `/api/v1/agents/5w1h/status` | 5W1H job status |
+| 52 | POST | `/api/v1/agents/identity/analyze` | Identity analysis |
+| 53 | GET | `/api/v1/agents/identity/status` | Identity job status |
+| 54 | POST | `/api/v1/agents/identity/suggest` | Identity suggestions |
+| 55 | POST | `/api/v1/agents/suggest/merge` | Suggest identity merge |
+| 56 | POST | `/api/v1/agents/suggest/clustering` | Suggest re-clustering |
+
+## Identity Search (identity_api.rs, new in V4.1)
+
+| # | Method | Route | Description |
+|---|--------|-------|-------------|
+| 57 | GET | `/api/v1/identities/search?q=` | Search identities by name → chunk results |
+| 58 | GET | `/api/v1/search/identity_text?q=&file_uuid=` | Full-text search → identity-bound chunks |
+
+---
+
+## Summary
+
+| Module | Routes | File |
+|--------|--------|------|
+| Core | 21 | `server.rs` |
+| File/Identity | 14 | `identity_api.rs` (+2 search endpoints) |
+| Binding | 3 | `identity_binding.rs` |
+| Faces | 1 | `identities.rs` |
+| Search | 3 | `search.rs`, `universal_search.rs` |
+| Trace | 2 | `trace_agent_api.rs` |
+| Media | 4 | `media_api.rs` |
+| Identity Delete | 1 | `identity_api.rs` |
+| Agents | 9 | `agent_api.rs`, `five_w1h_agent_api.rs`, `identity_agent_api.rs` |
+| **Total** | **58** | |
@@ -0,0 +1,381 @@
+ ---
+document_type: "reference_doc"
+service: "MOMENTRY_CORE"
+title: "Momentry Core Release API Reference v1.0.0"
+date: "2026-05-25"
+version: "V4.2"
+status: "active"
+owner: "Warren"
+---
+
+# Momentry Core API Reference v1.0.0
+
+55 endpoints across 10 categories, with real curl examples and responses.
+
+## Base
+
+| Environment | URL |
+|-------------|-----|
+| Production | `http://localhost:3002` or `https://api.momentry.ddns.net` |
+| Development | `http://localhost:3003` |
+| Auth | Header `X-API-Key: <key>` (login endpoint unprotected) |
+
+> **Note**: All examples below use production port 3002. For dev testing, replace `3002` with `3003`.
+
+---
+
+## 1. System
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 1 | GET | `/health` | Server status (ok/degraded) |
+| 2 | GET | `/health/detailed` | Per-service health + latency |
+| 3 | GET | `/health/consistency` | Data consistency check |
+| 4 | POST | `/api/v1/auth/login` | Username/password → API key |
+| 5 | POST | `/api/v1/auth/logout` | Invalidate session |
+| 6 | GET | `/api/v1/stats/sftpgo` | SFTPGo status |
+| 7 | POST | `/api/v1/config/cache` | Toggle Redis cache |
+| 8 | POST | `/api/v1/config/auto-pipeline` | Toggle auto-pipeline on register |
+| 9 | POST | `/api/v1/config/watcher-auto-register` | Toggle watcher auto-register |
+
+```bash
+curl http://localhost:3002/health
+```
+```json
+{
+  "status": "ok",
+  "version": "1.0.0",
+  "build_git_hash": "de88fd4e",
+  "build_timestamp": "2026-05-25",
+  "uptime_ms": 7052517
+}
+```
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 2a | GET | `/health/detailed` | Per-service health + resources + pipeline |
+
+```bash
+curl -X POST http://localhost:3002/api/v1/files/register \
+  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69" \
+  -H "Content-Type: application/json" \
+  -d '{"file_path":"/path/to/video.mp4","content_hash":"optional-sha256-of-file"}'
+```
+```json
+{"success":true,"file_uuid":"3abeee81d94597629ed8cb943f182e94","duration":5954.0}
+```
+
+Supports all file types (video, image, document, audio). SHA256 content_hash computed automatically if not provided.
+```json
+{
+  "status": "ok",
+  "build_git_hash": "de88fd4e",
+  "build_timestamp": "2026-05-25",
+  "services": {
+    "postgres": {"status": "ok", "latency_ms": 6},
+    "redis":    {"status": "ok", "latency_ms": 0},
+    "qdrant":   {"status": "ok", "latency_ms": 1},
+    "mongodb":  {"status": "ok", "latency_ms": 0}
+  },
+  "resources": {
+    "cpu_used_percent": 50.0,
+    "cpu_idle_percent": 50.0,
+    "memory_available_mb": 8028,
+    "memory_total_mb": 16384,
+    "memory_used_percent": 51.0,
+    "gpu_available": false,
+    "gpu_utilization": null,
+    "gpu_memory_used_pct": null
+  },
+  "pipeline": {
+    "scripts": true,
+    "models": true,
+    "ffmpeg": true,
+    "embedding_server": {"status": "ok", "latency_ms": 0},
+    "gdino_api":        {"status": "error", "latency_ms": 0, "error": "..."},
+    "llm":              {"status": "ok", "latency_ms": 0}
+  }
+}
+```
+
+---
+
+## 2. File Management
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 10 | POST | `/api/v1/files/register` | Register file → file_uuid. Body: `{"file_path":"...", "content_hash":"optional"}` |
+| 11 | GET | `/api/v1/files/lookup?file_name=` | Pre-upload name conflict check. Returns matches + `next_name` for auto-rename |
+| 12 | POST | `/api/v1/unregister` | Unregister file(s): by `file_uuid` or pattern match (`file_path`+`pattern`) |
+| 13 | GET | `/api/v1/files/scan` | Scan directory for new files |
+| 14 | GET | `/api/v1/files` | List files (paginated) |
+| 15 | GET | `/api/v1/file/:file_uuid` | Single file detail |
+| 16 | GET | `/api/v1/file/:file_uuid/probe` | ffprobe metadata |
+| 17 | POST | `/api/v1/file/:file_uuid/process` | Start pipeline |
+| 18 | POST | `/api/v1/file/:file_uuid/chunk/:chunk_id` | Single chunk detail (V1.0.2+) |
+| 19 | POST | `/api/v1/progress/:file_uuid` | Processing progress |
+| 20 | POST | `/api/v1/jobs` | Monitor jobs (filterable) |
+
+```bash
+curl -X POST http://localhost:3002/api/v1/files/register  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"  -H "Content-Type: application/json"  -d '{"file_path":"/Users/accusys/momentry/var/sftpgo/data/demo/video.mp4"}'
+```
+```json
+{"success":true,"file_uuid":"3abeee81d94597629ed8cb943f182e94","duration":5954.0}
+```
+
+Modes:
+- By `file_uuid`: unregister a single file
+- By `file_path` + `pattern` regex: unregister all matching files in a directory
+
+```bash
+# By file_uuid
+curl -X POST http://localhost:3002/api/v1/unregister \
+  -H "X-API-Key: muser_..." -H "Content-Type: application/json" \
+  -d '{"file_uuid":"53e3a229bf68878b7a799e811e097f9c"}'
+
+# By pattern (unregister all .mp4 files in directory)
+curl -X POST http://localhost:3002/api/v1/unregister \
+  -H "X-API-Key: muser_..." -H "Content-Type: application/json" \
+  -d '{"file_path":"/data/demo","pattern":"\\.mp4$"}'
+```
+```json
+{"success":true,"file_uuid":"53e3a229bf68878b7a799e811e097f9c","message":"File unregistered successfully"}
+```
+
+```bash
+curl "http://localhost:3002/api/v1/files?page=1&page_size=2" -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+```
+```json
+{"success":true,"data":[{"file_uuid":"aeed7134...","file_name":"Charade (1963)...","status":"ready"}],"total":0,"page":1,"page_size":2}
+```
+
+---
+
+## 3. Search
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 21 | POST | `/api/v1/search/visual` | Visual chunk search |
+| 22 | POST | `/api/v1/search/visual/class` | By object class |
+| 23 | POST | `/api/v1/search/visual/density` | By spatial density |
+| 24 | POST | `/api/v1/search/visual/combination` | Combined visual search |
+| 25 | POST | `/api/v1/search/visual/stats` | Visual stats |
+| 26 | POST | `/api/v1/search/smart` | Semantic (EmbeddingGemma + pgvector) |
+| 27 | POST | `/api/v1/search/universal` | BM25 keyword (requires file_uuid) |
+| 28 | POST | `/api/v1/search/frames` | Frame-level search |
+
+```bash
+curl -X POST http://localhost:3002/api/v1/search/universal  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"  -H "Content-Type: application/json"  -d '{"query":"name","limit":2,"mode":"bm25","file_uuid":"3abeee81d94597629ed8cb943f182e94"}'
+```
+```json
+{"query":"name","results":[{"chunk_id":"100","text":"What's your name?","start_time":258.68,"score":0.90}],"total":5,"page":1,"page_size":20,"took_ms":42}
+```
+
+```bash
+curl -X POST http://localhost:3002/api/v1/search/universal  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"  -H "Content-Type: application/json"  -d '{"query":"friends","limit":2,"mode":"bm25","file_uuid":"3abeee81d94597629ed8cb943f182e94"}'
+```
+```json
+{"query":"friends","results":[{"chunk_id":"104","text":"You won't find it difficult to make some new friends.","start_time":272.38,"score":0.90}],"total":3,"page":1,"page_size":20,"took_ms":38}
+```
+
+---
+
+## 4. Face Trace
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 29 | POST | `/api/v1/file/:file_uuid/traces` | List traces (sorted/filtered) |
+| 30 | GET | `/api/v1/file/:file_uuid/trace/:trace_id/faces` | Trace detections (+ interpolation) |
+
+### traces — list traces
+
+Parameters:
+- `sort_by`: `face_count` | `duration` | `first_appearance`
+- `min_faces`, `min_confidence`, `max_confidence`: filters
+- `limit`: max results
+
+```bash
+curl -X POST "http://localhost:3002/api/v1/file/3abeee81d94597629ed8cb943f182e94/traces"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"  -H "Content-Type: application/json"  -d '{"sort_by":"face_count","limit":2}'
+```
+```json
+{"success":true,"total_traces":6892,"total_faces":108204,"traces":[
+  {"trace_id":3128,"face_count":1109,"avg_confidence":0.779},
+  {"trace_id":3126,"face_count":743,"avg_confidence":0.758}
+]}
+```
+
+### trace/:trace_id/faces — individual detections
+
+Parameters:
+- `limit`, `offset`: pagination
+- `interpolate`: boolean (fills sparse gaps with lerp bbox)
+
+```bash
+curl "http://localhost:3002/api/v1/file/3abeee81d94597629ed8cb943f182e94/trace/2/faces?limit=2&interpolate=true"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+```
+```json
+{"success":true,"trace_id":2,"fps":25.0,"total":1,"faces":[
+  {"id":12399,"start_frame":4620,"end_frame":4620,"start_time":184.8,"end_time":184.8,"x":787,"y":582,"width":225,"height":225,"confidence":0.666,"interpolated":false}
+]}
+```
+
+---
+
+## 5. Media
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 31 | GET | `/api/v1/file/:file_uuid/thumbnail` | Frame JPEG (?frame=&x=&y=&w=&h=) |
+| 32 | GET | `/api/v1/file/:file_uuid/video` | Raw video stream. Dual input: `?start_time=&end_time=` (seconds) or `?start_frame=&end_frame=` (frames). |
+| 33 | GET | `/api/v1/file/:file_uuid/video/bbox` | Bbox overlay. `?start_frame=&end_frame=&face_uuid=&duration=` (all frame numbers). Dual input via `start_time`/`end_time`. |
+| 34 | GET | `/api/v1/file/:file_uuid/trace/:trace_id/video` | Trace clip (?mode=&padding=&audio=) |
+
+All video endpoints support:
+- `mode=normal|debug` (default: `normal`)
+- `audio=on|off` (default: `on`)
+
+`mode=normal`: raw clip, `-c copy`, no overlay.
+`mode=debug`: re-encoded with top-left text info + green bboxes (trace labels at actual frames with thickness=4, interpolated at first known position with thickness=1).
+
+```bash
+# Normal mode
+curl -o trace.mp4 "http://localhost:3002/api/v1/file/{file_uuid}/trace/42/video?mode=normal"
+# Debug mode
+curl -o trace_debug.mp4 "http://localhost:3002/api/v1/file/{file_uuid}/trace/42/video?mode=debug"
+```
+
+Debug overlay shows at bottom-left:
+```
+Frame {n}  {pts}s
+Cut: {id}
+{file_uuid}
+Trace {id}: start={frame}  {name}
+...
+```
+
+Green bbox per face detection: actual frames `thickness=4`, interpolated `thickness=1`.
+
+---
+
+## 6. Identities
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 35 | GET | `/api/v1/identities` | List all identities |
+| 36 | GET | `/api/v1/file/:file_uuid/identities` | Identities in a file |
+| 37 | POST | `/api/v1/identity` | Register new identity |
+| 38 | GET | `/api/v1/identity/:identity_uuid` | Identity detail |
+| 39 | DELETE | `/api/v1/identity/:identity_uuid` | Delete identity |
+| 40 | GET | `/api/v1/identity/:identity_uuid/files` | Files for identity |
+| 41 | GET | `/api/v1/identity/:identity_uuid/chunks` | Chunks for identity |
+| 42 | GET | `/api/v1/faces/candidates` | Unbound face gallery |
+| 43 | GET | `/api/v1/identities/search?q=` | Search identities by name → chunks |
+| 44 | GET | `/api/v1/search/identity_text?q=&file_uuid=` | Full-text search → identity-bound chunks |
+
+```bash
+curl "http://localhost:3002/api/v1/identities?page=1&page_size=3"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+```
+```json
+{"count":3852,"page":1,"page_size":3,"identities":[
+  {"id":18299,"identity_uuid":"76f85ee6-bc47-4a1a-9878-1beb67851ec5","name":"PERSON_aeed7134_390","metadata":{}},
+  {"id":18298,"identity_uuid":"f4d4ccbf-fccb-4f62-8806-2b7f4a706edb","name":"PERSON_aeed7134_389","metadata":{}},
+  {"id":18297,"identity_uuid":"e8a1b2c3-d4e5-4f67-8901-23456789abcd","name":"PERSON_aeed7134_388","metadata":{}}
+]}
+```
+
+### GET /api/v1/file/:file_uuid/identities — identities with frame/time ranges
+
+```bash
+curl "http://localhost:3002/api/v1/file/aeed71342a899fe4b4c57b7d41bcb692/identities?limit=2"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+```
+```json
+{"success":true,"file_uuid":"aeed71342a899fe4b4c57b7d41bcb692","fps":25.0,"total":20,"page":1,"page_size":20,"data":[
+  {"identity_id":18276,"identity_uuid":"77d895cc-bc2e-4f5a-84b3-3c1f0e2a5b6a","name":"PERSON_aeed7134_367","face_count":86,"start_frame":150744,"end_frame":152895,"start_time":6029.76,"end_time":6115.8,"confidence":0.855},
+  {"identity_id":18179,"identity_uuid":"90fc04cd-003b-4a1b-9f7d-8c3e1d2f4a5b","name":"PERSON_aeed7134_270","face_count":13,"start_frame":77418,"end_frame":77454,"start_time":3096.72,"end_time":3098.16,"confidence":0.851}
+]}
+```
+
+```bash
+curl "http://localhost:3002/api/v1/faces/candidates?page=1&page_size=2"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+```
+```json
+{"total":42,"candidates":[{"frame_number":30,"confidence":0.85},...]}
+```
+
+---
+
+## 7. Identity Binding
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 45 | POST | `/api/v1/identity/:identity_uuid/bind` | Bind face → identity |
+| 46 | POST | `/api/v1/identity/:identity_uuid/unbind` | Unbind face from identity |
+| 47 | POST | `/api/v1/identity/:identity_uuid/mergeinto` | Merge into another identity |
+
+```bash
+curl -X POST "http://localhost:3002/api/v1/identity/a9a90105-6d6b-46ff-92da-0c3c1a57dff4/bind"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"  -H "Content-Type: application/json"  -d '{"file_uuid":"3abeee81d94597629ed8cb943f182e94","face_id":"face_42"}'
+```
+```json
+{"success":true}
+```
+
+---
+
+## 8. Resources
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 48 | POST | `/api/v1/resource/register` | Register processing resource |
+| 49 | POST | `/api/v1/resource/heartbeat` | Resource heartbeat |
+| 50 | GET | `/api/v1/resources` | List all resources |
+
+```bash
+curl "http://localhost:3002/api/v1/resources"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+```
+```json
+{"success":true,"data":[{"resource_id":"mxbai-embed-large-v1","resource_type":"embedding_model"}],"message":"OK"}
+```
+
+---
+
+## 9. Agents — 5W1H
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 51 | POST | `/api/v1/agents/translate` | AI text translation |
+| 52 | POST | `/api/v1/agents/5w1h/analyze` | Single chunk analysis |
+| 53 | POST | `/api/v1/agents/5w1h/batch` | Batch analysis |
+| 54 | GET | `/api/v1/agents/5w1h/status` | Job status |
+
+```bash
+curl -X POST "http://localhost:3002/api/v1/agents/translate"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"  -H "Content-Type: application/json"  -d '{"text":"Hello world","target_language":"zh-TW"}'
+```
+```json
+{"success":true,"translated_text":"你好世界"}
+```
+
+---
+
+## 10. Agents — Identity
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 55 | POST | `/api/v1/agents/identity/match-from-photo` | Match face from photo |
+| 56 | POST | `/api/v1/agents/identity/match-from-trace` | Match face from trace |
+| 57 | POST | `/api/v1/agents/suggest/merge` | Suggest merge |
+| 58 | POST | `/api/v1/agents/suggest/clustering` | Suggest re-clustering |
+
+---
+
+## Version History
+
+| Version | Date | Changes |
+|---------|------|---------|
+| V4.2 | 2026-05-25 | Removed phantom routes (stats/ingest, stats/inference, agents/identity/status); fixed HTTP methods (chunk, progress, jobs → POST); renamed endpoints (face_trace/sortby → traces, analyze → match-from-photo, suggest → match-from-trace); added config endpoints (consistency, auto-pipeline, watcher-auto-register); updated git hash to de88fd4e |
+| V4.1 | 2026-05-14 | Added `build_timestamp` + `resources` + `pipeline` to health APIs; identity search endpoints; trace debug rework (green bbox, text overlay, all traces listed) |
+
+## Related
+
+- `API_DICTIONARY_V1.0.0.md` — Quick reference (55 endpoints)
+- `API_DOCUMENTATION_v1.0.0.md` — Detailed spec with examples
+- `TRACE/TRACE_API_REFERENCE_V1.0.0.md` — Trace-specific reference
@@ -0,0 +1,381 @@
+ ---
+document_type: "reference_doc"
+service: "MOMENTRY_CORE"
+title: "Momentry Core Release API Reference v1.0.0"
+date: "2026-05-25"
+version: "V4.2"
+status: "active"
+owner: "Warren"
+---
+
+# Momentry Core API Reference v1.0.0
+
+55 endpoints across 10 categories, with real curl examples and responses.
+
+## Base
+
+| Environment | URL |
+|-------------|-----|
+| Production | `http://localhost:3002` or `https://api.momentry.ddns.net` |
+| Development | `http://localhost:3003` |
+| Auth | Header `X-API-Key: <key>` (login endpoint unprotected) |
+
+> **Note**: All examples below use production port 3002. For dev testing, replace `3002` with `3003`.
+
+---
+
+## 1. System
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 1 | GET | `/health` | Server status (ok/degraded) |
+| 2 | GET | `/health/detailed` | Per-service health + latency |
+| 3 | GET | `/health/consistency` | Data consistency check |
+| 4 | POST | `/api/v1/auth/login` | Username/password → API key |
+| 5 | POST | `/api/v1/auth/logout` | Invalidate session |
+| 6 | GET | `/api/v1/stats/sftpgo` | SFTPGo status |
+| 7 | POST | `/api/v1/config/cache` | Toggle Redis cache |
+| 8 | POST | `/api/v1/config/auto-pipeline` | Toggle auto-pipeline on register |
+| 9 | POST | `/api/v1/config/watcher-auto-register` | Toggle watcher auto-register |
+
+```bash
+curl http://localhost:3002/health
+```
+```json
+{
+  "status": "ok",
+  "version": "1.0.0",
+  "build_git_hash": "de88fd4e",
+  "build_timestamp": "2026-05-25",
+  "uptime_ms": 7052517
+}
+```
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 2a | GET | `/health/detailed` | Per-service health + resources + pipeline |
+
+```bash
+curl -X POST http://localhost:3002/api/v1/files/register \
+  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69" \
+  -H "Content-Type: application/json" \
+  -d '{"file_path":"/path/to/video.mp4","content_hash":"optional-sha256-of-file"}'
+```
+```json
+{"success":true,"file_uuid":"3abeee81d94597629ed8cb943f182e94","duration":5954.0}
+```
+
+Supports all file types (video, image, document, audio). SHA256 content_hash computed automatically if not provided.
+```json
+{
+  "status": "ok",
+  "build_git_hash": "de88fd4e",
+  "build_timestamp": "2026-05-25",
+  "services": {
+    "postgres": {"status": "ok", "latency_ms": 6},
+    "redis":    {"status": "ok", "latency_ms": 0},
+    "qdrant":   {"status": "ok", "latency_ms": 1},
+    "mongodb":  {"status": "ok", "latency_ms": 0}
+  },
+  "resources": {
+    "cpu_used_percent": 50.0,
+    "cpu_idle_percent": 50.0,
+    "memory_available_mb": 8028,
+    "memory_total_mb": 16384,
+    "memory_used_percent": 51.0,
+    "gpu_available": false,
+    "gpu_utilization": null,
+    "gpu_memory_used_pct": null
+  },
+  "pipeline": {
+    "scripts": true,
+    "models": true,
+    "ffmpeg": true,
+    "embedding_server": {"status": "ok", "latency_ms": 0},
+    "gdino_api":        {"status": "error", "latency_ms": 0, "error": "..."},
+    "llm":              {"status": "ok", "latency_ms": 0}
+  }
+}
+```
+
+---
+
+## 2. File Management
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 10 | POST | `/api/v1/files/register` | Register file → file_uuid. Body: `{"file_path":"...", "content_hash":"optional"}` |
+| 11 | GET | `/api/v1/files/lookup?file_name=` | Pre-upload name conflict check. Returns matches + `next_name` for auto-rename |
+| 12 | POST | `/api/v1/unregister` | Unregister file(s): by `file_uuid` or pattern match (`file_path`+`pattern`) |
+| 13 | GET | `/api/v1/files/scan` | Scan directory for new files |
+| 14 | GET | `/api/v1/files` | List files (paginated) |
+| 15 | GET | `/api/v1/file/:file_uuid` | Single file detail |
+| 16 | GET | `/api/v1/file/:file_uuid/probe` | ffprobe metadata |
+| 17 | POST | `/api/v1/file/:file_uuid/process` | Start pipeline |
+| 18 | POST | `/api/v1/file/:file_uuid/chunk/:chunk_id` | Single chunk detail (V1.0.2+) |
+| 19 | POST | `/api/v1/progress/:file_uuid` | Processing progress |
+| 20 | POST | `/api/v1/jobs` | Monitor jobs (filterable) |
+
+```bash
+curl -X POST http://localhost:3002/api/v1/files/register  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"  -H "Content-Type: application/json"  -d '{"file_path":"/Users/accusys/momentry/var/sftpgo/data/demo/video.mp4"}'
+```
+```json
+{"success":true,"file_uuid":"3abeee81d94597629ed8cb943f182e94","duration":5954.0}
+```
+
+Modes:
+- By `file_uuid`: unregister a single file
+- By `file_path` + `pattern` regex: unregister all matching files in a directory
+
+```bash
+# By file_uuid
+curl -X POST http://localhost:3002/api/v1/unregister \
+  -H "X-API-Key: muser_..." -H "Content-Type: application/json" \
+  -d '{"file_uuid":"53e3a229bf68878b7a799e811e097f9c"}'
+
+# By pattern (unregister all .mp4 files in directory)
+curl -X POST http://localhost:3002/api/v1/unregister \
+  -H "X-API-Key: muser_..." -H "Content-Type: application/json" \
+  -d '{"file_path":"/data/demo","pattern":"\\.mp4$"}'
+```
+```json
+{"success":true,"file_uuid":"53e3a229bf68878b7a799e811e097f9c","message":"File unregistered successfully"}
+```
+
+```bash
+curl "http://localhost:3002/api/v1/files?page=1&page_size=2" -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+```
+```json
+{"success":true,"data":[{"file_uuid":"aeed7134...","file_name":"Charade (1963)...","status":"ready"}],"total":0,"page":1,"page_size":2}
+```
+
+---
+
+## 3. Search
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 21 | POST | `/api/v1/search/visual` | Visual chunk search |
+| 22 | POST | `/api/v1/search/visual/class` | By object class |
+| 23 | POST | `/api/v1/search/visual/density` | By spatial density |
+| 24 | POST | `/api/v1/search/visual/combination` | Combined visual search |
+| 25 | POST | `/api/v1/search/visual/stats` | Visual stats |
+| 26 | POST | `/api/v1/search/smart` | Semantic (EmbeddingGemma + pgvector) |
+| 27 | POST | `/api/v1/search/universal` | BM25 keyword (requires file_uuid) |
+| 28 | POST | `/api/v1/search/frames` | Frame-level search |
+
+```bash
+curl -X POST http://localhost:3002/api/v1/search/universal  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"  -H "Content-Type: application/json"  -d '{"query":"name","limit":2,"mode":"bm25","file_uuid":"3abeee81d94597629ed8cb943f182e94"}'
+```
+```json
+{"query":"name","results":[{"chunk_id":"100","text":"What's your name?","start_time":258.68,"score":0.90}],"total":5,"page":1,"page_size":20,"took_ms":42}
+```
+
+```bash
+curl -X POST http://localhost:3002/api/v1/search/universal  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"  -H "Content-Type: application/json"  -d '{"query":"friends","limit":2,"mode":"bm25","file_uuid":"3abeee81d94597629ed8cb943f182e94"}'
+```
+```json
+{"query":"friends","results":[{"chunk_id":"104","text":"You won't find it difficult to make some new friends.","start_time":272.38,"score":0.90}],"total":3,"page":1,"page_size":20,"took_ms":38}
+```
+
+---
+
+## 4. Face Trace
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 29 | POST | `/api/v1/file/:file_uuid/traces` | List traces (sorted/filtered) |
+| 30 | GET | `/api/v1/file/:file_uuid/trace/:trace_id/faces` | Trace detections (+ interpolation) |
+
+### traces — list traces
+
+Parameters:
+- `sort_by`: `face_count` | `duration` | `first_appearance`
+- `min_faces`, `min_confidence`, `max_confidence`: filters
+- `limit`: max results
+
+```bash
+curl -X POST "http://localhost:3002/api/v1/file/3abeee81d94597629ed8cb943f182e94/traces"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"  -H "Content-Type: application/json"  -d '{"sort_by":"face_count","limit":2}'
+```
+```json
+{"success":true,"total_traces":6892,"total_faces":108204,"traces":[
+  {"trace_id":3128,"face_count":1109,"avg_confidence":0.779},
+  {"trace_id":3126,"face_count":743,"avg_confidence":0.758}
+]}
+```
+
+### trace/:trace_id/faces — individual detections
+
+Parameters:
+- `limit`, `offset`: pagination
+- `interpolate`: boolean (fills sparse gaps with lerp bbox)
+
+```bash
+curl "http://localhost:3002/api/v1/file/3abeee81d94597629ed8cb943f182e94/trace/2/faces?limit=2&interpolate=true"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+```
+```json
+{"success":true,"trace_id":2,"fps":25.0,"total":1,"faces":[
+  {"id":12399,"start_frame":4620,"end_frame":4620,"start_time":184.8,"end_time":184.8,"x":787,"y":582,"width":225,"height":225,"confidence":0.666,"interpolated":false}
+]}
+```
+
+---
+
+## 5. Media
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 31 | GET | `/api/v1/file/:file_uuid/thumbnail` | Frame JPEG (?frame=&x=&y=&w=&h=) |
+| 32 | GET | `/api/v1/file/:file_uuid/video` | Raw video stream. Dual input: `?start_time=&end_time=` (seconds) or `?start_frame=&end_frame=` (frames). |
+| 33 | GET | `/api/v1/file/:file_uuid/video/bbox` | Bbox overlay. `?start_frame=&end_frame=&face_uuid=&duration=` (all frame numbers). Dual input via `start_time`/`end_time`. |
+| 34 | GET | `/api/v1/file/:file_uuid/trace/:trace_id/video` | Trace clip (?mode=&padding=&audio=) |
+
+All video endpoints support:
+- `mode=normal|debug` (default: `normal`)
+- `audio=on|off` (default: `on`)
+
+`mode=normal`: raw clip, `-c copy`, no overlay.
+`mode=debug`: re-encoded with top-left text info + green bboxes (trace labels at actual frames with thickness=4, interpolated at first known position with thickness=1).
+
+```bash
+# Normal mode
+curl -o trace.mp4 "http://localhost:3002/api/v1/file/{file_uuid}/trace/42/video?mode=normal"
+# Debug mode
+curl -o trace_debug.mp4 "http://localhost:3002/api/v1/file/{file_uuid}/trace/42/video?mode=debug"
+```
+
+Debug overlay shows at bottom-left:
+```
+Frame {n}  {pts}s
+Cut: {id}
+{file_uuid}
+Trace {id}: start={frame}  {name}
+...
+```
+
+Green bbox per face detection: actual frames `thickness=4`, interpolated `thickness=1`.
+
+---
+
+## 6. Identities
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 35 | GET | `/api/v1/identities` | List all identities |
+| 36 | GET | `/api/v1/file/:file_uuid/identities` | Identities in a file |
+| 37 | POST | `/api/v1/identity` | Register new identity |
+| 38 | GET | `/api/v1/identity/:identity_uuid` | Identity detail |
+| 39 | DELETE | `/api/v1/identity/:identity_uuid` | Delete identity |
+| 40 | GET | `/api/v1/identity/:identity_uuid/files` | Files for identity |
+| 41 | GET | `/api/v1/identity/:identity_uuid/chunks` | Chunks for identity |
+| 42 | GET | `/api/v1/faces/candidates` | Unbound face gallery |
+| 43 | GET | `/api/v1/identities/search?q=` | Search identities by name → chunks |
+| 44 | GET | `/api/v1/search/identity_text?q=&file_uuid=` | Full-text search → identity-bound chunks |
+
+```bash
+curl "http://localhost:3002/api/v1/identities?page=1&page_size=3"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+```
+```json
+{"count":3852,"page":1,"page_size":3,"identities":[
+  {"id":18299,"identity_uuid":"76f85ee6-bc47-4a1a-9878-1beb67851ec5","name":"PERSON_aeed7134_390","metadata":{}},
+  {"id":18298,"identity_uuid":"f4d4ccbf-fccb-4f62-8806-2b7f4a706edb","name":"PERSON_aeed7134_389","metadata":{}},
+  {"id":18297,"identity_uuid":"e8a1b2c3-d4e5-4f67-8901-23456789abcd","name":"PERSON_aeed7134_388","metadata":{}}
+]}
+```
+
+### GET /api/v1/file/:file_uuid/identities — identities with frame/time ranges
+
+```bash
+curl "http://localhost:3002/api/v1/file/aeed71342a899fe4b4c57b7d41bcb692/identities?limit=2"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+```
+```json
+{"success":true,"file_uuid":"aeed71342a899fe4b4c57b7d41bcb692","fps":25.0,"total":20,"page":1,"page_size":20,"data":[
+  {"identity_id":18276,"identity_uuid":"77d895cc-bc2e-4f5a-84b3-3c1f0e2a5b6a","name":"PERSON_aeed7134_367","face_count":86,"start_frame":150744,"end_frame":152895,"start_time":6029.76,"end_time":6115.8,"confidence":0.855},
+  {"identity_id":18179,"identity_uuid":"90fc04cd-003b-4a1b-9f7d-8c3e1d2f4a5b","name":"PERSON_aeed7134_270","face_count":13,"start_frame":77418,"end_frame":77454,"start_time":3096.72,"end_time":3098.16,"confidence":0.851}
+]}
+```
+
+```bash
+curl "http://localhost:3002/api/v1/faces/candidates?page=1&page_size=2"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+```
+```json
+{"total":42,"candidates":[{"frame_number":30,"confidence":0.85},...]}
+```
+
+---
+
+## 7. Identity Binding
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 45 | POST | `/api/v1/identity/:identity_uuid/bind` | Bind face → identity |
+| 46 | POST | `/api/v1/identity/:identity_uuid/unbind` | Unbind face from identity |
+| 47 | POST | `/api/v1/identity/:identity_uuid/mergeinto` | Merge into another identity |
+
+```bash
+curl -X POST "http://localhost:3002/api/v1/identity/a9a90105-6d6b-46ff-92da-0c3c1a57dff4/bind"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"  -H "Content-Type: application/json"  -d '{"file_uuid":"3abeee81d94597629ed8cb943f182e94","face_id":"face_42"}'
+```
+```json
+{"success":true}
+```
+
+---
+
+## 8. Resources
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 48 | POST | `/api/v1/resource/register` | Register processing resource |
+| 49 | POST | `/api/v1/resource/heartbeat` | Resource heartbeat |
+| 50 | GET | `/api/v1/resources` | List all resources |
+
+```bash
+curl "http://localhost:3002/api/v1/resources"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+```
+```json
+{"success":true,"data":[{"resource_id":"mxbai-embed-large-v1","resource_type":"embedding_model"}],"message":"OK"}
+```
+
+---
+
+## 9. Agents — 5W1H
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 51 | POST | `/api/v1/agents/translate` | AI text translation |
+| 52 | POST | `/api/v1/agents/5w1h/analyze` | Single chunk analysis |
+| 53 | POST | `/api/v1/agents/5w1h/batch` | Batch analysis |
+| 54 | GET | `/api/v1/agents/5w1h/status` | Job status |
+
+```bash
+curl -X POST "http://localhost:3002/api/v1/agents/translate"  -H "X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"  -H "Content-Type: application/json"  -d '{"text":"Hello world","target_language":"zh-TW"}'
+```
+```json
+{"success":true,"translated_text":"你好世界"}
+```
+
+---
+
+## 10. Agents — Identity
+
+| # | Method | Path | Description |
+|---|--------|------|-------------|
+| 55 | POST | `/api/v1/agents/identity/match-from-photo` | Match face from photo |
+| 56 | POST | `/api/v1/agents/identity/match-from-trace` | Match face from trace |
+| 57 | POST | `/api/v1/agents/suggest/merge` | Suggest merge |
+| 58 | POST | `/api/v1/agents/suggest/clustering` | Suggest re-clustering |
+
+---
+
+## Version History
+
+| Version | Date | Changes |
+|---------|------|---------|
+| V4.2 | 2026-05-25 | Removed phantom routes (stats/ingest, stats/inference, agents/identity/status); fixed HTTP methods (chunk, progress, jobs → POST); renamed endpoints (face_trace/sortby → traces, analyze → match-from-photo, suggest → match-from-trace); added config endpoints (consistency, auto-pipeline, watcher-auto-register); updated git hash to de88fd4e |
+| V4.1 | 2026-05-14 | Added `build_timestamp` + `resources` + `pipeline` to health APIs; identity search endpoints; trace debug rework (green bbox, text overlay, all traces listed) |
+
+## Related
+
+- `API_DICTIONARY_V1.0.0.md` — Quick reference (55 endpoints)
+- `API_DOCUMENTATION_v1.0.0.md` — Detailed spec with examples
+- `TRACE/TRACE_API_REFERENCE_V1.0.0.md` — Trace-specific reference
@@ -0,0 +1,218 @@
+# Momentry API 使用指南
+
+## 認證流程
+
+```mermaid
+sequenceDiagram
+    actor User
+    participant API as Momentry API
+    participant Auth as Auth Service
+
+    User->>API: POST /api/v1/auth/login
+    API->>Auth: 驗證 username/password
+    Auth-->>API: API Key
+    API-->>User: { "api_key": "muser_xxx..." }
+    Note over User: 後續請求帶入 Header
+    User->>API: GET /api/v1/files<br/>X-API-Key: muser_xxx...
+    API-->>User: { files: [...] }
+```
+
+**demo 帳號**: `demo` / `demo`
+
+---
+
+## 註冊 + 處理流程
+
+```mermaid
+flowchart LR
+    A[上傳影片] --> B[POST /files/register]
+    B --> C[取得 file_uuid]
+    C --> D[POST /file/:file_uuid/process]
+    ...
+    F --> M[GET /progress/:file_uuid]
+    G --> M
+    H --> M
+    I --> M
+    J --> M
+    K --> M
+    L --> M
+    M --> N[completed]
+```
+
+---
+
+## 臉部追蹤架構
+
+```mermaid
+graph TB
+    subgraph Detection
+        A[Face Processor] --> B[face_detections]
+        B --> C[Store Traced Faces]
+    end
+
+    subgraph Tracing
+        C --> D[face_traces]
+        D --> E[Trace Aggregation]
+    end
+
+    subgraph API
+        E --> F[POST /face_trace/sortby]
+        E --> G[GET /trace/:id/faces]
+        E --> H[GET /trace/:id/video]
+    end
+
+    subgraph Display
+        F --> I[Face Thumbnail Timeline V1]
+        F --> J[Identity Swimlane V2]
+        G --> K[Interpolation POC]
+        H --> L[MP4 with BBOX]
+    end
+```
+
+---
+
+## 搜尋三模式
+
+```mermaid
+flowchart TD
+    Q[使用者輸入查詢] --> M{選擇模式}
+
+    M -->|BM25| A[POST /search/universal]
+    A --> B[PostgreSQL ILIKE]
+    B --> C[關鍵字比對 text_content]
+
+    M -->|Vector| D[POST /search/smart]
+    D --> E[EmbeddingGemma 768D]
+    E --> F[pgvector 相似度搜尋]
+
+    M -->|Hybrid| G[內部組合]
+    G --> H[Vector Search]
+    G --> I[BM25 Rerank]
+    H --> J[Reranked Results]
+    I --> J
+
+    C --> K[結果回傳]
+    F --> K
+    J --> K
+```
+
+---
+
+## 資料模型關聯
+
+```mermaid
+erDiagram
+    VIDEOS ||--o{ FACE_DETECTIONS : contains
+    VIDEOS ||--o{ CHUNKS : contains
+    VIDEOS ||--o{ PRE_CHUNKS : contains
+    FACE_DETECTIONS ||--o{ FACE_TRACES : belongs_to
+    FACE_TRACES }o--|| IDENTITIES : identifies
+    IDENTITIES ||--o{ IDENTITY_BINDINGS : binds
+    CHUNKS ||--o{ PARENT_CHUNKS : groups
+    VIDEOS {
+        string file_uuid PK
+        string file_name
+        float duration
+        int width
+        int height
+        float fps
+    }
+    FACE_DETECTIONS {
+        int id PK
+        string file_uuid FK
+        int trace_id
+        int frame_number
+        int x
+        int y
+        float confidence
+    }
+    IDENTITIES {
+        int id PK
+        string name
+        string file_uuid
+        int tmdb_id
+    }
+```
+
+---
+
+## 端點路徑總覽
+
+```mermaid
+mindmap
+  root((api.momentry.ddns.net))
+    System
+      GET /health
+      POST /auth/login
+      GET /stats/ingest
+    Files
+      POST /files/register
+      GET /files
+      GET /file/:file_uuid
+      POST /file/:file_uuid/process
+    Traces
+      POST /face_trace/sortby
+      GET /trace/:trace_id/faces
+      GET /trace/:trace_id/video
+      GET /thumbnail
+    Search
+      POST /search/universal
+      POST /search/smart
+      POST /search/visual
+    Identities
+      GET /identities
+      POST /identity
+      POST /identity/:identity_uuid/bind
+    Agents
+      POST /agents/translate
+      POST /agents/5w1h/analyze
+      POST /agents/identity/suggest
+```
+
+---
+
+## 互動範例
+
+### 1. 登入 → 取得檔案列表
+
+```mermaid
+sequenceDiagram
+    actor Dev
+    Dev->>API: POST /api/v1/auth/login<br/>{ "username": "demo", "password": "demo" }
+    API-->>Dev: { "api_key": "muser_test_001..." }
+    Dev->>API: GET /api/v1/files<br/>X-API-Key: muser_test_001...
+    API-->>Dev: { "files": [...], "total": 37 }
+```
+
+### 2. 查看臉部追蹤 → 播放影片
+
+```mermaid
+sequenceDiagram
+    actor Dev
+    Dev->>API: POST /api/v1/file/{file_uuid}/face_trace/sortby<br/>{ "sort_by": "face_count", "limit": 3 }
+    API-->>Dev: { "total_traces": 6892, "traces": [...] }
+    Dev->>API: GET /api/v1/file/{file_uuid}/trace/3128/video
+    API-->>Dev: MP4 binary
+    Note over Dev: Browser opens video with bbox
+```
+
+### 3. 身分識別
+
+```mermaid
+sequenceDiagram
+    actor Dev
+    Dev->>API: GET /api/v1/identities?page=560&page_size=5
+    API-->>Dev: { "identities": [<br/>  {"name":"Cary Grant"},<br/>  {"name":"Audrey Hepburn"}<br/>] }
+```
+
+---
+
+## 快速參考
+
+| 用途 | 指令 |
+|------|------|
+| 登入取得 Key | `curl -X POST https://api.momentry.ddns.net/api/v1/auth/login -H "Content-Type: application/json" -d '{"username":"demo","password":"demo"}'` |
+| 列出檔案 | `curl https://api.momentry.ddns.net/api/v1/files -H "X-API-Key: muser_test_001"` |
+| Top Traces | `curl -X POST https://api.momentry.ddns.net/api/v1/file/{file_uuid}/face_trace/sortby -H "X-API-Key: muser_test_001" -H "Content-Type: application/json" -d '{"sort_by":"face_count","limit":3}'` |
+| BM25 搜尋 | `curl -X POST https://api.momentry.ddns.net/api/v1/search/universal -H "X-API-Key: muser_test_001" -H "Content-Type: application/json" -d '{"query":"friends","mode":"bm25","uuid":"{file_uuid}"}'` |
+| 身分列表 | `curl https://api.momentry.ddns.net/api/v1/identities?page=1&page_size=5 -H "X-API-Key: muser_test_001"` |
@@ -0,0 +1,136 @@
+{
+  "title": "Momentry Core 展示 v1.0.0",
+  "version": "1.0",
+  "language": "zh_TW",
+  "server": "https://api.momentry.ddns.net",
+  "setup": "KEY=\"X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69\"; BASE=https://api.momentry.ddns.net; FILE=3abeee81d94597629ed8cb943f182e94",
+  "steps": [
+    {
+      "type": "separator",
+      "label": "開場：系統活著"
+    },
+    {
+      "type": "note",
+      "label": "確認服務正常",
+      "note": "Momentry Core 是一套影片內容分析系統。給它一支影片，它會自動辨識裡面的人臉、追蹤他們的移動、分析誰是誰，還能用文字搜尋影片內容。"
+    },
+    {
+      "type": "curl",
+      "label": "伺服器狀態檢查",
+      "note": "先確認服務正常。正式環境伺服器回應狀態「ok」。",
+      "cmd": "curl -s $BASE/health",
+      "expect": "ok"
+    },
+    {
+      "type": "browser",
+      "label": "瀏覽器開啟狀態頁",
+      "note": "瀏覽器直接開啟狀態頁面也可以。",
+      "url": "$BASE/health"
+    },
+
+    {
+      "type": "separator",
+      "label": "檔案與人臉追蹤"
+    },
+    {
+      "type": "curl",
+      "label": "檢視已註冊檔案",
+      "note": "目前系統有三十七支已註冊的影片，以 Charade 這部老電影為主。",
+      "cmd": "curl -s \"$BASE/api/v1/files?page=1&page_size=3\" -H \"X-API-Key: $KEY\"",
+      "expect": "file_uuid"
+    },
+    {
+      "type": "curl",
+      "label": "人臉追蹤總覽",
+      "note": "核心功能：系統把影片中每個出現的人臉追蹤成一個「追蹤紀錄」。這部 Charade 總共找到六千八百九十二個追蹤、十萬八千二百零四次臉部偵測。最長的一段追蹤有一千一百零九次連續出現，持續四十四點三秒。",
+      "cmd": "curl -s -X POST $BASE/api/v1/file/$FILE/face_trace/sortby -H \"X-API-Key: $KEY\" -H \"Content-Type: application/json\" -d '{\"sort_by\":\"face_count\",\"limit\":5}'",
+      "expect": "total_traces"
+    },
+    {
+      "type": "curl",
+      "label": "追蹤細節與補間動畫",
+      "note": "人臉處理器每隔三十個影格才取樣一次，原始資料是稀疏的。加上補間參數後，系統會自動計算中間每個影格的方框位置。補間標記為真的代表這是運算產生的，信心度為零。",
+      "cmd": "curl -s \"$BASE/api/v1/file/$FILE/trace/2/faces?limit=5&interpolate=true\" -H \"X-API-Key: $KEY\"",
+      "expect": "interpolated"
+    },
+
+    {
+      "type": "separator",
+      "label": "影片播放"
+    },
+    {
+      "type": "browser",
+      "label": "觀看追蹤影片",
+      "note": "把人臉追蹤渲染成影片，紅色方框標記人臉位置。每個偵測的框會持續到下一次偵測為止。",
+      "url": "$BASE/api/v1/file/$FILE/trace/5/video?padding=1"
+    },
+    {
+      "type": "browser",
+      "label": "觀看單張縮圖",
+      "note": "單一個影格的 JPEG 截圖。",
+      "url": "$BASE/api/v1/file/$FILE/thumbnail?frame=68280"
+    },
+
+    {
+      "type": "separator",
+      "label": "文字搜尋"
+    },
+    {
+      "type": "curl",
+      "label": "關鍵字搜尋「朋友」",
+      "note": "文字搜尋：不需要向量，直接用關鍵字比對。這是搜尋「朋友」的結果。",
+      "cmd": "curl -s -X POST $BASE/api/v1/search/universal -H \"X-API-Key: $KEY\" -H \"Content-Type: application/json\" -d '{\"query\":\"friends\",\"limit\":3,\"mode\":\"bm25\",\"uuid\":\"$FILE\"}'",
+      "expect": "friends"
+    },
+    {
+      "type": "curl",
+      "label": "關鍵字搜尋「名字」",
+      "note": "再搜尋「名字」看看，會找到「你叫什麼名字？」這段台詞。",
+      "cmd": "curl -s -X POST $BASE/api/v1/search/universal -H \"X-API-Key: $KEY\" -H \"Content-Type: application/json\" -d '{\"query\":\"name\",\"limit\":3,\"mode\":\"bm25\",\"uuid\":\"$FILE\"}'",
+      "expect": "name"
+    },
+
+    {
+      "type": "separator",
+      "label": "身分辨識"
+    },
+    {
+      "type": "curl",
+      "label": "電影資料庫身分列表",
+      "note": "系統不只是追蹤臉，它還知道誰是誰。處理管線自動比對電影資料庫後的結果：兩千八百一十個身分，包含 Cary Grant、Audrey Hepburn 等知名演員。",
+      "cmd": "curl -s \"$BASE/api/v1/identities?page=560&page_size=5\" -H \"X-API-Key: $KEY\"",
+      "expect": "\"name\""
+    },
+    {
+      "type": "curl",
+      "label": "未辨識人臉候選",
+      "note": "還沒被指認的身分叫做候選人，可以在這裡手動綁定到正確人名。",
+      "cmd": "curl -s \"$BASE/api/v1/faces/candidates?page=1&page_size=3\" -H \"X-API-Key: $KEY\"",
+      "expect": "candidates"
+    },
+    {
+      "type": "curl",
+      "label": "系統資源一覽",
+      "note": "系統資源一覽：包含目前使用的文字嵌入模型等資訊。",
+      "cmd": "curl -s \"$BASE/api/v1/resources\" -H \"X-API-Key: $KEY\"",
+      "expect": "success"
+    },
+
+    {
+      "type": "separator",
+      "label": "人工智慧語意搜尋"
+    },
+    {
+      "type": "curl",
+      "label": "向量語意搜尋",
+      "note": "最後是人工智慧搜尋。查詢先經由嵌入模型轉成七百六十八維的向量，再到向量資料庫做相似度比對。",
+      "cmd": "curl -s -X POST $BASE/api/v1/search/smart -H \"X-API-Key: $KEY\" -H \"Content-Type: application/json\" -d '{\"query\":\"Audrey Hepburn\",\"uuid\":\"$FILE\"}'",
+      "expect": "results"
+    },
+
+    {
+      "type": "separator",
+      "label": "展示結束"
+    }
+  ]
+}
@@ -0,0 +1,173 @@
+ # Momentry Demo Script v1.0.0
+
+Curl for POST/API, browser for video/thumbnail. 約 10 分鐘。
+
+---
+
+## 開場：這是什麼？
+
+> 「Momentry Core — 影片內容分析系統。給它一支影片，它會自動辨識裡面的人臉、追蹤他們的移動、分析誰是誰，還能用文字搜尋影片內容。」
+
+---
+
+## Step 0: 設定
+
+```bash
+KEY="X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+BASE=https://api.momentry.ddns.net
+```
+
+---
+
+## Step 1: 系統活著
+
+> 「先確認服務正常。」
+
+```bash
+curl $BASE/health
+```
+
+**預期**: `{"status":"ok","version":"1.0.0","uptime_ms":...}`
+
+👉 瀏覽器開 `https://api.momentry.ddns.net/health` 也可。
+
+---
+
+## Step 2: 檔案一覽
+
+> 「目前系統有 37 支已註冊的影片。」
+
+```bash
+curl "$BASE/api/v1/files?page=1&page_size=3" -H "$KEY"
+```
+
+**預期**: Charade (1963) 為主，還有其他測試檔。
+
+---
+
+## Step 3: 臉部追蹤概覽
+
+> 「這是核心功能。系統把影片中每個出現的人臉追蹤成一個『trace』。這部 Charade 總共找到 **6,892 個 trace、108,204 次臉部偵測**。」
+
+```bash
+curl -X POST $BASE/api/v1/file/3abeee81d94597629ed8cb943f182e94/face_trace/sortby -H "$KEY" \
+  -H "Content-Type: application/json" \
+  -d '{"sort_by":"face_count","limit":5}'
+```
+
+**解說**:
+- trace #3128: **1,109 次出現**，持續 44.3 秒 — 這是最長的一段
+- trace #3126: 743 次
+- 數字越高代表這個人出現在畫面上的時間越長
+
+---
+
+## Step 4: 單一 Trace 細節
+
+> 「點進去看一個 trace 的每一幀。每個框框就是一次臉部偵測，包含位置、大小、信心度。」
+
+```bash
+curl "$BASE/api/v1/file/3abeee81d94597629ed8cb943f182e94/trace/2/faces?limit=3" -H "$KEY"
+```
+
+**解說**: 回傳的資料包含 `start_frame`（第幾幀）、`start_time`（第幾秒）、bbox 座標、信心度。
+
+---
+
+## Step 5: 補間動畫
+
+> 「因為 face processor 每隔 30 幀才取樣一次，所以原始資料是稀疏的。加上 `interpolate=true` 後，系統會自動線性補間，填滿中間每一幀的 bbox 位置。」
+
+```bash
+curl "$BASE/api/v1/file/3abeee81d94597629ed8cb943f182e94/trace/2/faces?limit=5&interpolate=true" -H "$KEY"
+```
+
+**解說**: `interpolated: false` 是真實偵測，`interpolated: true` 是補間的，confidence = 0。前端的淺色框就是補間框。
+
+---
+
+## Step 6: Trace 影片播放（瀏覽器）
+
+> 「把 trace 渲染成影片，紅框標記人臉位置。」
+
+**瀏覽器開**:
+```
+https://api.momentry.ddns.net/api/v1/file/3abeee81d94597629ed8cb943f182e94/trace/5/video?padding=1
+```
+
+**解說**: 紅框 = 臉部位置，文字標籤 = trace ID。每個 detection 的框會持續到下一次偵測為止。
+
+---
+
+## Step 7: 關鍵字搜尋 (BM25)
+
+> 「文字搜尋 — 不需要向量，直接用關鍵字比對。這是『friends』的搜尋結果。」
+
+```bash
+curl -X POST $BASE/api/v1/search/universal -H "$KEY" \
+  -H "Content-Type: application/json" \
+  -d '{"query":"friends","limit":3,"mode":"bm25","file_uuid":"3abeee81d94597629ed8cb943f182e94"}'
+```
+
+**預期**: `"You won't find it difficult to make some new friends."` score=0.90
+
+> 「再搜尋『name』看看：」
+
+```bash
+curl -X POST $BASE/api/v1/search/universal -H "$KEY" \
+  -H "Content-Type: application/json" \
+  -d '{"query":"name","limit":3,"mode":"bm25","file_uuid":"3abeee81d94597629ed8cb943f182e94"}'
+```
+
+**預期**: `"What's your name?"` score=0.90
+
+---
+
+## Step 8: 身分辨識
+
+> 「系統不只是追蹤臉，它還知道誰是誰。這是 M5 pipeline 自動比對 TMDb 資料庫後的結果 — **2,810 個身分**，包含 Cary Grant、Audrey Hepburn 等。」
+
+```bash
+curl "$BASE/api/v1/identities?page=560&page_size=5" -H "$KEY"
+```
+
+**預期**: Raoul Delfosse, Albert Daumergue, Claudine Berg...
+
+> 「也可以直接看所有身分的列表，按頁次翻找。」
+
+---
+
+## Step 9: 臉部候選人（未辨識）
+
+> 「還沒被指认的身分叫做『candidate』，可以在這裡手動綁定。」
+
+```bash
+curl "$BASE/api/v1/faces/candidates?page=1&page_size=3" -H "$KEY"
+```
+
+---
+
+## Step 10: 嵌入向量搜尋
+
+> 「最後是 AI 搜尋。Query 先經由 EmbeddingGemma 轉成 768 維向量，再到 Qdrant 做相似度比對。」
+
+```bash
+curl -X POST $BASE/api/v1/search/smart -H "$KEY" \
+  -H "Content-Type: application/json" \
+  -d '{"query":"Audrey Hepburn","file_uuid":"3abeee81d94597629ed8cb943f182e94"}'
+```
+
+---
+
+## 收尾
+
+> 「以上就是 Momentry Core v1.0.0 的主要功能展示。總結：**
+>
+> 1. **臉部追蹤** — 6,892 traces, 108,204 detections
+> 2. **補間動畫** — 稀疏取樣 → 連續軌跡
+> 3. **影片渲染** — bbox overlay MP4
+> 4. **關鍵字搜尋** — BM25 全文檢索
+> 5. **身分辨識** — 2,810 identities, TMDb 整合
+> 6. **AI 語意搜尋** — EmbeddingGemma + Qdrant
+>
+> 所有 API 皆可透過 `https://api.momentry.ddns.net` 存取，使用 demo/demo 登入取得 API key。"
@@ -0,0 +1,173 @@
+ # Momentry Demo Script v1.0.0
+
+Curl for POST/API, browser for video/thumbnail. 約 10 分鐘。
+
+---
+
+## 開場：這是什麼？
+
+> 「Momentry Core — 影片內容分析系統。給它一支影片，它會自動辨識裡面的人臉、追蹤他們的移動、分析誰是誰，還能用文字搜尋影片內容。」
+
+---
+
+## Step 0: 設定
+
+```bash
+KEY="X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+BASE=https://api.momentry.ddns.net
+```
+
+---
+
+## Step 1: 系統活著
+
+> 「先確認服務正常。」
+
+```bash
+curl $BASE/health
+```
+
+**預期**: `{"status":"ok","version":"1.0.0","uptime_ms":...}`
+
+👉 瀏覽器開 `https://api.momentry.ddns.net/health` 也可。
+
+---
+
+## Step 2: 檔案一覽
+
+> 「目前系統有 37 支已註冊的影片。」
+
+```bash
+curl "$BASE/api/v1/files?page=1&page_size=3" -H "$KEY"
+```
+
+**預期**: Charade (1963) 為主，還有其他測試檔。
+
+---
+
+## Step 3: 臉部追蹤概覽
+
+> 「這是核心功能。系統把影片中每個出現的人臉追蹤成一個『trace』。這部 Charade 總共找到 **6,892 個 trace、108,204 次臉部偵測**。」
+
+```bash
+curl -X POST $BASE/api/v1/file/3abeee81d94597629ed8cb943f182e94/face_trace/sortby -H "$KEY" \
+  -H "Content-Type: application/json" \
+  -d '{"sort_by":"face_count","limit":5}'
+```
+
+**解說**:
+- trace #3128: **1,109 次出現**，持續 44.3 秒 — 這是最長的一段
+- trace #3126: 743 次
+- 數字越高代表這個人出現在畫面上的時間越長
+
+---
+
+## Step 4: 單一 Trace 細節
+
+> 「點進去看一個 trace 的每一幀。每個框框就是一次臉部偵測，包含位置、大小、信心度。」
+
+```bash
+curl "$BASE/api/v1/file/3abeee81d94597629ed8cb943f182e94/trace/2/faces?limit=3" -H "$KEY"
+```
+
+**解說**: 回傳的資料包含 `start_frame`（第幾幀）、`start_time`（第幾秒）、bbox 座標、信心度。
+
+---
+
+## Step 5: 補間動畫
+
+> 「因為 face processor 每隔 30 幀才取樣一次，所以原始資料是稀疏的。加上 `interpolate=true` 後，系統會自動線性補間，填滿中間每一幀的 bbox 位置。」
+
+```bash
+curl "$BASE/api/v1/file/3abeee81d94597629ed8cb943f182e94/trace/2/faces?limit=5&interpolate=true" -H "$KEY"
+```
+
+**解說**: `interpolated: false` 是真實偵測，`interpolated: true` 是補間的，confidence = 0。前端的淺色框就是補間框。
+
+---
+
+## Step 6: Trace 影片播放（瀏覽器）
+
+> 「把 trace 渲染成影片，紅框標記人臉位置。」
+
+**瀏覽器開**:
+```
+https://api.momentry.ddns.net/api/v1/file/3abeee81d94597629ed8cb943f182e94/trace/5/video?padding=1
+```
+
+**解說**: 紅框 = 臉部位置，文字標籤 = trace ID。每個 detection 的框會持續到下一次偵測為止。
+
+---
+
+## Step 7: 關鍵字搜尋 (BM25)
+
+> 「文字搜尋 — 不需要向量，直接用關鍵字比對。這是『friends』的搜尋結果。」
+
+```bash
+curl -X POST $BASE/api/v1/search/universal -H "$KEY" \
+  -H "Content-Type: application/json" \
+  -d '{"query":"friends","limit":3,"mode":"bm25","file_uuid":"3abeee81d94597629ed8cb943f182e94"}'
+```
+
+**預期**: `"You won't find it difficult to make some new friends."` score=0.90
+
+> 「再搜尋『name』看看：」
+
+```bash
+curl -X POST $BASE/api/v1/search/universal -H "$KEY" \
+  -H "Content-Type: application/json" \
+  -d '{"query":"name","limit":3,"mode":"bm25","file_uuid":"3abeee81d94597629ed8cb943f182e94"}'
+```
+
+**預期**: `"What's your name?"` score=0.90
+
+---
+
+## Step 8: 身分辨識
+
+> 「系統不只是追蹤臉，它還知道誰是誰。這是 M5 pipeline 自動比對 TMDb 資料庫後的結果 — **2,810 個身分**，包含 Cary Grant、Audrey Hepburn 等。」
+
+```bash
+curl "$BASE/api/v1/identities?page=560&page_size=5" -H "$KEY"
+```
+
+**預期**: Raoul Delfosse, Albert Daumergue, Claudine Berg...
+
+> 「也可以直接看所有身分的列表，按頁次翻找。」
+
+---
+
+## Step 9: 臉部候選人（未辨識）
+
+> 「還沒被指认的身分叫做『candidate』，可以在這裡手動綁定。」
+
+```bash
+curl "$BASE/api/v1/faces/candidates?page=1&page_size=3" -H "$KEY"
+```
+
+---
+
+## Step 10: 嵌入向量搜尋
+
+> 「最後是 AI 搜尋。Query 先經由 EmbeddingGemma 轉成 768 維向量，再到 Qdrant 做相似度比對。」
+
+```bash
+curl -X POST $BASE/api/v1/search/smart -H "$KEY" \
+  -H "Content-Type: application/json" \
+  -d '{"query":"Audrey Hepburn","file_uuid":"3abeee81d94597629ed8cb943f182e94"}'
+```
+
+---
+
+## 收尾
+
+> 「以上就是 Momentry Core v1.0.0 的主要功能展示。總結：**
+>
+> 1. **臉部追蹤** — 6,892 traces, 108,204 detections
+> 2. **補間動畫** — 稀疏取樣 → 連續軌跡
+> 3. **影片渲染** — bbox overlay MP4
+> 4. **關鍵字搜尋** — BM25 全文檢索
+> 5. **身分辨識** — 2,810 identities, TMDb 整合
+> 6. **AI 語意搜尋** — EmbeddingGemma + Qdrant
+>
+> 所有 API 皆可透過 `https://api.momentry.ddns.net` 存取，使用 demo/demo 登入取得 API key。"
@@ -0,0 +1,114 @@
+# Demo Sequence v1.0.0
+
+Curl for POST, browser for GET/Video.
+
+## Setup
+
+```bash
+KEY="X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+BASE=https://api.momentry.ddns.net
+FILE=3abeee81d94597629ed8cb943f182e94
+```
+
+---
+
+## 1. Server Alive
+
+Curl:
+```bash
+curl $BASE/health
+```
+
+Browser: open `https://api.momentry.ddns.net/health`
+
+---
+
+## 2. List Traces (top 3 最多臉孔)
+
+Curl:
+```bash
+curl -X POST $BASE/api/v1/file/$FILE/face_trace/sortby -H "$KEY" -H "Content-Type: application/json" -d '{"sort_by":"face_count","limit":3}'
+```
+
+**預期**: 6892 traces, 最大 trace 1109 faces
+
+---
+
+## 3. Trace 詳情 + 補間動畫
+
+Curl:
+```bash
+curl "$BASE/api/v1/file/$FILE/trace/2/faces?limit=3&interpolate=true" -H "$KEY"
+```
+
+**預期**: real + interpolated frames，bbox 線性過渡
+
+---
+
+## 4. BM25 關鍵字搜尋
+
+Curl:
+```bash
+curl -X POST $BASE/api/v1/search/universal -H "$KEY" -H "Content-Type: application/json" -d '{"query":"friends","limit":3,"mode":"bm25","file_uuid":"$FILE"}'
+```
+
+**預期**: "You won't find it difficult to make some new friends."
+
+---
+
+## 5. 身分列表
+
+Curl:
+```bash
+curl "$BASE/api/v1/identities?page=560&page_size=5" -H "$KEY"
+```
+
+**預期**: Cary Grant, Audrey Hepburn, Walter Matthau...
+
+---
+
+## 6. Trace 影片播放 (Browser)
+
+Browser 開:
+```
+https://api.momentry.ddns.net/api/v1/file/3abeee81d94597629ed8cb943f182e94/trace/3128/video?padding=1
+```
+
+**預期**: MP4 影片，紅框標記臉部，顯示 "t3128" 標籤
+
+---
+
+## 7. BBOX 影片 (frame 區間)
+
+Browser 開:
+```
+https://api.momentry.ddns.net/api/v1/file/3abeee81d94597629ed8cb943f182e94/video/bbox?start_frame=68000&end_frame=69000
+```
+
+**預期**: 該區間內所有臉部偵測的 bbox overlay 影片
+
+---
+
+## 8. Frame 縮圖
+
+Browser 開:
+```
+https://api.momentry.ddns.net/api/v1/file/3abeee81d94597629ed8cb943f182e94/thumbnail?frame=68280
+```
+
+**預期**: JPEG 圖片（trace #3128 的第一幀）
+
+---
+
+## Summary
+
+| Step | Type | Endpoint | What to See |
+|------|------|----------|-------------|
+| 1 | Curl/Browser | `/health` | Server ok |
+| 2 | Curl | `face_trace/sortby` | 6892 traces |
+| 3 | Curl | `trace/:trace_id/faces?interpolate=true` | Interpolated bbox |
+| 4 | Curl | `search/universal` | BM25 match |
+| 5 | Curl | `/identities` | Named persons |
+| 6 | **Browser** | `trace/:trace_id/video` | MP4 with bbox |
+| 7 | **Browser** | `video/bbox` | Frame interval overlay |
+| 8 | **Browser** | `thumbnail` | Single frame JPEG |
@@ -0,0 +1,114 @@
+# Demo Sequence v1.0.0
+
+Curl for POST, browser for GET/Video.
+
+## Setup
+
+```bash
+KEY="X-API-Key: muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69"
+BASE=https://api.momentry.ddns.net
+FILE=3abeee81d94597629ed8cb943f182e94
+```
+
+---
+
+## 1. Server Alive
+
+Curl:
+```bash
+curl $BASE/health
+```
+
+Browser: open `https://api.momentry.ddns.net/health`
+
+---
+
+## 2. List Traces (top 3 最多臉孔)
+
+Curl:
+```bash
+curl -X POST $BASE/api/v1/file/$FILE/face_trace/sortby -H "$KEY" -H "Content-Type: application/json" -d '{"sort_by":"face_count","limit":3}'
+```
+
+**預期**: 6892 traces, 最大 trace 1109 faces
+
+---
+
+## 3. Trace 詳情 + 補間動畫
+
+Curl:
+```bash
+curl "$BASE/api/v1/file/$FILE/trace/2/faces?limit=3&interpolate=true" -H "$KEY"
+```
+
+**預期**: real + interpolated frames，bbox 線性過渡
+
+---
+
+## 4. BM25 關鍵字搜尋
+
+Curl:
+```bash
+curl -X POST $BASE/api/v1/search/universal -H "$KEY" -H "Content-Type: application/json" -d '{"query":"friends","limit":3,"mode":"bm25","file_uuid":"$FILE"}'
+```
+
+**預期**: "You won't find it difficult to make some new friends."
+
+---
+
+## 5. 身分列表
+
+Curl:
+```bash
+curl "$BASE/api/v1/identities?page=560&page_size=5" -H "$KEY"
+```
+
+**預期**: Cary Grant, Audrey Hepburn, Walter Matthau...
+
+---
+
+## 6. Trace 影片播放 (Browser)
+
+Browser 開:
+```
+https://api.momentry.ddns.net/api/v1/file/3abeee81d94597629ed8cb943f182e94/trace/3128/video?padding=1
+```
+
+**預期**: MP4 影片，紅框標記臉部，顯示 "t3128" 標籤
+
+---
+
+## 7. BBOX 影片 (frame 區間)
+
+Browser 開:
+```
+https://api.momentry.ddns.net/api/v1/file/3abeee81d94597629ed8cb943f182e94/video/bbox?start_frame=68000&end_frame=69000
+```
+
+**預期**: 該區間內所有臉部偵測的 bbox overlay 影片
+
+---
+
+## 8. Frame 縮圖
+
+Browser 開:
+```
+https://api.momentry.ddns.net/api/v1/file/3abeee81d94597629ed8cb943f182e94/thumbnail?frame=68280
+```
+
+**預期**: JPEG 圖片（trace #3128 的第一幀）
+
+---
+
+## Summary
+
+| Step | Type | Endpoint | What to See |
+|------|------|----------|-------------|
+| 1 | Curl/Browser | `/health` | Server ok |
+| 2 | Curl | `face_trace/sortby` | 6892 traces |
+| 3 | Curl | `trace/:trace_id/faces?interpolate=true` | Interpolated bbox |
+| 4 | Curl | `search/universal` | BM25 match |
+| 5 | Curl | `/identities` | Named persons |
+| 6 | **Browser** | `trace/:trace_id/video` | MP4 with bbox |
+| 7 | **Browser** | `video/bbox` | Frame interval overlay |
+| 8 | **Browser** | `thumbnail` | Single frame JPEG |
@@ -0,0 +1,83 @@
+# Embedding 跨機器部署方案 v1.0.0
+
+## 分工原則
+
+```
+M5（Pipeline + 主力 Embedding）        M4（Portal + Fallback Embedding）
+├── 批量 vectorize（1709 chunks）        ├── Portal search query embedding
+├── EmbeddingGemma 主 server             ├── 備援 embed server
+├── 模型已上線（port 11436）              └── 預設呼叫 M5 API
+└── 出門 demo 可離線運作
+```
+
+## 部署架構
+
+```
+Portal Search Query
+       │
+       ▼
+  ┌─────────────┐    成功     ┌──────────────────┐
+  │ M4 Portal   │ ──────────→ │ M5:11436         │
+  │ embed       │             │ EmbeddingGemma   │
+  │ client      │             │ (主力)           │
+  │             │    失敗     └──────────────────┘
+  │ retry       │ ──────────→ ┌──────────────────┐
+  │ fallback    │             │ M4:11436         │
+  └─────────────┘             │ EmbeddingGemma   │
+                              │ (備援)           │
+                              └──────────────────┘
+```
+
+## M4 安裝步驟
+
+```bash
+# 1. 安裝 Python 依賴
+pip install torch transformers flask
+
+# 2. 登入 HuggingFace（需接受授權）
+open https://huggingface.co/google/embeddinggemma-300m
+huggingface-cli login --token YOUR_TOKEN
+
+# 3. 取得 script
+rsync -av accusys@192.168.110.201:/Users/accusys/momentry_core_0.1/scripts/embeddinggemma_server.py \
+       ./scripts/embeddinggemma_server.py
+
+# 4. 啟動備援 server
+python3 scripts/embeddinggemma_server.py --port 11436
+```
+
+## Portal Embed Client
+
+```javascript
+async function embedQuery(text) {
+  const servers = [
+    'http://192.168.110.201:11436/v1/embeddings',  // M5 主力
+    'http://localhost:11436/v1/embeddings',          // M4 備援
+  ];
+  for (const url of servers) {
+    try {
+      const res = await fetch(url, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ input: text }),
+      });
+      const data = await res.json();
+      return data.data[0].embedding;
+    } catch (e) {
+      continue;  // 下一台
+    }
+  }
+  throw new Error('Embedding servers unreachable');
+}
+```
+
+## 模型一致性
+
+| 項目 | M5 | M4 |
+|------|-----|-----|
+| 模型 | EmbeddingGemma 300M | EmbeddingGemma 300M |
+| 維度 | 768D | 768D |
+| Server | Python MPS (port 11436) | Python CPU/MPS (port 11436) |
+| Qdrant | 192.168.110.201:6333 | 192.168.110.201:6333 |
+
+兩台使用同一模型、同一維度，確保 query embedding 與索引 embedding 可比對。
@@ -0,0 +1,316 @@
+---
+document_type: "deployment_record"
+service: "MOMENTRY_CORE"
+title: "Gemma 4 31B — M5 Max 部署記錄"
+date: "2026-05-06"
+version: "V1.1"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+---
+
+# Gemma 4 31B — M5 Max 部署記錄
+
+## 1. 環境
+
+| 項目 | M4（開發機） | M5 Max（LLM 伺服器） |
+|------|------------|-------------------|
+| 機型 | MacBook Pro M4 | MacBook Pro M5 Max |
+| 記憶體 | 16 GB | **48 GB** |
+| 架構 | arm64 | arm64 |
+| OS | macOS 26.x | macOS 26.4.1 |
+| IP（初始） | — | 10.10.10.10 |
+| IP（最終） | — | **192.168.110.201** |
+| 外網 | 有 | 先無 → 後有（接上同網段 192.168.110.x） |
+| Homebrew | 有 | 無（用戶非 admin，無法 sudo brew） |
+| Xcode CLT | 有 | 無（install_name_tool、codesign 不可用） |
+| Rust | 有 | rustup 已安裝 (1.95.0) |
+| 專案目錄 | `/Users/accusys/momentry_core_0.1/` | `~/momentry_core_0.1/`（已 clone） |
+
+## 2. 模型規格
+
+| 屬性 | 值 |
+|------|-----|
+| 模型 | **Gemma 4 31B-it**（Image-Text-to-Text） |
+| 參數量 | 33B (30,697,345,596) |
+| 量化 | Q5_K_M |
+| GGUF 大小 | **20.16 GB** (`21658399744 bytes`) |
+| Embedding dim | 5376 |
+| Vocabulary | 262144 |
+| Context | 4096 (訓練 262144) |
+| 來源 | `unsloth/gemma-4-31B-it-GGUF` |
+| HF 下載數 | 1,685,377 |
+| HF 許可 | Gated（需 `huggingface-cli login`） |
+| License | Gemma (Apache 2.0 derived) |
+
+## 3. Binary 與依賴
+
+### 3.1 建置方式
+
+llama.cpp 從 source build，不透過 Homebrew。原因：Homebrew binary 有**絕對路徑** dylib 參照，無法搬移至 M5。
+
+```bash
+# M4 上執行
+cd /tmp
+git clone https://github.com/ggerganov/llama.cpp.git
+cd llama.cpp
+cmake -B build -DGGML_METAL=ON
+cmake --build build -j10 --target llama-server
+```
+
+### 3.2 Binary 依賴
+
+llama-server binary 依賴以下 dylib（共 26 個檔案）：
+
+| 類別 | 檔案 | 來源 |
+|------|------|------|
+| 核心 GGML | `libggml.0.dylib`, `libggml.dylib` | `build/bin/` |
+| 核心 GGML | `libggml-base.0.dylib`, `libggml-base.dylib` | `build/bin/` |
+| Metal GPU | `libggml-metal.0.dylib`, `libggml-metal.dylib` | `build/bin/` |
+| CPU | `libggml-cpu.0.dylib`, `libggml-cpu.dylib` | `build/bin/` |
+| BLAS | `libggml-blas.0.dylib`, `libggml-blas.dylib` | `build/bin/` |
+| LLama | `libllama.0.dylib`, `libllama.dylib` | `build/bin/` |
+| LLamaCommon | `libllama-common.0.dylib`, `libllama-common.dylib` | `build/bin/` |
+| MTMD | `libmtmd.0.dylib`, `libmtmd.dylib` | `build/bin/` |
+| OpenSSL | `libssl.3.dylib`, `libcrypto.3.dylib` | `/opt/homebrew/opt/openssl@3/lib/` |
+
+### 3.3 @rpath 修復
+
+build 時期 embedded 的 @rpath 指向 `/tmp/llama.cpp/build/bin/`，需改為 `@executable_path/../lib`。
+
+在 **M4** 上執行（Xcode CLT 可用）：
+
+```bash
+cp build/bin/llama-server /tmp/llama_final
+chmod +w /tmp/llama_final
+
+# 修復 OpenSSL 絕對路徑
+install_name_tool -change /opt/homebrew/opt/openssl@3/lib/libssl.3.dylib @rpath/libssl.3.dylib /tmp/llama_final
+install_name_tool -change /opt/homebrew/opt/openssl@3/lib/libcrypto.3.dylib @rpath/libcrypto.3.dylib /tmp/llama_final
+
+# 修復 GGML 絕對路徑（Homebrew build 才需要，source build 不需要）
+install_name_tool -change /opt/homebrew/opt/ggml/lib/libggml.0.dylib @rpath/libggml.0.dylib /tmp/llama_final
+install_name_tool -change /opt/homebrew/opt/ggml/lib/libggml-base.0.dylib @rpath/libggml-base.0.dylib /tmp/llama_final
+
+# 修正 @rpath
+install_name_tool -delete_rpath /tmp/llama.cpp/build/bin /tmp/llama_final
+install_name_tool -add_rpath @executable_path/../lib /tmp/llama_final
+
+# 重新簽章（install_name_tool 會破壞 code signature）
+codesign --force --sign - /tmp/llama_final
+```
+
+### 3.4 libssl.3.dylib 自身也需修復
+
+libssl.3.dylib 內部也參照了 `/opt/homebrew/Cellar/openssl@3/3.6.1/lib/libcrypto.3.dylib`：
+
+```bash
+cp /opt/homebrew/opt/openssl@3/lib/libssl.3.dylib /tmp/libssl_fixed.dylib
+cp /opt/homebrew/opt/openssl@3/lib/libcrypto.3.dylib /tmp/libcrypto_fixed.dylib
+chmod +w /tmp/libssl_fixed.dylib /tmp/libcrypto_fixed.dylib
+install_name_tool -change /opt/homebrew/Cellar/openssl@3/3.6.1/lib/libcrypto.3.dylib @loader_path/libcrypto.3.dylib /tmp/libssl_fixed.dylib
+codesign --force --sign - /tmp/libssl_fixed.dylib /tmp/libcrypto_fixed.dylib
+```
+
+### 3.5 全部傳送至 M5
+
+```bash
+# 模型（20GB）
+scp ~/llama.cpp/models/gemma-4-31B-it-Q5_K_M.gguf \
+  accusys@192.168.110.201:~/models/
+
+# binary + 全部 dylib
+ssh accusys@192.168.110.201 'rm -rf ~/llama && mkdir -p ~/llama/bin ~/llama/lib'
+scp /tmp/llama_final accusys@192.168.110.201:~/llama/bin/llama-server
+scp /tmp/llama.cpp/build/bin/*.dylib accusys@192.168.110.201:~/llama/lib/
+scp /tmp/libssl_fixed.dylib accusys@192.168.110.201:~/llama/lib/libssl.3.dylib
+scp /tmp/libcrypto_fixed.dylib accusys@192.168.110.201:~/llama/lib/libcrypto.3.dylib
+```
+
+## 4. 啟動與驗證
+
+### 4.1 一次性手動啟動
+
+```bash
+ssh accusys@192.168.110.201
+export DYLD_LIBRARY_PATH=$HOME/llama/lib
+codesign --force --sign - ~/llama/bin/llama-server
+codesign --force --sign - ~/llama/lib/*.dylib
+nohup ~/llama/bin/llama-server \
+  -m ~/models/gemma-4-31B-it-Q5_K_M.gguf \
+  --host 0.0.0.0 --port 8081 \
+  --n-gpu-layers 999 --ctx-size 4096 \
+  --threads 10 --mlock \
+  --reasoning off \
+  > ~/llama.log 2>&1 &
+```
+
+### 4.2 啟動腳本
+
+`~/start_llm.sh`（已建立）：
+
+```bash
+#!/bin/bash
+export DYLD_LIBRARY_PATH=$HOME/llama/lib
+pkill -9 -f llama-server 2>/dev/null
+sleep 1
+nohup $HOME/llama/bin/llama-server \
+  -m $HOME/models/gemma-4-31B-it-Q5_K_M.gguf \
+  --host 0.0.0.0 --port 8081 \
+  --n-gpu-layers 999 --ctx-size 4096 \
+  --threads 10 --mlock \
+  --reasoning off \
+  > $HOME/llama.log 2>&1 &
+echo "llama-server PID: $!"
+```
+
+### 4.3 參數說明
+
+| 參數 | 值 | 說明 |
+|------|-----|------|
+| `-m` | `~/models/gemma-4-31B-it-Q5_K_M.gguf` | 模型路徑 |
+| `--host` | `0.0.0.0` | 綁定所有網路介面 |
+| `--port` | `8081` | HTTP API port |
+| `--n-gpu-layers` | `999` | 所有層進 GPU (Metal) |
+| `--ctx-size` | `4096` | 上下文長度 |
+| `--threads` | `10` | M5 Max P-core 數量 |
+| `--mlock` | — | 鎖住記憶體以防 swap |
+| `--reasoning` | `off` | 關閉 thinking，否則 content 進 `reasoning_content` |
+| `DYLD_LIBRARY_PATH` | `~/llama/lib` | dylib 搜尋路徑 |
+
+### 4.4 啟動過程中遇到的問題
+
+| # | 問題 | 原因 | 解決 |
+|---|------|------|------|
+| 1 | `Library not loaded: libmtmd.0.dylib` | 未拷貝 Metal 相關 dylib | 從 build 拷貝全部 26 個 dylib |
+| 2 | `Library not loaded: /opt/homebrew/.../libssl.3.dylib` | binary 有 OpenSSL 絕對路徑 | `install_name_tool -change → @rpath` |
+| 3 | `Killed: 9` (exit 137) | code signature 被破壞 | `codesign --force --sign -` |
+| 4 | `Library not loaded: /opt/homebrew/Cellar/.../libcrypto.3.dylib` | libssl.3.dylib 內部也有絕對路徑 | `install_name_tool` 修復 libssl |
+| 5 | `no backends are loaded` | 缺少 Metal GPU backend | source build 時需 `-DGGML_METAL=ON` |
+| 6 | `couldn't bind HTTP server socket` | 前一個 process 未完全釋放 port | `pkill -9 -f llama-server` 先 |
+| 7 | **content 全在 reasoning_content** | Gemma4 預設為 thinking model | `--reasoning off` |
+
+## 5. API 驗證
+
+### 5.1 Health Check
+
+```bash
+curl -s http://192.168.110.201:8081/health
+# → {"status":"ok"}
+```
+
+### 5.2 推理測試（--reasoning off 後）
+
+```bash
+curl -s http://192.168.110.201:8081/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "gemma-4-31B-it-Q5_K_M.gguf",
+    "messages": [{"role": "user", "content": "Hello"}],
+    "max_tokens": 100
+  }'
+```
+
+回應（OpenAI-compatible）:
+
+```json
+{
+  "choices": [{
+    "finish_reason": "stop",
+    "message": {
+      "role": "assistant",
+      "content": "Hello! How can I help you today?",
+      "reasoning_content": ""
+    }
+  }],
+  "usage": {
+    "completion_tokens": 100,
+    "prompt_tokens": 18,
+    "total_tokens": 118
+  },
+  "model": "gemma-4-31B-it-Q5_K_M.gguf",
+  "object": "chat.completion"
+}
+```
+
+### 5.3 效能
+
+| 指標 | 實測 |
+|------|------|
+| Prompt 速度 | 60.8 tok/s |
+| 生成速度 | **25.8 tok/s** |
+| Prompt 延遲 | 296 ms（18 tokens） |
+| 生成延遲 | 387 ms（10 tokens） |
+
+## 6. 整合至 OpenCode
+
+`~/.config/opencode/config.json` 中新增 provider：
+
+```json
+{
+  "m5-gemma4": {
+    "npm": "@ai-sdk/openai-compatible",
+    "name": "M5 Max Gemma 4",
+    "options": { "baseURL": "http://192.168.110.201:8081/v1" },
+    "models": {
+      "gemma-4-31B-it-Q5_K_M.gguf": { "name": "Gemma 4 31B" }
+    }
+  }
+}
+```
+
+預設 model 設為 `"m5-gemma4/gemma-4-31B-it-Q5_K_M.gguf"`。Provider list 確認：
+
+```bash
+opencode models m5-gemma4
+# → m5-gemma4/gemma-4-31B-it-Q5_K_M.gguf
+```
+
+## 7. M5 網路異動記錄
+
+| 時間 | IP | 網路 | 原因 |
+|------|-----|------|------|
+| 初始 | `10.10.10.10` | bridge (Thunderbolt) | 無外網，需透過 M4 NAT |
+| 切換後 | `192.168.110.201` | en0 (WiFi/Ethernet) | 改接同網段，有外網 |
+
+## 8. Rust 安裝（for Momentry dev）
+
+```bash
+curl --proto "=https" --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
+source $HOME/.cargo/env
+```
+
+- rustc 1.95.0
+- cargo 1.95.0
+- 免 sudo
+
+## 9. 記憶體使用
+
+```
+48 GB total
+  ├─ 20 GB  Gemma 4 31B Q5_K_M (process RSS ~28 GB)
+  ├─  4 GB  macOS + 系統
+  └─ 24 GB  剩餘
+```
+
+實測啟動後 RSS: `28,325,600 KB` (~28 GB)。
+
+## 10. 維護指令
+
+| 操作 | 指令 |
+|------|------|
+| 啟動 | `ssh accusys@192.168.110.201 '~/start_llm.sh'` |
+| 停止 | `ssh accusys@192.168.110.201 'pkill -9 -f llama-server'` |
+| 查看日誌 | `ssh accusys@192.168.110.201 'tail -50 ~/llama.log'` |
+| 健康檢查 | `curl http://192.168.110.201:8081/health` |
+| 模型檔案 | `~/models/gemma-4-31B-it-Q5_K_M.gguf (20G)` |
+| Binary 與 lib | `~/llama/bin/llama-server`, `~/llama/lib/*.dylib` |
+| config | `~/.config/opencode/config.json` |
+| 監控 | `htop -p $(pgrep llama-server)` |
+| 記憶體 | `ps -o rss= -p $(pgrep llama-server)` |
+
+## 11. 已知限制
+
+- **Thinking model**: Gemma4 為 thinking 模型（`--reasoning off` 關閉後 content 正常，但某些場景可能需要 reasoning）
+- **無 Homebrew**: 非 admin 帳號，無法 `brew install`。Momentry 其他服務（PostgreSQL, Redis, MongoDB）需用 portable binary 手動安裝
+- **無 Xcode CLT**: `install_name_tool`, `codesign` 不可用於 M5。binary 修復需在 M4 完成後 scp
@@ -0,0 +1,296 @@
+---
+document_type: "architecture_design"
+service: "MOMENTRY_CORE"
+title: "Vision Agent — Rust Integration Design"
+date: "2026-05-10"
+version: "V1.0"
+status: "active"
+owner: "M5"
+created_by: "OpenCode"
+current_state: "draft"
+tags:
+  - "vision-agent"
+  - "rust-integration"
+  - "python-executor"
+  - "grounding-dino"
+  - "architecture"
+ai_query_hints:
+  - "Vision Agent Rust 整合架構與 PythonExecutor 設計"
+  - "Grounding DINO 無法 ONNX 匯出的原因與解決方案"
+  - "Rust 端 detect/search/multimodal handler 實作方式"
+  - "PythonExecutor persistent mode 與 model cache 設計"
+  - "Vision Agent 從 Flask 5052 遷移至 Rust 3003 的遷移計畫"
+related_documents:
+  - "../VISION_AGENT_API_V1.0.0.md"
+---
+
+# Vision Agent — Rust Integration Design
+
+**Goal:** Replace standalone Python Flask service (port 5052) with a Rust-native agent under `3003/api/v1/agents/vision/*`, following the same pattern as 5W1H, Identity, and Translate agents.
+
+---
+
+## Architecture
+
+```
+Client → 3003 (Rust Axum)
+           │
+           ├── /api/v1/agents/vision/detect      → PythonExecutor → vision_inference.py
+           ├── /api/v1/agents/vision/search       → PythonExecutor → vision_inference.py
+           ├── /api/v1/agents/vision/multimodal   → Rust DB query + PythonExecutor
+           └── /api/v1/agents/vision/models       → pure Rust (no Python needed)
+```
+
+### Why PythonExecutor?
+
+Grounding DINO uses `MultiScaleDeformableAttention` — a PyTorch custom CUDA kernel with no Rust/candle/ort equivalent. ONNX export is also impossible due to this custom op. Python is the only viable runtime.
+
+This matches the project's existing processor pattern:
+
+| Component | Rust | Inference |
+|-----------|------|-----------|
+| ASR | `PythonExecutor` | `asr_processor.py` |
+| ASRX | `PythonExecutor` | `asrx_processor_custom.py` |
+| YOLO | `PythonExecutor` | `yolo_processor.py` |
+| **Vision** | **`PythonExecutor`** | **`vision_inference.py`** |
+
+---
+
+## Config
+
+Add to existing `MOMENTRY_*` env var pattern in `src/core/config.rs`:
+
+```rust
+// Existing pattern — env::var("MOMENTRY_*")
+pub fn vision_enabled() -> bool {
+    env::var("MOMENTRY_VISION_ENABLED")
+        .unwrap_or_else(|_| "true".to_string())
+        .parse()
+        .unwrap_or(true)
+}
+```
+
+### Environment Variables
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `MOMENTRY_VISION_ENABLED` | `true` | Enable/disable all vision endpoints |
+| `MOMENTRY_VISION_MODEL` | `grounding-dino` | Default model: `grounding-dino` or `fusion` |
+| `MOMENTRY_VISION_GDINO_MODEL` | `IDEA-Research/grounding-dino-base` | HF model ID or local path |
+| `MOMENTRY_VISION_PALIGEMMA_ENABLED` | `false` | Enable PaliGemma (requires ~3GB download) |
+| `MOMENTRY_VISION_THRESHOLD` | `0.1` | Default confidence threshold |
+| `MOMENTRY_VISION_DEVICE` | `mps` on Apple Silicon, else `cpu` | Inference device |
+| `MOMENTRY_VISION_TIMEOUT` | `30000` | PythonExecutor timeout (ms) |
+
+---
+
+## Rust Route — `src/api/vision_agent_api.rs`
+
+### Route Registration
+
+```rust
+pub fn vision_agent_routes() -> Router<AppState> {
+    Router::new()
+        .route("/api/v1/agents/vision/detect", post(vision_detect))
+        .route("/api/v1/agents/vision/search", post(vision_search))
+        .route("/api/v1/agents/vision/multimodal", post(vision_multimodal))
+        .route("/api/v1/agents/vision/models", get(vision_models))
+}
+```
+
+Mount in `server.rs`:
+
+```rust
+if config::vision_enabled() {
+    app = app.merge(vision_agent_routes());
+}
+```
+
+### Detect Handler Flow
+
+```
+1. Receive JSON with {frame, query, model, threshold}
+2. Parse query → extract prompt (e.g., "find the gun" → "gun")
+3. Resolve frame → timestamp (for Python compatibility)
+4. Call PythonExecutor::run_script("vision_inference.py", args)
+5. Parse Python stdout → JSON response
+6. Return formatted result
+```
+
+### Frame/Time Resolution
+
+```rust
+fn resolve_frame(data: &Value, fps: f64) -> i64 {
+    // Priority: frame > time
+    if let Some(f) = data.get("frame").and_then(|v| v.as_i64()) {
+        return f;
+    }
+    if let Some(t) = data.get("time").and_then(|v| v.as_f64()) {
+        return (t * fps) as i64;
+    }
+    0
+}
+```
+
+### JSON Protocol (Rust ↔ Python)
+
+**Stdin (Rust → Python):**
+
+```json
+{
+  "action": "detect",
+  "frame": 136525,
+  "timestamp": 5461.0,
+  "prompt": "gun",
+  "model": "grounding-dino",
+  "threshold": 0.1,
+  "weights": {"grounding-dino": 0.6, "paligemma": 0.4},
+  "config": {
+    "gdino_model": "IDEA-Research/grounding-dino-base",
+    "paligemma_model": "google/paligemma-3b-mix-224",
+    "device": "mps"
+  }
+}
+```
+
+**Stdout (Python → Rust):**
+
+```json
+{
+  "success": true,
+  "frame": 136525,
+  "timestamp": 5461.0,
+  "detections": [
+    {"bbox": [726.2, 567.4, 969.0, 694.6], "score": 0.476, "label": "gun"}
+  ],
+  "time_ms": 345.2
+}
+```
+
+---
+
+## Python Script — `scripts/vision_inference.py`
+
+### Design
+
+- **No Flask.** Pure stdin/stdout protocol.
+- **Model cache.** `_model` global persists across PythonExecutor calls.
+- **Single entry point.** Reads JSON from stdin, dispatches by `action` field.
+
+```python
+#!/opt/homebrew/bin/python3.11
+"""
+Vision inference — called by Rust PythonExecutor.
+Reads JSON from stdin, runs inference, writes JSON to stdout.
+"""
+import json, sys, os, torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
+
+_model = None
+_processor = None
+_device = None
+
+def load_model():
+    global _model, _processor, _device
+    if _model is not None:
+        return _model, _processor
+    _device = os.environ.get("MOMENTRY_VISION_DEVICE", "mps")
+    model_name = os.environ.get("MOMENTRY_VISION_GDINO_MODEL",
+                                "IDEA-Research/grounding-dino-base")
+    _processor = AutoProcessor.from_pretrained(model_name)
+    _model = AutoModelForZeroShotObjectDetection.from_pretrained(model_name).to(_device)
+    return _model, _processor
+
+def detect_gdino(img, prompt, threshold):
+    model, processor = load_model()
+    inputs = processor(images=img, text=f"{prompt}.", return_tensors="pt").to(_device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    dets = processor.post_process_grounded_object_detection(
+        outputs, threshold=threshold,
+        target_sizes=[img.size[::-1]])[0]
+    results = []
+    for i in range(len(dets["boxes"])):
+        results.append({
+            "bbox": [round(v, 1) for v in dets["boxes"][i].tolist()],
+            "score": round(dets["scores"][i].item(), 3),
+            "label": prompt,
+        })
+    return results
+
+def main():
+    input_data = json.load(sys.stdin)
+    action = input_data.get("action", "detect")
+    
+    if action == "detect":
+        # ... run inference
+    elif action == "search":
+        # ... iterate frames
+    elif action == "models":
+        # ... return model info
+    
+    json.dump(result, sys.stdout)
+    sys.stdout.flush()
+
+if __name__ == "__main__":
+    main()
+```
+
+---
+
+## Model Lifecycle
+
+### Issue
+
+GDINO loads in ~4s (download + CUDA init + weight load). PythonExecutor starts a new process per call — this would add 4s latency to every request.
+
+### Solution: Warm Process
+
+Use `PythonExecutor` in persistent/session mode where the Python process stays alive between calls. The `_model` global cache keeps the model in memory.
+
+From `src/core/processor/executor.rs` — check if persistent mode is supported, or use a simple approach:
+
+```rust
+// Keep Python process alive for multiple calls
+let executor = PythonExecutor::new("vision_inference.py")
+    .persistent(true)  // reuse same process
+    .timeout_ms(30000);
+```
+
+If `PythonExecutor` doesn't support persistent mode, implement a simple sidecar:
+
+```rust
+// Launch Python process on agent init
+let child = std::process::Command::new(python_path)
+    .arg(script_path)
+    .stdin(std::process::Stdio::piped())
+    .stdout(std::process::Stdio::piped())
+    .spawn()?;
+
+// Write request, read response per call
+child.stdin.write_all(json_request.as_bytes())?;
+let response = child.stdout.read_to_string(&mut buffer)?;
+```
+
+---
+
+## Files to Create/Modify
+
+| File | Action | Description |
+|------|--------|-------------|
+| `src/api/vision_agent_api.rs` | **Create** | Rust route handlers |
+| `src/core/config.rs` | **Modify** | Add `MOMENTRY_VISION_*` env vars |
+| `src/api/server.rs` | **Modify** | Merge `vision_agent_routes()` |
+| `scripts/vision_inference.py` | **Create** | Python inference script (stdin/stdout) |
+| `API_V1.0.0/VISION_AGENT_API_V1.0.0.md` | Created | API docs |
+
+## Migration Plan
+
+| Phase | Steps | Status |
+|-------|-------|--------|
+| **1** | Create `vision_inference.py` (stdin/stdout, model cache) | ⏳ |
+| **2** | Create `vision_agent_api.rs` (detect + search + multimodal handlers) | ⏳ |
+| **3** | Add config + mount routes to 3003 | ⏳ |
+| **4** | Test detect/search via 3003 (no 5052) | ⏳ |
+| **5** | Deprecate 5052 Flask service | ⏳ |
@@ -0,0 +1,91 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "5W1H+ Agent v1.0.0"
+date: "2026-05-07"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+tags:
+  - "momentry"
+  - "agent"
+  - "5w1h"
+  - "llm"
+  - "summary"
+related_documents:
+  - "../../TRACE/TRACE_API_REFERENCE_V1.0.0.md"
+  - "../CHUNK_DEFINITION_V1.0.0.md"
+  - "../VECTOR_SPEC_V1.0.0.md"
+---
+
+# 5W1H+ Agent v1.0.0
+
+## 概述
+
+對每個 cut scene 產生 5W1H+ 摘要（parent summary + child enhanced text）。
+
+## 遞迴 Context（Story So Far）
+
+採用方案 B：每段 scene 的 LLM call 帶入前面所有 scene 的摘要。
+
+```
+Scene 1 → LLM(context="")                  → summary_1
+Scene 2 → LLM(context=summary_1)           → summary_2
+Scene 3 → LLM(context=summary_1+summary_2) → summary_3
+```
+
+Context truncation：保留最近 ~500 tokens 的前情，避免超過模型 limit。
+
+## Prompt 結構
+
+每個 scene 的 LLM call 包含以下資訊：
+
+| Prompt 區塊 | 來源 | 說明 |
+|------------|------|------|
+| Scene time | chunk metadata | 目前 scene 的時間區間 |
+| Dialogue | sentences in scene | 該 scene 內的對話行 |
+| Actors present | face_detections JOIN identity_bindings JOIN identities | 場景中出現的演員 |
+| Objects detected | pre_chunks WHERE processor_type='yolo' | YOLO 偵測到的物體 |
+| Face traces | face_detections JOIN identity_bindings JOIN identities | trace 與對應的演員名稱 |
+| Active speakers | pre_chunks WHERE processor_type='asrx' JOIN identity_bindings | 說話者與對應的演員 |
+| Story so far | 前 N 個 scene 的 parent_summary | 前情摘要 |
+
+## LLM 模型
+
+| 項目 | 值 |
+|------|-----|
+| 模型 | Gemma4 26B MoE (Q5_K_M, 18GB) |
+| 部署 | llama-server（Metal GPU, port 8082） |
+| 環境變數 | `MOMENTRY_LLM_SUMMARY_URL=http://localhost:8082/v1/chat/completions` |
+| 溫度 | 0.1 |
+| max_tokens | 4096 |
+
+## 產出
+
+| 輸出 | 儲存位置 | 說明 |
+|------|---------|------|
+| parent_summary | `cut.summary_text` | 5 句 scene_summary（5W1H 流暢段落） |
+| parent_5w1h | `cut.metadata -> 5w1h` | 結構化 who/what/where/when/why/how |
+| child_enhanced | `sentence.text_content` | 自包含的 enhanced sentence（供 embedding + search） |
+| child_5w1h | `sentence.content -> 5w1h` | 逐句的 5w1h 結構 |
+| embedding | `sentence.embedding` | EmbeddingGemma 300M 768D（產出 summary 後自動 vectorize） |
+
+## API
+
+```
+POST /api/v1/agents/5w1h/analyze
+POST /api/v1/agents/5w1h/batch
+GET  /api/v1/agents/5w1h/status
+```
+
+## Pipeline 觸發
+
+Job Worker 中的 P4 trigger：
+
+```rust
+// all_completed + has_cut + has_asr → run_5w1h_agent(db, uuid)
+```
+
+## 選型文件
+
+詳細方案比較：`M5_workspace/2026-05-07_5w1h_recursive_summary_design.md`
@@ -0,0 +1,84 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "Identity Agent v1.0.0"
+date: "2026-05-07"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+tags:
+  - "momentry"
+  - "agent"
+  - "identity"
+  - "face"
+  - "speaker"
+related_documents:
+  - "../DATA_SCHEMA_FILE_IDENTITY_V1.0.0.md"
+  - "../../TRACE/TRACE_API_REFERENCE_V1.0.0.md"
+  - "../PROCESSORS/FACE_V1.0.0.md"
+  - "../PROCESSORS/ASRX_V1.0.0.md"
+---
+
+# Identity Agent v1.0.0
+
+## 概述
+
+將 face trace 與 speaker 綁定到人物身份（identity），實現跨場景的人員辨識。
+
+## 處理流程
+
+```
+face_clustered.json + asrx.json
+  → extract_persons (face clusters)
+  → extract_speakers (ASRX segments)
+  → analyze_person_speaker_overlap
+  → 寫入 dev.identities
+  → match_faces_iterative (TMDb seed → propagation)
+  → bind_speakers (speaker_id → identity_id)
+```
+
+## 迭代多角度 Face Matching
+
+```
+TMDb seeds (12 identities, with mulitple angles)
+  → Round 1: ~33% trace-to-identity
+  → Round 2: propagate matched traces as new seeds
+  → Round 3: propagate again
+  → Final: 99% binding (6,175 / 6,186 face detections)
+```
+
+## Speaker Binding
+
+```
+face_detections (trace_id, frame_number)
+  + ASRX segments (speaker_id, start_time, end_time)
+  → frame-level overlap computation
+  → winner-takes-all: best_overlap > 30%
+  → 寫入 identity_bindings (identity_type='speaker')
+```
+
+## Pipeline 觸發
+
+Job Worker 中的 P3 trigger：
+
+```rust
+// has_face + has_asrx → run_identity_agent(db, uuid)
+```
+
+觸發時機：all_completed，face 與 asrx 皆完成後。
+
+## DB 結構
+
+| Table | 用途 |
+|-------|------|
+| `identities` | 身份主表（name, type, metadata, embedding） |
+| `identity_bindings` | 綁定表（identity_id → trace_id 或 speaker_id） |
+| `file_identities` | 檔案級身份對應 |
+
+## API
+
+```
+POST /api/v1/agents/identity/analyze
+POST /api/v1/agents/identity/suggest
+GET  /api/v1/agents/identity/status
+```
@@ -0,0 +1,175 @@
+---
+document_type: "reference_doc"
+service: "MOMENTRY_CORE"
+title: "Momentry Core API 字典 V1.0.0"
+date: "2026-05-06"
+version: "V1.3"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+tags:
+  - "momentry"
+  - "core"
+  - "api"
+  - "dictionary"
+  - "v1.0.0"
+ai_query_hints:
+  - "Momentry Core API 字典查詢"
+  - "API 端點與參數說明"
+  - "API 回應格式定義"
+  - "查詢所有 Public/Internal/Admin API 端點列表"
+  - "API 端點的 HTTP 方法與路徑結構"
+  - "搜尋 API 有哪些端點（search/bm25/hybrid/visual）"
+  - "API 端點的狀態分類（Public/Internal/Admin）"
+related_documents:
+  - "API_V1.0.0/MOMENTRY_CORE_API_V1.0.0.md"
+  - "API_V1.0.0/API_USAGE_DEMO_V1.0.0.md"
+  - "API_V1.0.0/CHUNK_DEFINITION_V1.0.0.md"
+  - "API_V1.0.0/VECTOR_SPEC_V1.0.0.md"
+---
+
+# Momentry Core API 字典 V1.0.0
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| Public API | 供前端與外部系統使用的標準介面 |
+| Internal API | 系統內部流程或狀態查詢用 |
+| Admin API | 管理員專用 |
+| file_uuid | 32 碼 birth UUID（MAC + time + path + filename） |
+| identity_uuid | 32 碼 UUIDv5（source + external_id） |
+| RESTful | 以資源為中心的 API 設計風格，collection 複數、resource 單數 |
+
+## 端點統計
+
+| 分類 | 數量 | 說明 |
+|---|---|---|
+| Public | 40 | 供前端與外部系統使用的標準介面 |
+| Internal | 4 | 系統內部流程或狀態查詢 |
+| Admin | 3 | 管理員專用 |
+| Health | 2 | 服務健康檢查 |
+| **總計** | **48** | 所有已註冊路由 |
+
+## 設計原則
+
+### 1. RESTful 命名規範
+- Collection（複數）: `/api/v1/files`, `/api/v1/identities`
+- Resource（單數）: `/api/v1/file/:file_uuid`, `/api/v1/identity/:identity_uuid`
+- Action on resource: `/api/v1/identity/:identity_uuid/bind`
+
+### 2. File-Centric
+- 每個媒體檔案由 32 碼 UUID (`file_uuid`) 唯一標識
+- File 是所有資料的根節點，Chunk、Job 隸屬於特定 File
+
+### 3. Global Identity
+- Identity 跨檔案關聯，不受單一檔案限制
+- 透過 bind/unbind/mergeinto 管理 Face → Identity 的直接 FK 綁定（V4.0）
+
+---
+
+## 1. 系統與認證
+
+| 方法 | 路徑 | 狀態 |
+|------|------|------|
+| `GET` | `/health` | Health |
+| `GET` | `/health/detailed` | Health |
+| `POST` | `/api/v1/auth/login` | Public |
+| `POST` | `/api/v1/auth/logout` | Public |
+
+## 2. 檔案管理 (Files)
+
+| 方法 | 路徑 | 狀態 |
+|------|------|------|
+| `GET` | `/api/v1/files` | Public |
+| `GET` | `/api/v1/files/scan` | Public |
+| `POST` | `/api/v1/files/register` | Public |
+| `POST` | `/api/v1/unregister` | Public |
+| `GET` | `/api/v1/file/:file_uuid` | Public |
+| `GET` | `/api/v1/file/:file_uuid/probe` | Public |
+| `POST` | `/api/v1/file/:file_uuid/process` | Public |
+| `GET` | `/api/v1/file/:file_uuid/identities` | Public |
+| `GET` | `/api/v1/file/:file_uuid/chunks` | Public |
+| `GET` | `/api/v1/file/:file_uuid/thumbnail?frame=&x=&y=&w=&h=` | Public |
+| `POST` | `/api/v1/file/:file_uuid/face_trace/sortby` | Public |
+
+## 3. 管線與任務 (Pipeline & Jobs)
+
+| 方法 | 路徑 | 狀態 |
+|------|------|------|
+| `GET` | `/api/v1/progress/:file_uuid` | Public |
+| `GET` | `/api/v1/jobs` | Public |
+| `GET` | `/api/v1/job/:job_id` | Public |
+| `GET` | `/api/v1/rule/:rule_id/status` | Public |
+| `POST` | `/api/v1/resource/register` | Internal |
+| `POST` | `/api/v1/resource/heartbeat` | Internal |
+| `GET` | `/api/v1/resources` | Internal |
+
+## 4. 搜尋 (Search)
+
+| 方法 | 路徑 | 狀態 |
+|------|------|------|
+| `POST` | `/api/v1/search` | Public |
+| `POST` | `/api/v1/search/bm25` | Public |
+| `POST` | `/api/v1/search/hybrid` | Public |
+| `POST` | `/api/v1/search/smart` | Public |
+| `POST` | `/api/v1/search/universal` | Public |
+| `POST` | `/api/v1/search/frames` | Public |
+| `POST` | `/api/v1/search/visual` | Public |
+| `POST` | `/api/v1/search/visual/class` | Public |
+| `POST` | `/api/v1/search/visual/density` | Public |
+| `POST` | `/api/v1/search/visual/combination` | Public |
+| `POST` | `/api/v1/search/visual/stats` | Public |
+
+## 5. 身份管理 (Identity)
+
+| 方法 | 路徑 | 狀態 |
+|------|------|------|
+| `GET` | `/api/v1/identities` | Public |
+| `POST` | `/api/v1/identity` | Public |
+| `GET` | `/api/v1/identity/:identity_uuid` | Public |
+| `DELETE` | `/api/v1/identity/:identity_uuid` | Public |
+| `GET` | `/api/v1/identity/:identity_uuid/files` | Public |
+| `GET` | `/api/v1/identity/:identity_uuid/chunks` | Public |
+| `POST` | `/api/v1/identity/:identity_uuid/bind` | Public |
+| `POST` | `/api/v1/identity/:identity_uuid/unbind` | Public |
+| `POST` | `/api/v1/identity/:from_uuid/mergeinto` | Public |
+
+## 6. 臉部 (Faces)
+
+| 方法 | 路徑 | 狀態 |
+|------|------|------|
+| `GET` | `/api/v1/faces/candidates` | Public |
+
+## 7. 代理人 (Agents)
+
+| 方法 | 路徑 | 狀態 |
+|------|------|------|
+| `POST` | `/api/v1/agents/translate` | Public |
+| `POST` | `/api/v1/agents/identity/analyze` | Public |
+| `POST` | `/api/v1/agents/identity/suggest` | Public |
+| `GET` | `/api/v1/agents/identity/status` | Public |
+| `POST` | `/api/v1/agents/suggest/merge` | Public |
+| `POST` | `/api/v1/agents/5w1h/analyze` | Public |
+| `POST` | `/api/v1/agents/5w1h/batch` | Public |
+| `GET` | `/api/v1/agents/5w1h/status` | Public |
+
+## 8. 狀態與管理 (Stats & Admin)
+
+| 方法 | 路徑 | 狀態 |
+|------|------|------|
+| `GET` | `/api/v1/stats/sftpgo` | Internal |
+| `GET` | `/api/v1/stats/inference` | Internal |
+| `POST` | `/api/v1/config/cache` | Admin |
+| `POST` | `/api/v1/config/auto-pipeline` | Admin |
+| `POST` | `/api/v1/config/watcher-auto-register` | Admin |
+
+---
+
+## 變更歷史
+
+| 版本 | 日期 | 作者 | 說明 |
+|------|------|------|------|
+| V1.3 | 2026-05-06 | OpenCode | 新增 `face_thumbnail` ffmpeg 即時裁切端點 + `face_trace/sortby` 端點；portal 修復 hardcoded URL/API key/legacy endpoints |
+| V1.1 | 2026-05-01 | OpenCode | Route fixes + arch notes |
+| V1.0 | 2026-04 | OpenCode | 初始版本 |
@@ -0,0 +1,310 @@
+---
+document_type: "reference_doc"
+service: "MOMENTRY_CORE"
+title: "Momentry Core API 參考文件 V1.0.0 (Demo 完整指南)"
+date: "2026-05-01"
+version: "V3.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+tags:
+  - "api"
+  - "reference"
+  - "v1.0.0"
+  - "demo"
+  - "marcom"
+ai_query_hints:
+  - "查詢 V1.0.0 Demo 所需 API 列表"
+  - "Momentry Core Demo 流程如何使用 API？"
+  - "API 的檔案註冊、處理、臉部綁定流程"
+  - "Demo 流程中 Scan → Unregister → Register → Probe → Process → Faces → Bind 的完整步驟"
+  - "API 的 curl 範例與回應格式"
+  - "Process 回傳 400 Bad Request 的常見原因與解決方法"
+  - "臉部查詢回傳空結果的疑難排解步驟"
+related_documents:
+  - "STANDARDS/DOCS_STANDARD.md"
+  - "API_V1.0.0/MOMENTRY_CORE_API_V1.0.0.md"
+  - "TEST_REPORT_CLI.md"
+---
+
+# Momentry Core API 參考文件 V1.0.0 (Demo 完整指南)
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| file_uuid | 32 碼 SHA256 檔案識別碼 |
+| X-API-Key | API 認證方式，透過 HTTP Header 傳遞 |
+| Scan | 掃描檔案系統，列出所有檔案及當前狀態 |
+| Register | 將檔案加入資料庫系統 |
+| Probe | 讀取檔案 metadata（時長、解析度、幀率） |
+| Bind | 將臉部綁定到指定身份 |
+| Progress | 獲取處理進度與目前階段 |
+
+## 📊 文件統計 (Document Statistics)
+
+| 項目 | 數值 |
+|---|---|
+| **收錄端點** | 15+ (Demo 核心流程) |
+| **涵蓋率** | Demo 流程 100% |
+| **測試狀態** | ✅ CLI Verified |
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-01 |
+| 文件版本 | V3.0 |
+
+---
+
+## 1. Demo 流程總覽 (Demo Workflow)
+
+本文件專注於 **Demo 測試計畫** 所需的 API。以下是完整流程與對應 API：
+
+```
+1. 掃描狀態 (Scan)       → GET  /api/v1/files/scan
+2. 檔案重置 (Unregister)  → POST /api/v1/unregister
+3. 檔案註冊 (Register)    → POST /api/v1/files/register
+4. 檔案探測 (Probe)      → GET  /api/v1/files/:file_uuid/probe
+5. 開始處理 (Process)    → POST /api/v1/files/:file_uuid/process
+6. 監控進度 (Progress)   → GET  /api/v1/progress/:file_uuid**
+7. 查詢臉部 (Faces)      → GET  /api/v1/faces/candidates
+8. 綁定身份 (Bind)       → POST /api/v1/identities/bind
+```
+
+---
+
+## 2. 快速資訊
+
+- **Base URL (Dev)**: `http://localhost:3003`
+- **Base URL (Prod)**: `http://localhost:3002`
+- **認證方式**: Header `X-API-Key: muser_test_001`
+- **測試 Key**: `muser_test_001`
+
+---
+
+## 3. API 詳細說明 (依 Demo 順序)
+
+### 3.1 掃描檔案系統 (Scan Files)
+**路徑**: `GET /api/v1/files/scan`
+
+**用途**: 列出檔案系統中所有檔案及當前狀態，**是 Demo 流程的第一步**。
+
+**Response**:
+```json
+{
+    "files": [
+        {
+            "file_name": "A12T3-Share-User Experience of Thunderbolt 3 Shareable Storage.mp4",
+            "file_path": "/Users/accusys/momentry/var/sftpgo/data/demo/A12T3-Share-User Experience of Thunderbolt 3 Shareable Storage.mp4",
+            "file_uuid": "7ab7e25f48b58675e33aca44d15c1ecc",
+            "is_registered": true,
+            "status": "processing"
+        }
+    ],
+    "total": 20,
+    "registered_count": 20,
+    "unregistered_count": 0
+}
+```
+
+---
+
+### 3.2 取消註冊 (Unregister File)
+**路徑**: `POST /api/v1/unregister`
+
+**用途**: 從 Scan 結果中選取 `file_uuid`，對該檔案執行取消註冊。
+
+**Request**:
+```json
+{
+    "uuid": "53e3a229bf68878b7a799e811e097f9c"
+}
+```
+
+**Response**:
+```json
+{
+    "success": true,
+    "uuid": "53e3a229bf68878b7a799e811e097f9c",
+    "message": "File unregistered successfully"
+}
+```
+
+---
+
+### 3.3 註冊檔案 (Register File)
+**路徑**: `POST /api/v1/files/register`
+
+**用途**: 從 Scan 結果中選取 `file_path`，將檔案加入資料庫系統。
+
+**Request**:
+```json
+{
+    "file_path": "/Users/accusys/momentry/var/sftpgo/data/demo/view15.mp4"
+}
+```
+
+**Response**:
+```json
+{
+    "success": true,
+    "file_uuid": "53e3a229bf68878b7a799e811e097f9c",
+    "file_name": "view15.mp4",
+    "file_path": "/Users/.../demo/view15.mp4",
+    "already_exists": false
+}
+```
+
+---
+
+### 3.4 檔案探測 (Probe File)
+**路徑**: `GET /api/v1/files/:file_uuid/probe`
+
+**用途**: 讀取檔案的 metadata (時長、解析度、幀率)。**必須在 Process 前執行**。
+
+**Response**:
+```json
+{
+    "file_uuid": "7ab7e25f48b58675e33aca44d15c1ecc",
+    "file_name": "A12T3-Share-User Experience of Thunderbolt 3 Shareable Storage.mp4",
+    "duration": 621.55,
+    "width": 1920,
+    "height": 1080,
+    "fps": 29.97,
+    "cached": true
+}
+```
+
+---
+
+### 3.5 觸發處理 (Process File)
+**路徑**: `POST /api/v1/files/:file_uuid/process`
+
+**用途**: 啟動後端 Worker 進行分析 (ASR, Face, YOLO, 等)。
+
+**Request**:
+```json
+{}
+```
+
+**Response**:
+```json
+{
+    "success": true,
+    "message": "Processing started"
+}
+```
+
+---
+
+### 3.6 查詢進度 (Progress)
+**路徑**: `GET /api/v1/progress/:file_uuid`
+
+**用途**: 獲取處理進度與目前階段。
+
+**Response**:
+```json
+{
+    "file_uuid": "53e3a229bf68878b7a799e811e097f9c",
+    "overall_progress": 65,
+    "current_processor": "face",
+    "status": "running",
+    "processors": [
+        { "name": "probe", "status": "completed" },
+        { "name": "asr", "status": "completed" },
+        { "name": "face", "status": "running" }
+    ]
+}
+```
+
+---
+
+### 3.6 查詢未綁定臉部 (List Face Candidates)
+**路徑**: `GET /api/v1/faces/candidates`
+
+**用途**: 列出檔案中尚未綁定身份的臉部。
+
+**Query Parameters**:
+- `file_uuid` (必填): 檔案 UUID
+- `min_confidence` (選填): 最低信心值 (預設 0.5)
+- `page_size` (選填): 每頁數量 (預設 20)
+
+**Response**:
+```json
+{
+    "candidates": [
+        {
+            "id": 123,
+            "face_id": "123_RoleA",
+            "file_uuid": "384b0ff44aaaa1f14cb2cd63b3fea966",
+            "frame_number": 115,
+            "confidence": 0.98,
+            "bbox": { "x": 50, "y": 50, "w": 100, "h": 100 }
+        }
+    ],
+    "total": 1,
+    "page": 1,
+    "page_size": 20
+}
+```
+
+---
+
+### 3.7 綁定身份 (Bind Identity)
+**路徑**: `POST /api/v1/identities/bind`
+
+**用途**: 將臉部綁定到指定身份 (或建立新身份)。
+
+**Request**:
+```json
+{
+    "identity_id": 22,
+    "binding_type": "face",
+    "binding_value": "123_RoleA"
+}
+```
+
+**Response**:
+```json
+{
+    "success": true,
+    "message": "Bound face '123_RoleA' to Identity 'Cary Grant'"
+}
+```
+
+---
+
+## 4. 補充 API (Demo 選用)
+
+### 4.1 列出身份 (List Identities)
+**路徑**: `GET /api/v1/identities`
+
+**用途**: 列出系統中所有已建立的身份。
+
+---
+
+## 5. 常見問題 (FAQ)
+
+### Q1: 為什麼 Process 回傳 400 Bad Request？
+**Ans**: 必須先執行 **Probe** (`GET /api/v1/files/:file_uuid/probe`)，確保系統已知曉檔案的幀數資訊。
+
+### Q2: 為什麼 Unregister 回傳 404？
+**Ans**: 確認伺服器是否已更新至最新版本。舊版可能尚未包含此路由。
+
+### Q3: 臉部查詢回傳空結果？
+**Ans**: 
+1. 確認檔案已**處理完成** (Progress = 100%)。
+2. 嘗試降低 `min_confidence` 參數 (例如設為 0.0)。
+3. 確認該檔案內容確實包含可辨識的臉部。
+
+---
+
+## 6. 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 |
+|------|------|------|--------|
+| V1.0 | 2026-04-30 | 初始 API 列表 | OpenCode |
+| V2.0 | 2026-05-01 | 基於 Production 測試結果補足文件 | OpenCode |
+| V3.0 | 2026-05-01 | 重構為 Demo 流程導向，補齊 Probe/Unregister 說明 | OpenCode |
+| V3.1 | 2026-05-01 | 修正 `:uuid`→`:file_uuid`，修正 port 3002→3003，移除重複 Scan 章節 | OpenCode |
@@ -0,0 +1,376 @@
+---
+document_type: "develop_guide"
+service: "MOMENTRY_CORE"
+title: "Momentry Core V1.0.0 API 示範與整合指南"
+date: "2026-05-01"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+tags:
+  - "momentry"
+  - "core"
+  - "api-usage"
+  - "demo"
+  - "n8n"
+  - "wordpress"
+ai_query_hints:
+  - "查詢 V1.0.0 API 示範與整合指南的內容"
+  - "如何使用 n8n 呼叫 V1.0.0 API？"
+  - "如何整合 V1.0.0 API 到 WordPress？"
+  - "V1.0.0 API 的 curl 範例"
+  - "PHP 整合 V1.0.0 API 的方式（wp_remote_request）"
+  - "n8n 工作流如何串接 V1.0.0 API"
+  - "Face 綁定錯誤修正的 API 操作步驟"
+  - "前端 Face Interpolation 的實作方式"
+related_documents:
+  - "API_V1.0.0/MOMENTRY_CORE_API_V1.0.0.md"
+  - "API_V1.0.0/API_DICTIONARY_V1.0.0.md"
+  - "API_V1.0.0/API_REFERENCE_v1.0.0.20260501md.md"
+  - "API_V1.0.0/CHUNK_DEFINITION_V1.0.0.md"
+  - "API_V1.0.0/PROCESSOR_SELECTION_V1.0.0.md"
+---
+
+# Momentry Core V1.0.0 API 示範與整合指南
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-01 |
+| 文件版本 | V1.0 |
+| 適用版本 | Momentry Core V1.0.0+ |
+
+---
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| file_uuid | 32 碼 SHA256 檔案識別碼 |
+| X-API-Key | API 認證方式，透過 HTTP Header 傳遞 |
+| face_id | 單一幀中的人臉偵測 ID，格式為 `<檢測ID>_<角色後綴>` |
+| Identity | 全域人物身份，跨檔案關聯同一人物 |
+| Face Interpolation | 前端線性插值，補足非逐幀臉部標記的顯示 |
+| Scan | 掃描檔案系統，列出所有檔案及當前狀態 |
+
+## 1. 快速開始 (Quick Start)
+
+### 1.1 環境 URL
+
+| 環境 | URL | 用途 |
+|------|-----|------|
+| **對外 URL** | `https://api.momentry.ddns.net` | 外部存取 |
+| **Dev Server** | `http://localhost:3003` | **開發環境，所有測試用** |
+| **Local Server** | `http://localhost:3002` | Production，僅 release 用 |
+
+### 1.2 測試連線
+
+```bash
+curl http://localhost:3003/health
+```
+
+```json
+{
+  "status": "ok",
+  "version": "1.0.0 (build: ...)",
+  "uptime_ms": 64880
+}
+```
+
+---
+
+## 2. 核心 API 工作流 (Workflows)
+
+### 2.1 掃描檔案系統 (Scan Files)
+**入口 API**: `GET /api/v1/files/scan` — 所有 Demo 流程從這裡開始。
+
+**掃描檔案**:
+```bash
+curl -s "http://localhost:3003/api/v1/files/scan" \
+  -H "X-API-Key: <your_api_key>"
+```
+
+**列出檔案 (分頁)**:
+```bash
+curl -s "http://localhost:3003/api/v1/files?page=1&page_size=10" \
+  -H "X-API-Key: <your_api_key>"
+```
+
+**取得單一檔案詳情**:
+```bash
+curl -s "http://localhost:3003/api/v1/files/<file_uuid>" \
+  -H "X-API-Key: <your_api_key>"
+```
+
+### 2.2 搜尋 (Search)
+支援語意搜尋、混合搜尋與視覺搜尋。
+
+```bash
+curl -X POST "http://localhost:3003/api/v1/search" \
+  -H "X-API-Key: <your_api_key>" \
+  -H "Content-Type: application/json" \
+  -d '{"query": "尋找紅色信封", "uuid": "<file_uuid>"}'
+```
+
+### 2.3 單獨 Face 綁定流程 (Single Face Binding Workflow)
+
+此流程適用於手動將特定臉部關聯到已知人物或建立新人物的場景。系統支援**一人分飾多角**，透過 `face_id` 加上角色後綴來區分。
+
+#### 步驟 1: 選定 Face (Input Format)
+使用者需提供一個 **`file_uuid`** 搭配 **`face_id`** 來鎖定目標。
+選定的意思是輸入 **`<file_uuid>:<face_id>`** 的組合。
+
+*   **命名規則**: `face_id` 格式通常為 `<原始檢測 ID>_<後綴>`，用於區分同一人的不同臉部實體或角色。
+    *   **有角色名稱**: 使用角色名 (如 `123_PeterJoshua`)。
+    *   **無角色名稱**: 使用通用代號 (如 `123_RoleA`, `123_RoleB`)。
+
+#### 步驟 2: 列出 Identities 或新增 Identity
+使用者決定將該 Face 綁定到系統中已存在的全域人物 (Identity)，或是建立一個新人物。
+*   **Identity 特性**: 代表現實世界中的真實人物，具備**全域唯一性** (如 "Cary Grant")。
+
+- **選項 A: 列出人物清單**
+  ```bash
+  curl -s "http://localhost:3003/api/v1/identities?page=1&page_size=20" \
+    -H "X-API-Key: <your_api_key>"
+  ```
+
+- **選項 B: 決定新增人物名稱**
+  若列表中沒有對應人物，使用者需準備一個新名稱（如 "Cary Grant"）。
+
+#### 步驟 3: 確認綁定
+透過 `POST /api/v1/identities/bind` 完成綁定。
+*   **若提供 `identity_id`**: 將帶有後綴的 `face_id` 綁定至該人物。
+*   **若提供 `name`**: 系統自動建立新人物 (Identity)，並將該臉部綁定上去。
+
+- **綁定至現有身份 (範例)**:
+  假設我們要綁定的目標是檔案 `file_uuid_abc` 中的臉部 `123_PeterJoshua`。
+  ```bash
+  curl -X POST "http://localhost:3003/api/v1/identities/bind" \
+    -H "X-API-Key: <your_api_key>" \
+    -H "Content-Type: application/json" \
+    -d '{
+      "identity_id": 101,
+      "binding_type": "face",
+      "binding_value": "123_PeterJoshua"
+    }'
+  ```
+  *註: 雖然 API 接收的是 `binding_value`，但系統內部會根據選定的 `file_uuid` 與 `face_id` 組合來精確鎖定目標。*
+
+#### 步驟 4: 循環
+完成綁定後，返回列表處理下一個未綁定的 Face。
+
+---
+
+### 2.4 取得 Face 截圖 (Retrieve Face Snapshots)
+
+在確認綁定前，通常需要檢視臉部截圖。根據使用場景，取得截圖有兩種方式：
+
+#### 1. Local Path / Filename (本地路徑)
+*   **適用**: Tauri 桌面應用、本機腳本。
+*   **說明**: 直接從硬碟讀取圖片檔案，速度最快，無需經過網路層。
+*   **路徑**: `<MOMENTRY_OUTPUT_DIR>/<file_uuid>/snapshots/faces/<face_id>.jpg`
+
+#### 2. URL (網路存取)
+*   **適用**: Web 前端、外部系統。
+*   **說明**: 透過 HTTP GET 請求取得影像串流。
+*   **API Endpoint**: `GET /api/v1/files/<file_uuid>/faces/<face_id>/thumbnail`
+*   **範例**:
+    ```bash
+    curl -s -o face.jpg \
+      "http://localhost:3003/api/v1/files/<file_uuid>/faces/<face_id>/thumbnail" \
+      -H "X-API-Key: <your_api_key>"
+    ```
+
+---
+
+### 2.4.1 前端動態辨識與插值 (Face Interpolation Logic)
+
+由於系統對臉部標記並非逐幀 (Frame-by-Frame) 進行（為節省運算資源或受限於取樣率），在 Client 端進行**逐幀播放**或**時間軸拖曳**時，若直接顯示會導致臉部框選忽閃忽滅。
+
+#### 運作邏輯
+前端需實作**線性插值 (Linear Interpolation)** 機制：
+
+1.  **取得資料**：從 API 取得該 `face_id` 在所有 `frame_number` 的座標列表（例如：Frame 10, Frame 15 有資料）。
+2.  **插值計算**：
+    *   當使用者停在 **Frame 12** 時，系統無直接資料。
+    *   前端應找出前後最近的有資料幀（Frame 10 與 Frame 15）。
+    *   根據時間差比例，動態計算出 Frame 12 的座標 `x, y, w, h`。
+
+#### 實作範例 (JavaScript/TypeScript)
+
+```typescript
+// 假設 API 回傳該 Face 的軌跡點
+const detections = [
+  { frame: 10, bbox: { x: 100, y: 100, w: 50, h: 60 } },
+  { frame: 15, bbox: { x: 110, y: 105, w: 50, h: 60 } },
+];
+
+// 計算 Frame 12 的預測框選
+function getInterpolatedBBox(frameIndex: number, detections) {
+  // 找到前一幀與後一幀
+  const prev = detections.find(d => d.frame <= frameIndex); // Frame 10
+  const next = detections.find(d => d.frame > frameIndex);  // Frame 15
+
+  if (!prev) return null; // 還沒開始出現
+  if (!next) return prev.bbox; // 結束了，維持最後位置
+
+  // 計算比例 (0.0 - 1.0)
+  const ratio = (frameIndex - prev.frame) / (next.frame - prev.frame);
+
+  return {
+    x: prev.bbox.x + (next.bbox.x - prev.bbox.x) * ratio,
+    y: prev.bbox.y + (next.bbox.y - prev.bbox.y) * ratio,
+    // w, h 亦可依此邏輯進行縮放插值
+    w: prev.bbox.w, 
+    h: prev.bbox.h, 
+  };
+}
+```
+
+---
+
+### 2.5 Face 綁定錯誤修正 (Face Binding Error Correction)
+
+此流程適用於移除錯誤綁定的臉部資料，使其恢復為未綁定狀態。
+
+1.  **選定 Face**: 確認需要解除綁定的臉部 `face_id` 以及所屬的 `file_uuid`。
+2.  **解除綁定 (Unbind)**:
+    ```bash
+    curl -X POST "http://localhost:3003/api/v1/identities/unbind" \
+      -H "X-API-Key: <your_api_key>" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "binding_type": "face",
+        "binding_value": "<selected_face_id>"
+      }'
+    ```
+
+---
+
+## 3. n8n 整合範例
+
+### 3.1 HTTP Request 設定
+
+| 欄位 | 值 |
+|---|---|
+| Method | `GET` 或 `POST` |
+| URL | `http://localhost:3003/api/v1/files` (Dev) 或 `https://<your-domain>` (Prod) |
+| Header `X-API-Key` | `<your_api_key>` |
+
+### 3.2 列出檔案 Workflow (JSON)
+使用 `GET /api/v1/files/scan` 作為入口。
+
+```json
+{
+  "nodes": [
+    {
+      "name": "Get Files",
+      "type": "n8n-nodes-base.httpRequest",
+      "parameters": {
+        "method": "GET",
+        "url": "http://localhost:3003/api/v1/files/scan",
+        "sendHeaders": true,
+        "headerParameters": {
+          "parameters": [{ "name": "X-API-Key", "value": "{{ $env.API_KEY }}" }]
+        },
+        "options": { "qs": { "page": 1, "page_size": 10 } }
+      },
+      "position": [450, 300]
+    },
+    {
+      "name": "Extract List",
+      "type": "n8n-nodes-base.code",
+      "parameters": {
+        "jsCode": "return $input.first().json.data.map(f => ({\n  json: {\n    uuid: f.file_uuid,\n    name: f.file_name,\n    status: f.status\n  }\n}));"
+      },
+      "position": [650, 300]
+    }
+  ]
+}
+```
+
+---
+
+## 4. WordPress / PHP 整合範例
+
+### 4.1 PHP Client Library (V1.0.0 相容)
+
+```php
+<?php
+class Momentry_API {
+    private const API_URL = 'http://localhost:3003'; // Dev environment
+    private const API_KEY = '<your_api_key>';
+
+    private function request(string $endpoint, array $data = [], string $method = 'GET'): array {
+        $url = self::API_URL . $endpoint;
+        $args = [
+            'headers' => [
+                'X-API-Key' => self::API_KEY,
+                'Content-Type' => 'application/json',
+            ],
+            'timeout' => 30,
+        ];
+
+        if ($method === 'POST') {
+            $args['method'] = 'POST';
+            $args['body'] = json_encode($data);
+        }
+
+        $response = wp_remote_request($url, $args);
+        if (is_wp_error($response)) {
+            throw new Exception($response->get_error_message());
+        }
+        return json_decode(wp_remote_retrieve_body($response), true);
+    }
+
+    // 掃描檔案
+    public function scan_files(): array {
+        return $this->request('/api/v1/files/scan');
+    }
+
+    // 列出檔案
+    public function list_files(): array {
+        return $this->request('/api/v1/files');
+    }
+
+    // 搜尋
+    public function search(string $query): array {
+        return $this->request('/api/v1/search', ['query' => $query], 'POST');
+    }
+}
+?>
+```
+
+---
+
+## 5. 疑難排解
+
+| 錯誤 | 原因 | 解決方案 |
+|------|------|----------|
+| `401 Unauthorized` | API Key 無效 | 檢查 Key 格式與權限 |
+| `404 Not Found` | 端點不存在 | 確認是否使用了舊版 `/api/v1/videos`，應改為 `/api/v1/files` |
+| `400 Bad Request on Process` | 缺少 Probe 資料 | 先執行 `GET /api/v1/files/:file_uuid/probe` |
+| `500 Error` | 伺服器錯誤 | 檢查資料庫連線與 Schema 版本 |
+
+---
+
+## 6. 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-01 | 初始版本 | OpenCode | deepseek-chat |
+| V1.1 | 2026-05-01 | 修正 port 為 Dev(3003)，更新 API 路徑與掃描入口 | OpenCode | deepseek-chat |
+
+---
+
+## 7. 附錄：UUID 格式說明
+
+V1.0.0 使用 **32 碼 SHA256** 作為 `file_uuid`。
+
+```
+/Users/.../demo/video.mp4 
+    ↓
+SHA256 Hash (前 32 字元)
+    ↓
+53e3a229bf68878b7a799e811e097f9c
+```
@@ -0,0 +1,148 @@
+---
+document_type: "experiment_report"
+service: "MOMENTRY_CORE"
+title: "兒童偵測與年齡估算模型選型報告"
+date: "2026-05-06"
+version: "V1.0"
+status: "completed"
+owner: "Warren"
+created_by: "OpenCode"
+---
+
+# 兒童偵測與年齡估算模型選型報告
+
+## 1. 實驗目標
+
+在 Momentry Core 的 Face Trace 資料中，尋找「非主要演員中的兒童角色」並評估三種年齡估算方案的可行性：
+1. **DeepFace AgeNet** — 深度學習年齡估算（MIT License）
+2. **Apple Vision 頭肩比** — 用頭寬/肩寬比例推測年齡（系統內建）
+3. **MiVOLO** — HuggingFace 年齡模型（Apache 2.0）
+
+## 2. 實驗環境
+
+| 項目 | 內容 |
+|------|------|
+| 測試影片 | Charade (1963), 113 min, 24fps |
+| Face detections | 6182 faces, 2347 traces |
+| Face 偵測 | Apple Vision `VNDetectFaceRectanglesRequest` (swift_face) |
+| Face 嵌入 | CoreML FaceNet512 |
+| 取樣間隔 | 60 幀 (2.5 秒) |
+| 體態偵測 | Apple Vision `VNDetectHumanBodyPoseRequest` |
+
+## 3. 實驗方法
+
+### 3.1 主要角色年齡估算
+
+從 2347 個 trace 中挑選 face_count ≥ 5 的 12 個主要 trace，提取中間幀進行 DeepFace 年齡估算 + Apple Vision 頭肩比計算。
+
+### 3.2 非主要角色搜尋
+
+搜尋小臉（< 60px）、低 face_count（≤ 2）的 trace，找出群眾演員（可能包含兒童）。
+
+### 3.3 滑雪場水槍場景
+
+Charade 開場 Megève 滑雪場有一名男孩用水槍噴灑女主角的場景。對此場景進行密集幀掃描（30 幀間隔）搜尋兒童臉。
+
+## 4. 模型選型結果
+
+### 4.1 模型可用性
+
+| 方案 | 可用 | 速度/face | License | 結論 |
+|------|------|----------|---------|------|
+| **DeepFace AgeNet** | ✓ | 0.2s（快取後） | MIT | **推薦** |
+| Apple Vision 年齡 | ✗ | — | 系統內建 | Vision 無年齡 API |
+| Apple Vision 頭肩比 | ✓ | 即時 | 系統內建 | 僅成人/兒童分類 |
+| MiVOLO | ✗ | — | Apache 2.0 | 模型不可用（HuggingFace 不存在） |
+
+### 4.2 DeepFace 年齡估算（12 主要角色取樣）
+
+| Trace | Faces | 出現時間 | 臉寬 | DeepFace 年齡 | 性別 | 情緒 |
+|-------|-------|----------|------|-------------|------|------|
+| 0 | 45 | 35s | 160px | 35 | Man | sad |
+| 24 | 6 | 708s | 100px | 34 | Man | neutral |
+| 26 | 5 | 728s | 100px | 31 | Woman | neutral |
+| 39 | 14 | 760s | 120px | 30 | Man | sad |
+| 43 | 12 | 765s | 120px | 25 | Man | sad |
+| 45 | 8 | 775s | — | 36 | Woman | neutral |
+| 46 | 9 | 795s | — | 29 | Woman | neutral |
+| 48 | 6 | 818s | 140px | 50 | Man | angry |
+| 76 | 13 | 908s | — | 29 | Man | sad |
+| 87 | 5 | 972s | — | 35 | Man | sad |
+| 103 | 7 | 1022s | — | 35 | Woman | neutral |
+| 132 | 5 | 1158s | — | 27 | Man | surprise |
+
+**年齡範圍：25–50 歲，全成人。**
+
+### 4.3 Apple Vision 頭肩比
+
+| Frame | 臉寬 | 肩寬 | 頭肩比 | DeepFace 年齡 | 場景 |
+|-------|------|------|--------|-------------|------|
+| 840 | 160px | 407px | **0.39** | 35 | 滑雪場（主角） |
+| 17460 | 100px | 354px | **0.28** | 31 | 中段場景 |
+| 18360 | 120px | 306px | **0.39** | 25 | 中段場景 |
+| 19620 | 140px | 425px | **0.33** | 50 | 最年長角色 |
+| 27780 | 110px | 381px | **0.29** | 27 | 後段場景 |
+
+**頭肩比範圍：0.28–0.39（全成人範圍）。兒童預期 > 0.6。**
+
+### 4.4 非主要演員（群眾）
+
+| Trace | Faces | 臉寬 | DeepFace 年齡 | 性別 | 頭肩比 | 場景 |
+|-------|-------|------|-------------|------|--------|------|
+| 129 | 1 | 42px | 37 | Man | 0.13 | 遠景群眾 |
+| 172 | 2 | 51px | 31 | Man | 0.22 | 遠景群眾 |
+| 304 | 2 | 47px | 41 | Man | 0.14 | 遠景群眾 |
+| 57 | 1 | 52px | 35 | Woman | — | 遠景群眾 |
+| 322 | 1 | 52px | 34 | Man | 0.18 | 遠景群眾 |
+
+**全成人。遠景群眾頭肩比更低 (0.13–0.22)，因相機距離影響 > 體型差異。**
+
+## 5. 水槍場景搜尋結果
+
+**成功找到小孩，但無法可靠估算年齡。**
+
+| 參數 | 數值 |
+|------|------|
+| 影片 | Charade (1963) |
+| 場景 | Megève 滑雪場戶外餐廳 |
+| 時間 | Frame 2450 (102 秒 / 1:42) |
+| 臉部尺寸 | **29 × 29 px** |
+| Swift Face 偵測 | ✓ 已偵測（trace_id 未分配，單幀） |
+| DeepFace 年齡 | 33 Man ❌ **誤判**（解析度不足） |
+| Apple Vision 頭肩比 | 無法計算（身體被遮擋） |
+
+### 誤判原因
+
+29×29px 遠低於年齡估算模型的最低解析度需求（一般需 ≥ 50×50px）。在遠景中，兒童的臉太小，神經網路無法提取足夠的年齡特徵，導致：
+- DeepFace 將兒童誤判為成人
+- 頭肩比受距離影響大於實際年齡
+
+## 6. 結論與建議
+
+| 發現 | 說明 |
+|------|------|
+| Charade 無兒童主要角色 | 全卡司成人，DeepFace 年齡範圍 25–50 |
+| 水槍小孩已找到 | Frame 2450，102 秒，但 29px 太小無法估齡 |
+| DeepFace 可行 | MIT license，0.2s/face，適合 ≥ 50px 臉部 |
+| Apple Vision 頭肩比 | 僅適合作近景成人/兒童分類（非精確年齡） |
+| MiVOLO | 不可用（HuggingFace 模型不存在） |
+
+### 建議
+
+1. **整合 DeepFace** 年齡估算入 `face_processor.py` pipeline，對 ≥ 50px 的臉進行年齡標記
+2. **保留頭肩比** 做為輔助驗證（成人/兒童二元分類）
+3. **降低取樣間隔** 從 60 幀降至 10–15 幀以捕捉更多短暫出現的角色
+4. **若需測試兒童年齡**：使用片庫中的 `Alice Comedies (1926)`，該片有近景小女孩（Virginia Davis，6–8 歲），臉部可達 150px+
+
+---
+
+## 附錄：測試資料
+
+| 檔案 | 路徑 |
+|------|------|
+| DeepFace 年齡 JSON | `output_dev/experiments/age_benchmark/age_benchmark_report.json` |
+| 頭肩比 JSON | `output_dev/experiments/head_shoulder/head_shoulder_report.json` |
+| 水槍場景幀 | `output_dev/experiments/head_shoulder/child_f2450.jpg` |
+| 年齡基準腳本 | `scripts/age_benchmark.py` |
+| 頭肩比腳本 | `scripts/head_shoulder_quick.py` |
+| Face trace 排序 API | `POST /api/v1/file/:file_uuid/face_trace/sortby` |
@@ -0,0 +1,298 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "Story Parent-Child Chunk Rules V1.0"
+date: "2026-05-05"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+tags:
+  - "momentry"
+  - "core"
+  - "chunk"
+  - "story"
+  - "parent-child"
+  - "v1.0"
+ai_query_hints:
+  - "Story parent-child chunk generation rules"
+  - "CUT scene → parent chunk, ASR sentence → child chunk"
+  - "boundary overlap: partial match enriches child context"
+  - "parent_summary template + child_summary template"
+  - "children per parent distribution"
+related_documents:
+  - "../CHUNK_DEFINITION_V1.0.0.md"
+  - "../DUAL_EMBEDDING_PIPELINE_V1.0.0.md"
+  - "../PROCESSORS/ASR_V1.0.0.md"
+  - "../PROCESSORS/CUT_V1.0.0.md"
+---
+
+# Story Parent-Child Chunk Rules V1.0
+
+## 核心概念
+
+- **Parent chunk** = CUT 場景邊界內的所有對話 → 一個場景敘述
+- **Child chunk** = 單一 ASR sentence → 一句對白
+- **Boundary overlap** = 場景邊界重疊的句子 → 同時歸屬前後 parent
+
+## 匹配規則
+
+### Rule 1: Fully-Contained Matching
+
+```
+ASR sentence 完全在 CUT 場景時間範圍內
+  → seg.start >= scene.start_time AND seg.end <= scene.end_time
+  → 加入該 scene 的 children 列表
+```
+
+### Rule 2: Boundary Overlap (所有 parent)
+
+```
+對於每個 parent chunk（即使只有 1 child）:
+  → 找出與 scene 時間範圍有 partial overlap 的 ASR sentence
+  → seg.start < scene.end_time AND seg.end > scene.start_time
+  → AND 未被 Rule 1 匹配（不是 fully-contained）
+  → 加入該 scene 的 children 列表
+```
+
+邊界 overlap 讓 child chunk 可以同時歸屬前後兩個 parent，提供更多上下文。
+
+### Rule 3: Scene Filter
+
+```
+CUT scene duration < 1s → 跳過（場景太短無意義）
+```
+
+## Parent Summary 模板
+
+```
+[{start}s-{end}s, {duration}s]
+Cast: {character_list}
+Total dialogue: N lines, W words
+Speakers: {name} (N lines): "sample text..."
+```
+
+## Child Summary 模板
+
+```
+[{start}s-{end}s] {speaker_name}: "{asr_text}"
+```
+
+### Embedding Target
+
+Child summary text → Ollama nomic-embed-text-v2-moe → 768D vector → pgvector
+
+## 數據實例：Charade (1963) — 長片 113min
+
+### 輸入
+
+| 來源 | 數量 | 說明 |
+|------|------|------|
+| ASR segments | **1,629** | Whisper small 英文字幕 |
+| ASR with text | 1,629 | 全部有文字 |
+| ASR total duration | 6,760s (113 min) | |
+| CUT scenes | **1,331** | PySceneDetect 場景切割 |
+| CUT scenes ≥ 1s | 1,200 | 過濾後有效場景 |
+| CUT mean duration | 5.2s | 平均場景長度 |
+| CUT scene gap (unmatched) | 131 | < 1s 場景被過濾 |
+
+### 輸出 (V2.1 — boundary overlap for ALL scenes, duration filter removed)
+
+| 指標 | 數值 |
+|------|------|
+| **Parent chunks** | **1,313** (all CUT scenes ≥ 0s) |
+| **Child chunks** (total in DB) | **2,927** (1,629 unique + 1,298 overlaps) |
+| **Unique children** | **1,629** (100% ASR coverage) |
+| DB duplicates (shared) | 1,298 (ON CONFLICT merge) |
+| Children per parent | 1 ~ 43, avg **2.2** |
+| Unmatched | **0** |
+
+### 分佈
+
+```
+Children per parent:
+    1:  128 parents  (獨白/短場景)
+    2:   58 parents
+    3:    0 parents  ← 邊界 overlap 後 3 被 2/4 吸收
+    4-9:  64 parents  (中等對話場景)
+   10-27: 50 parents  (多人對話場景)
+```
+
+### 已匹配率
+
+| 指標 | 數值 |
+|------|------|
+| ASR unmatched | **0** (V2.1: boundary overlap for ALL scenes) |
+| 已匹配率 | **100%** |
+
+## 輸入/輸出範例
+
+### Big Parent（多子女）
+
+**輸入原始數據**：
+```
+CUT scene [2783s-2847s, 65s]
+27 ASR sentences, all spoken by Audrey Hepburn + Cary Grant + SPEAKER_2
+```
+
+**輸出 Parent Summary**：
+```
+[2783s-2847s, 65s] Cast: Audrey Hepburn, Cary Grant, SPEAKER_2.
+Total dialogue: 27 lines, 143 words.
+```
+
+**輸出 Child Summaries**（embedding target）：
+```
+[2784s-2786s] Audrey Hepburn: "they stole it"
+[2786s-2788s] Audrey Hepburn: "by burying it"
+[2788s-2790s] Audrey Hepburn: "then reporting the Germans had captured it"
+... (27 total)
+```
+
+**Metadata 信度**（隨 parent/child 傳遞）：
+
+```json
+// Parent metadata
+{
+  "speaker_confidence": { "Audrey Hepburn": 0.85, "Cary Grant": 0.64 },
+  "face_confidence": { "Audrey Hepburn": 0.60, "Cary Grant": 0.64 },
+  "yolo_objects": { "car": 0.72, "bottle": 0.55, "chair": 0.68 }
+}
+
+// Child metadata
+{
+  "speaker_name": "Audrey Hepburn",
+  "speaker_confidence": 0.85,   // MAR lip: 57% events during SPEAKER_1
+  "face_confidence": 0.60,       // clustering composite
+  "asr_confidence": 0.92         // Whisper confidence
+}
+```
+
+### 1:1 Parent（單子女）
+
+**輸入原始數據**：
+```
+CUT scene [304s-318s, 14s]
+1 ASR sentence, spoken by Cary Grant alone
+```
+
+**輸出 Parent Summary**：
+```
+[304s-318s, 14s] Cast: Cary Grant.
+Total dialogue: 1 lines, 13 words.
+```
+
+**輸出 Child Summary**（embedding target）：
+```
+[309s-317s] Cary Grant: "Sylvia I'm getting a divorce what from Charles he's the only husband I"
+```
+
+## 與 LLM Pipeline 的關係
+
+```
+Pipeline 1 (Story):  template summary → DB + embedding
+Pipeline 2 (LLM):    LLM summary → DB + embedding (future)
+
+chunk_type:
+  story_parent / story_child   ← Pipeline 1
+  llm_parent   / llm_child     ← Pipeline 2 (future)
+```
+
+## 版本歷史
+
+| 版本 | 日期 | 變更 |
+|------|------|------|
+| V1.0 | 2026-05-05 | 初始規則：fully-contained + boundary overlap |
+| V2.1 | 2026-05-05 | 移除 duration filter，boundary overlap 對所有場景（含空場景）。100% ASR coverage。Speaker mapping 從 DB 動態讀取。 |
+
+## Charade 1963 統計分析記錄
+
+### 影片資料
+
+| 指標 | 值 |
+|------|-----|
+| 片長 | 113 分鐘 |
+| 總幀數 | 412,343 |
+| FPS | 59.94 |
+| 解析度 | 1920×1080 |
+
+### 處理器產出
+
+| Processor | 輸出行數 | 說明 |
+|-----------|---------|------|
+| CUT | 1,331 scenes | 平均 5.2s/scene，min 0.2s，max 64.5s |
+| ASR | 1,629 segments | Whisper small，113 min total |
+| ASRX | 10 speakers | SPEAKER_0/1 為主要角色 |
+| Face | 4,008 frames, 6,182 faces | sample=60, Vision+CoreML ANE |
+| Face Trace | 6,182 detections, 2,347 traces | IoU+embedding tracking |
+| Identity | 677 traces → 7 identities | 99.4% coverage, MAR lip speaker binding |
+| YOLO | 328,800 frames, 57 object classes | CoreML ANE |
+
+### Matching 迭代記錄
+
+#### Iteration 1: Fully-contained only, >= 1s scene filter
+
+```
+Rule: seg.start >= scene.start AND seg.end <= scene.end
+Scene filter: duration >= 1s (131 scenes filtered out)
+
+Result: 990/1629 (61%) matched
+       454 unmatched, 74 in filtered scenes
+       Only scenes with children got boundary overlaps
+```
+
+#### Iteration 2: Add boundary overlap for scenes with >= 3 children
+
+```
+Rule: For scenes with >= 3 children, add partial overlaps
+
+Result: 1,210 children (+220 partial)
+       Still 454 unmatched (boundary overlap only for rich scenes)
+```
+
+#### Iteration 3: Remove duration filter
+
+```
+Rule: Remove >=1s scene filter
+
+Result: 1,496 unique children (92% coverage)
+       133 unmatched
+       Root cause: boundary overlap still gated by "if children:"
+```
+
+#### Iteration 4: Boundary overlap for ALL scenes (regardless of children)
+
+```
+Rule: Move boundary overlap code outside "if children:" guard
+       All 1,331 scenes participate
+
+Result: 1,629 unique children (100% coverage)
+       1,313 parents (all scenes)
+       2,927 total children (1,629 unique + 1,298 overlaps)
+```
+
+### 關鍵決策
+
+| 決策 | 理由 | 影響 |
+|------|------|------|
+| 移除 duration filter | 131 scenes <1s 會漏掉句子 | +24 parents, +321 children |
+| 移除 children guard | 空場景也要加 boundary children | +133 children (100%) |
+| 用 overlap 而非 fully-contained | ASR/CUT 時間邊界不對齊 | 避免 565 sentences orphan |
+| Partial overlaps 存兩次 | 邊界句可歸屬兩個 parent | 1,298 duplicates via ON CONFLICT |
+| Speaker map 從 DB 讀 | 不再 hardcode 演員名 | 通用化任何影片 |
+
+### 效能指標
+
+| 指標 | 值 |
+|------|-----|
+| Story 生成時間 | < 1s (template, instant) |
+| Embedding 時間 (Ollama) | ~2 min for 1,629 chunks |
+| Qdrant sync 時間 | ~3 min for rule1, ~1 min for story |
+| BM25 search 時間 | < 10ms per query |
+
+### 教學要點
+
+1. **時間邊界不對齊是常態**：ASR（語音邊界）與 CUT（視覺邊界）用不同演算法，永遠不會完美對齊。overlap matching 是必要設計。
+2. **Boundary overlap 需對所有場景生效**：不能只限有 children 的場景，否則會產生 orphan sentences。
+3. **ON CONFLICT merge**：同一 sentence 出現在兩個 parent 時，DB 層面用最後一個 parent。如需多對多關係，需 junction table。
+4. **Hardcoded 到 Dynamic**：speaker map 從 hardcode → DB-driven 是通用化的關鍵一步。
@@ -0,0 +1,192 @@
+---
+document_type: "design"
+service: "MOMENTRY_CORE"
+title: "Class 分類系統設計 V1.0"
+date: "2026-05-05"
+version: "V1.0"
+status: "design"
+owner: "Warren"
+created_by: "OpenCode"
+tags:
+  - "momentry"
+  - "core"
+  - "class"
+  - "taxonomy"
+  - "design"
+  - "v1.0"
+ai_query_hints:
+  - "Class 分層分類系統設計"
+  - "參照 IPC (國際專利分類) 及 HS (海關稅則)"
+  - "編碼格式: {section}-{NNNN}"
+  - "用於 identity 多層分類、快速定位"
+related_documents:
+  - "../DATA_SCHEMA_FILE_IDENTITY_V1.0.0.md"
+  - "../UUID_ENCODING_RULES_V1.0.0.md"
+---
+
+# Class 分類系統設計 V1.0
+
+> 狀態：設計階段，尚未實施
+
+## 設計參考
+
+IPC（國際專利分類）與 HS（海關稅則）。
+
+共通原則：**層級碼**、**數字越長越精細**、**全球通用**、**可無限擴展**。
+
+## 設計目標
+
+- IPC/HS 式的 hierarchical code → **快速定位**
+- Tag 式的 multi-label 使用 → **靈活分類**
+- 同一 entity 可擁有多條 class path
+- 新增分類只需 INSERT，無 migration
+
+```
+Cary Grant
+  → P-0201 (演員/主角)
+  → T-0102 (1960s)
+  → S-0200 (場景/戶外 — 他在片中出現的場景)
+
+Ferrari 250 GT
+  → O-0101 (汽車)
+  → B-0300 (汽車品牌/Ferrari)
+  → T-0102 (1960s)
+
+## 編碼格式
+
+```
+{section}-{NNNN}
+   │      └── 4 digits，每 2 digits 一層
+   └───────── 1 char section prefix
+```
+
+| 層級 | 範例 | 意義 |
+|------|------|------|
+| `P-0000` | top section | 人物 |
+| `P-0200` | subclass | 人物 → 演員 |
+| `P-0201` | group | 人物 → 演員 → 主角 |
+| `P-0202` | group | 人物 → 演員 → 配角 |
+
+層級判斷：`code.length`。`P-` = section，`P-02` = subclass，`P-0201` = group。
+
+### Section 定義
+
+| Section | 名稱 | 範疇 | 預留 |
+|---------|------|------|------|
+| `P` | 人物 | 演員、導演、公眾人物、虛構角色、運動員... | 01-99 |
+| `O` | 物件 | 交通工具、家具、武器、工具、電子產品... | 01-99 |
+| `B` | 品牌/組織 | 時尚、科技、汽車品牌、政府機構、NGO... | 01-99 |
+| `C` | 概念/抽象 | 情感、思想、事件、主題、風格... | 01-99 |
+| `A` | 生物 | 動物、植物、真菌... | 01-99 |
+| `S` | 場景/地點 | 室內、戶外、城市、自然地標、建築內部... | 01-99 |
+| `E` | 環境/自然 | 天氣、地形、天象、自然災害... | 01-99 |
+| `M` | 音樂/聲音 | 樂器、音樂類型、自然聲音、人工聲音... | 01-99 |
+| `L` | 語言/文字 | 語言、方言、書寫系統、符號... | 01-99 |
+| `T` | 時間/時期 | 年代、季節、節日、歷史時期... | 01-99 |
+| `F` | 檔案類型 | 影片格式、文件類型、圖片格式... | 01-99 |
+| `D` | 領域/學科 | 科學、藝術、體育、政治、經濟... | 01-99 |
+
+12 個 Section，各 99 subclass × 99 group = ~117K 分類槽位。可隨時新增 Section。
+
+## 初始 Class Tree
+
+```
+P-0000  人物
+├── P-0100  公眾人物
+├── P-0200  演員
+│   ├── P-0201  主角
+│   └── P-0202  配角
+├── P-0300  導演
+├── P-0400  虛構角色
+└── P-9900  其他人物
+
+O-0000  物件
+├── O-0100  交通工具
+│   ├── O-0101  汽車
+│   ├── O-0102  船
+│   └── O-0103  飛機
+├── O-0200  建築
+├── O-0300  家具
+└── O-9900  其他物件
+
+B-0000  品牌
+├── B-0100  時尚
+├── B-0200  科技
+└── B-9900  其他品牌
+
+C-0000  概念
+├── C-0100  情感
+├── C-0200  思想
+└── C-9900  其他概念
+```
+
+## Table
+
+```sql
+CREATE TABLE classes (
+    code        VARCHAR(8) PRIMARY KEY,     -- P-0201
+    name        TEXT NOT NULL,               -- 主角
+    description TEXT,
+    created_at  TIMESTAMPTZ DEFAULT now()
+);
+
+-- 多對多：同一 identity 可有多個 class code（如 tag 使用）
+CREATE TABLE identity_classes (
+    identity_id INTEGER REFERENCES identities(id),
+    class_code  VARCHAR(8) REFERENCES classes(code),
+    confidence  REAL DEFAULT 1.0,
+    source      VARCHAR(20),                 -- which agent classified
+    PRIMARY KEY (identity_id, class_code)
+);
+```
+
+## Query 範例
+
+```sql
+-- 查某 identity 的所有 class
+SELECT c.code, c.name
+FROM identity_classes ic
+JOIN classes c ON ic.class_code = c.code
+WHERE ic.identity_id = 8;
+
+-- 查所有屬於 "演員" (P-0200) 的 identity
+SELECT i.name
+FROM identity_classes ic
+JOIN identities i ON ic.identity_id = i.id
+WHERE ic.class_code LIKE 'P-02%';
+
+-- 查某 section 下的所有 identity
+SELECT DISTINCT i.name
+FROM identity_classes ic
+JOIN identities i ON ic.identity_id = i.id
+WHERE ic.class_code LIKE 'P-%';
+```
+
+## 擴展方式
+
+1. 新增 leaf class：`INSERT INTO classes VALUES ('P-0203', '配音員')` — P-02 底下的新 group
+2. 新增 subclass：`INSERT INTO classes VALUES ('P-0500', '製作團隊')` — P 底下的新 subclass
+3. 新增 section：`INSERT INTO classes VALUES ('X-0000', '新分類')` — 全新 top-level
+
+無需 migration，insert 即可。
+
+## 版本歷史
+
+| 版本 | 日期 | 狀態 |
+|------|------|------|
+| V1.0 | 2026-05-05 | 設計階段 |
+
+## Future: Class-Based Search
+
+實施 class 系統後，search API 可加入 class filter 提升命中率：
+
+```
+GET /api/v1/search?q=car&class=O-0101
+  → 只搜被分類為「汽車」的內容，過濾 "care", "car accident", "car wash"
+
+GET /api/v1/search/hybrid?q=divorce&class=P-0200
+  → 只搜演員說出的 "divorce"，排除旁白、字幕
+
+GET /api/v1/search/universal?class=T-0102
+  → 搜所有 1960s 相關內容
+```
@@ -0,0 +1,328 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "Data Schema: File & Identity V1.0"
+date: "2026-05-05"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+tags:
+  - "momentry"
+  - "core"
+  - "schema"
+  - "file"
+  - "identity"
+  - "v1.0"
+ai_query_hints:
+  - "File & Identity DB schema"
+  - "face_detections.identity_id direct FK"
+  - "identity multi-modal: face + voice + TMDb + manual"
+related_documents:
+  - "../DUAL_EMBEDDING_PIPELINE_V1.0.0.md"
+  - "../UUID_ENCODING_RULES_V1.0.0.md"
+---
+
+# Data Schema: File & Identity V1.0
+
+## 1. File Schema
+
+### videos / files
+
+| Column | Type | 說明 |
+|--------|------|------|
+| `id` | SERIAL PK | |
+| `file_uuid` | VARCHAR(32) | Birth UUID |
+| `file_path` | VARCHAR(512) | 檔案完整路徑 |
+| `file_name` | VARCHAR(256) | |
+| `probe_json` | JSONB | ffprobe raw output |
+| `status` | VARCHAR(20) | ready / processing / completed |
+| `processing_status` | JSONB | per-processor progress |
+| `total_frames` | INTEGER | |
+| `fps` | DOUBLE | |
+| `duration` | DOUBLE | 影片長度（秒） |
+| `width` / `height` | INTEGER | 解析度 |
+| `registration_time` | TIMESTAMP | 註冊時間 |
+
+### face_detections (per-file face data)
+
+| Column | Type | 說明 |
+|--------|------|------|
+| `id` | SERIAL PK | |
+| `file_uuid` | VARCHAR(32) | → videos.file_uuid |
+| `frame_number` | BIGINT | 幀號 |
+| `face_id` | VARCHAR(64) | per-file face identifier |
+| `trace_id` | INTEGER | 跨幀追蹤 ID |
+| `x, y, width, height` | INTEGER | bbox |
+| `confidence` | REAL | 偵測信度 |
+| `embedding` | REAL[] | 512D CoreML FaceNet |
+| `identity_id` | INTEGER | → identities.id (V4.0 direct FK) |
+
+### chunks (per-file parent/child chunks)
+
+| Column | Type | 說明 |
+|--------|------|------|
+| `id` | SERIAL PK | |
+| `chunk_id` / `old_chunk_id` | VARCHAR | chunk identifier |
+| `file_uuid` | VARCHAR(32) | → videos.file_uuid |
+| `chunk_type` | VARCHAR(32) | story_parent / story_child / rule1_sentence |
+| `chunk_index` | INTEGER | per-file ordering |
+| `start_time` / `end_time` | DOUBLE | time range |
+| `content` | JSONB | metadata |
+| `text_content` | TEXT | summary text → embedding target |
+| `embedding` | VECTOR | pgvector 768D |
+| `search_vector` | TSVECTOR | BM25 full-text |
+| `parent_chunk_id` | VARCHAR | → chunks.chunk_id |
+
+## 2. Identity Schema
+
+### 概念
+
+Identity 是可命名的任何識別標的，不限於人。
+
+| identity_type | 範例 | 識別模型 |
+|--------------|------|---------|
+| `people` | Cary Grant, Audrey Hepburn | face, voice, name |
+| `animal` | 電影中的狗、馬 | face, body, sound |
+| `object` | 特定道具、車輛 | yolo, image embedding |
+| `plant` | 場景中的特定植物 | image embedding |
+| `building` | 艾菲爾鐵塔、特定建築 | image embedding, OCR |
+| `place` | Paris, 咖啡廳 | scene classification |
+| `concept` | "離婚", "復仇" | text embedding |
+| `brand` | Coca-Cola | OCR, logo detection |
+
+每種 identity_type 可以使用不同的識別模型組合。
+
+### 識別模型
+
+| model | dimension | source | 適用 identity_type |
+|-------|-----------|--------|-------------------|
+| `face` | 512D | CoreML FaceNet | people, animal |
+| `voice` | 192D | SpeechBrain ECAPA-TDNN | people |
+| `text` | 768D | Ollama nomic-embed | concept, place |
+| `image` | 768D | — (future) | object, building, plant |
+| `yolo_class` | — | YOLO label | object |
+
+### Table
+
+```sql
+CREATE TABLE identities (
+    id              SERIAL PRIMARY KEY,
+    uuid            UUID,                  -- 32-char UUIDv5 (source:external_id)
+    name            TEXT NOT NULL UNIQUE,
+    identity_type   VARCHAR(30) DEFAULT 'people',  -- people/animal/object/building/place/concept
+    source          VARCHAR(20) DEFAULT 'manual',  -- tmdb/manual/face_cluster/yolo
+    status          VARCHAR(20) DEFAULT 'pending',
+
+    -- Reference vectors per model (in JSONB for extensibility)
+    reference_vectors JSONB DEFAULT '{}',
+    -- {
+    --   "face": [{"vec":[...], "pose":"frontal", "source":"video_trace"}],
+    --   "voice": [{"vec":[...], "speaker_id":"SPEAKER_0"}],
+    --   "image": [{"vec":[...], "source":"manual"}]
+    -- }
+
+    -- Legacy columns (migrating to reference_vectors)
+    face_embedding      VECTOR(512),
+    voice_embedding     VECTOR(192),
+    identity_embedding  VECTOR(768),
+
+    reference_data  JSONB DEFAULT '{}',
+    metadata        JSONB DEFAULT '{}',
+    tmdb_id         INTEGER,
+    tmdb_profile    TEXT,
+    created_at      TIMESTAMP DEFAULT now()
+);
+```
+
+### 彈性設計
+
+現有 `face_embedding` / `voice_embedding` column 維持向下相容。  
+未來全部移入 `reference_vectors` JSONB，支援任意 model × 多個 reference vectors：
+
+```json
+{
+  "reference_vectors": {
+    "face": [
+      {"vec": [0.1, 0.2, ...], "pose": "frontal", "source": "video_trace_0", "confidence": 0.95},
+      {"vec": [0.3, 0.4, ...], "pose": "profile", "source": "video_trace_0", "confidence": 0.88}
+    ],
+    "voice": [
+      {"vec": [0.5, 0.6, ...], "speaker_id": "SPEAKER_0", "source": "asrx"}
+    ],
+    "image": []
+  }
+}
+```
+
+### 識別 Agent 架構
+
+每個識別模型由對應的 Agent 負責。Identity 本身只存 reference vectors，不綁定特定 model。
+
+```
+                    ┌─────────────────────────┐
+                    │      identities          │
+                    │  name, type, source      │
+                    │  reference_vectors (JSONB)│
+                    └──────────┬──────────────┘
+                               │
+          ┌────────────────────┼────────────────────┐
+          │                    │                    │
+     ┌────▼────┐         ┌────▼────┐         ┌────▼────┐
+     │FaceAgent│         │VoiceAgent│        │ImageAgent│
+     │         │         │         │         │ (future) │
+     │ input:  │         │ input:  │         │ input:  │
+     │ face_   │         │ asrx    │         │ image   │
+     │ detect  │         │ segments│         │ features│
+     │ ions    │         │         │         │         │
+     │         │         │         │         │         │
+     │ output: │         │ output: │         │ output: │
+     │ face →  │         │ voice → │         │ img →   │
+     │ identity│         │ identity│         │ identity│
+     └─────────┘         └─────────┘         └─────────┘
+```
+
+### Agent 定義
+
+| Agent | 輸入 | 模型 | 輸出 | 狀態 |
+|-------|------|------|------|------|
+| **FaceAgent** | `face_detections` | CoreML FaceNet 512D | `identity_id` on face_detections | ✅ |
+| **VoiceAgent** | ASRX segments | ECAPA-TDNN 192D + MAR lip | `metadata.speaker_id` | ✅ |
+| **ImageAgent** | — | — | — | ⬜ future |
+| **YoloAgent** | YOLO detections | — | object → identity | ⬜ future |
+| **TextAgent** | chunk text | nomic-embed 768D | concept → identity | ⬜ future |
+
+### Agent 運作模式
+
+```
+1. Agent 讀取 raw detections（face / voice / yolo）
+2. 對比 identities.reference_vectors[model]
+3. 相似度達標 → bind to existing identity
+4. 不達標 → create new identity
+5. 更新 identities.reference_vectors（enrich reference set）
+```
+
+同一個 identity 可以被多個 Agent 同時更新。例如：
+- FaceAgent 寫入 `reference_vectors.face`
+- VoiceAgent 寫入 `reference_vectors.voice`
+- 兩者指向同一個 identity (Cary Grant)
+
+### Face → Identity 綁定（V4.0）
+
+```
+face_detections.identity_id ──── FK ────→ identities.id
+```
+
+Direct FK。不需要 intermediate table。操作 API：
+
+```
+POST /api/v1/identities/bind
+  { "file_uuid": "...", "face_id": "face_1", "identity_uuid": "..." }
+  → UPDATE face_detections SET identity_id = X
+
+POST /api/v1/identities/unbind
+  { "file_uuid": "...", "face_id": "face_1" }
+  → UPDATE face_detections SET identity_id = NULL
+```
+
+### Voice/Speaker → Identity 綁定
+
+透過 `identities.metadata.speaker_id`：
+
+```
+identities.metadata = {"speaker_id": "SPEAKER_0", "speaker_confidence": 0.85}
+```
+
+Voice embedding 直接寫入 `identities.voice_embedding`。
+
+## 3. File-Identity 關聯
+
+```
+file (1a04db97...)                    identity (Cary Grant)
+│                                     │
+├── face_detections                   │
+│   ├── face_id="face_1"              │
+│   │   identity_id ──────────────────┤
+│   ├── face_id="face_2"              │
+│   │   identity_id ──────────────────┤
+│   └── face_id="face_3"              │
+│       identity_id = NULL            │  ← unbounded
+│                                     │
+├── chunks                            │
+│   ├── story_parent                  │
+│   │   content.metadata.characters   │
+│   │   = ["Cary Grant", ...]         │
+│   └── story_child                   │
+│       content.metadata.speaker      │
+│       = "Cary Grant"                │
+│                                     │
+└── asrx.json                          │
+    └── segments[].speaker_id          │
+        = "SPEAKER_0" ────────────────┘
+
+file_identities (N:N junction, if needed)
+  file_uuid → identity_uuid
+```
+
+## 4. Class 分層分類（參照 IPC + HS）
+
+### 設計參考
+
+IPC（國際專利分類）與 HS（海關稅則）的分層編碼體系。
+
+| 標準 | 結構 |
+|------|------|
+| **IPC** | Section(A-H) → Class(2digits) → Subclass → Group/NNN |
+| **HS** | Section → Chapter(2digits) → Heading(4digits) → Subheading(6digits) |
+
+共通原則：**層級碼**、**數字越長越精細**、**全球通用**。
+
+### 編碼格式
+
+```
+{SECTION}-{NNN}-{NNN}-{NNN}
+   │       │      │      └─ subgroup
+   │       │      └──────── main_group
+   │       └─────────────── subclass
+   └─────────────────────── section
+```
+
+| Section | 涵蓋 |
+|---------|------|
+| `P` | People |
+| `O` | Object |
+| `B` | Brand |
+| `C` | Concept |
+| `A` | Animal |
+| `S` | Scene |
+| `E` | Environment |
+| `M` | Music/Sound |
+
+### Table
+
+```sql
+CREATE TABLE classes (
+    code            VARCHAR(20) PRIMARY KEY,     -- P-001-010/010
+    name            TEXT NOT NULL,
+    parent_code     VARCHAR(20) REFERENCES classes(code),
+    section         CHAR(1),
+    level           INTEGER DEFAULT 0,
+    description     TEXT,
+    created_at      TIMESTAMPTZ DEFAULT now()
+);
+
+CREATE TABLE identity_classes (
+    identity_id     INTEGER REFERENCES identities(id),
+    class_code      VARCHAR(20) REFERENCES classes(code),
+    confidence      REAL DEFAULT 1.0,
+    source          VARCHAR(20),
+    PRIMARY KEY (identity_id, class_code)
+);
+```
+
+## 版本歷史
+
+| 版本 | 日期 | 變更 |
+|------|------|------|
+| V1.0 | 2026-05-05 | File & Identity schema，V4.0 direct FK binding |
+| V1.1 | 2026-05-05 | Class 分層分類（IPC/HS），Agent 識別架構 |
@@ -0,0 +1,216 @@
+---
+document_type: "reference_doc"
+service: "MOMENTRY_CORE"
+title: "Momentry Core Dev API 參考文件"
+date: "2026-05-06"
+version: "V1.1"
+status: "deprecated"
+owner: "Warren"
+---
+
+> ⚠️ **此文件為 V3.x 歷史參考，含已移除的路由。**
+> 請改用 `API_DICTIONARY_V1.0.0.md`（root）取得當前準確的 53 條 API 路由。
+created_by: "OpenCode"
+tags:
+  - "api"
+  - "reference"
+  - "dev"
+  - "v1.1"
+  - "restful"
+related_documents:
+  - "MOMENTRY_CORE_API_V1.0.0.md"
+  - "RELEASE/RELEASE_API_REFERENCE_v1.0.0.md"
+---
+
+# Momentry Core Dev API 參考文件
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-06 |
+| 文件版本 | V1.1 |
+| Base URL | `http://localhost:3003` |
+| 認證方式 | Header `X-API-Key`（部分端點需要） |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 |
+|------|------|------|--------|
+| V1.1 | 2026-05-06 | 從程式碼實際路由重新產生 53 端點清單 | OpenCode |
+| V1.0 | 2026-04-30 | 原始文件，含多個不存在之端點 | OpenCode |
+
+---
+
+## 認證
+
+- **Header**: `X-API-Key: <your_api_key>`
+- 目前 `/api/v1/auth/login` 回傳固定 demo Key: `muser_test_001`
+- Protected routes 透過 `api_key_validation` middleware 驗證
+- Public routes（免 Key）: `/health`, `/health/detailed`, `/api/v1/auth/login`
+
+---
+
+## 端點列表
+
+總計 **53 個註冊路由**（另有 1 個定義但未掛載）。
+
+### 1. 系統與認證（System & Auth）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 1 | GET | `/health` | 基本健康檢查（回傳 status/version/uptime） | ❌ |
+| 2 | GET | `/health/detailed` | 詳細健康狀態（含 PG/Redis/Qdrant/MongoDB 各別延遲） | ❌ |
+| 3 | POST | `/api/v1/auth/login` | 登入（固定 demo/demo，回傳 API Key） | ❌ |
+| 4 | POST | `/api/v1/auth/logout` | 登出 | ✅ |
+
+### 2. 檔案管理（File Management）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 5 | GET | `/api/v1/files` | 檔案列表（支援分頁、status、q、uuid 過濾） | ✅ |
+| 6 | GET | `/api/v1/file/:file_uuid` | 檔案詳細資訊（含 probe_json、metadata） | ✅ |
+| 7 | POST | `/api/v1/files/register` | 從磁碟註冊新檔案（支援 pattern 批次註冊） | ✅ |
+| 8 | POST | `/api/v1/unregister` | 取消註冊檔案 | ✅ |
+| 9 | GET | `/api/v1/files/scan` | 掃描 SFTPGo demo 目錄中的新檔案 | ✅ |
+| 10 | GET | `/api/v1/file/:file_uuid/probe` | 取得/快取 ffprobe 資訊 | ✅ |
+| 11 | POST | `/api/v1/file/:file_uuid/process` | 啟動處理 pipeline（建立 monitor job） | ✅ |
+| 12 | GET | `/api/v1/file/:file_uuid/chunks` | 列出 pre_chunks | ✅ |
+| 13 | GET | `/api/v1/progress/:uuid` | 即時處理進度（來自 Redis PubSub） | ✅ |
+| 14 | GET | `/api/v1/jobs` | 任務列表（支援分頁、status 過濾） | ✅ |
+
+### 3. 搜尋（Search）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 15 | POST | `/api/v1/search/visual` | 視覺搜尋 | ✅ |
+| 16 | POST | `/api/v1/search/visual/class` | 依物件類別過濾搜尋 | ✅ |
+| 17 | POST | `/api/v1/search/visual/density` | 依視覺密度搜尋 | ✅ |
+| 18 | POST | `/api/v1/search/visual/stats` | 視覺統計資料 | ✅ |
+| 19 | POST | `/api/v1/search/visual/combination` | 視覺組合搜尋（多條件） | ✅ |
+| 20 | POST | `/api/v1/search/smart` | 智慧搜尋（語意向量） | ✅ |
+| 21 | POST | `/api/v1/search/universal` | 通用搜尋 | ✅ |
+| 22 | POST | `/api/v1/search/frames` | 影格搜尋 | ✅ |
+
+### 4. 身份管理（Identity）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 23 | GET | `/api/v1/identities` | 身份列表 | ✅ |
+| 24 | POST | `/api/v1/identity` | 建立身份（從 face.json 建立參考向量） | ✅ |
+| 25 | GET | `/api/v1/identity/:identity_uuid` | 身份詳細資訊 | ✅ |
+| 26 | DELETE | `/api/v1/identity/:identity_uuid` | 刪除身份 | ✅ |
+| 27 | GET | `/api/v1/identity/:identity_uuid/files` | 該身份出現的所有檔案 | ✅ |
+| 28 | GET | `/api/v1/identity/:identity_uuid/chunks` | 該身份的時間軸片段 | ✅ |
+| 29 | POST | `/api/v1/identity/:identity_uuid/bind` | 綁定信號至身份 | ✅ |
+| 30 | POST | `/api/v1/identity/:identity_uuid/unbind` | 解除綁定 | ✅ |
+| 31 | POST | `/api/v1/identity/:from_uuid/mergeinto` | 合併身份（將 from 合併至目標） | ✅ |
+
+### 5. 臉部（Face）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 32 | GET | `/api/v1/faces/candidates` | 臉部候選列表（未綁定者） | ✅ |
+
+### 6. 媒體串流（Media）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 33 | GET | `/api/v1/file/:file_uuid/video` | 影片串流 | ✅ |
+| 34 | GET | `/api/v1/file/:file_uuid/video/bbox` | 含 Bounding Box 的影片串流 | ✅ |
+| 35 | GET | `/api/v1/file/:file_uuid/trace/:trace_id/video` | 特定 trace 的影片片段 | ✅ |
+| 36 | GET | `/api/v1/file/:file_uuid/thumbnail` | 影片縮圖 | ✅ |
+
+### 7. 檔案身份關聯（File-Identity）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 37 | GET | `/api/v1/file/:file_uuid/identities` | 該檔案的所有關聯身份 | ✅ |
+
+### 8. Agent
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 38 | POST | `/api/v1/agents/translate` | 翻譯 Agent | ✅ |
+| 39 | POST | `/api/v1/agents/identity/analyze` | 身份分析 Agent | ✅ |
+| 40 | POST | `/api/v1/agents/identity/suggest` | 身份合併建議 | ✅ |
+| 41 | GET | `/api/v1/agents/identity/status` | 身份 Agent 狀態 | ✅ |
+| 42 | POST | `/api/v1/agents/suggest/clustering` | 聚類建議 | ✅ |
+| 43 | POST | `/api/v1/agents/suggest/merge` | 合併建議 | ✅ |
+| 44 | POST | `/api/v1/agents/5w1h/analyze` | 5W1H 分析 | ✅ |
+| 45 | POST | `/api/v1/agents/5w1h/batch` | 5W1H 批量分析 | ✅ |
+| 46 | GET | `/api/v1/agents/5w1h/status` | 5W1H 狀態 | ✅ |
+
+### 9. 資源管理（Resource）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 47 | POST | `/api/v1/resource/register` | 註冊運算資源 | ✅ |
+| 48 | POST | `/api/v1/resource/heartbeat` | 資源心跳回報 | ✅ |
+| 49 | GET | `/api/v1/resources` | 資源列表 | ✅ |
+
+### 10. 統計與設定（Stats & Config）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 50 | GET | `/api/v1/stats/ingest` | 攝取統計（video/chunk 計數） | ✅ |
+| 51 | GET | `/api/v1/stats/sftpgo` | SFTPGo 使用者狀態 | ✅ |
+| 52 | GET | `/api/v1/stats/inference` | 推理叢集健康狀態 | ✅ |
+| 53 | POST | `/api/v1/config/cache` | 切換快取開關 | ✅ |
+| 54 | POST | `/api/v1/config/auto-pipeline` | 註冊後自動處理 | ✅ |
+| 55 | POST | `/api/v1/config/watcher-auto-register` | Watcher 自動註冊 | ✅ |
+
+---
+
+## 未掛載的端點（定義了 handler 但未註冊路由）
+
+| Handler | 位置 | 說明 |
+|---------|------|------|
+| `POST /api/v1/file/:file_uuid/face_trace/sortby` | `trace_agent_api.rs` | 定義了 `trace_agent_routes()` 但從未被 `server.rs` merge |
+
+---
+
+## 程式碼中存在 handler 但未註冊路由的端點
+
+下列 handler 有實作但**沒有對應的 `.route()` 呼叫**，無法透過 HTTP 存取：
+
+- `GET /api/v1/assets/:uuid/status` — `get_asset_status`
+- `GET /api/v1/jobs/:job_id` — `get_job`
+- `GET /api/v1/rules/:rule/status` — `get_rule_status`
+- `GET /api/v1/videos/:uuid/details` — `video_details`
+- `DELETE /api/v1/videos/:uuid` — `delete_video`
+- `POST /api/v1/search` — `search`（語意搜尋）
+- `POST /api/v1/search/hybrid` — `hybrid_search`
+- `POST /api/v1/search/bm25` — `search_bm25`
+- `GET /api/v1/lookup` — `lookup`
+- `POST /api/v1/search/smart` — `search_smart`（server.rs 版，實際註冊的是 search.rs 版）
+
+---
+
+## 與 V1.0 文件的差異
+
+V1.0 文件（`MOMENTRY_CORE_API_V1.0.0.md`）宣稱的端點中有以下**不存在於實際程式碼**：
+
+| 文件宣稱 | 實際狀況 |
+|----------|---------|
+| `DELETE /api/v1/videos/:uuid` | handler 存在但未註冊路由 |
+| `POST /api/v1/search` | handler 存在但未註冊路由 |
+| `POST /api/v1/search/hybrid` | handler 存在但未註冊路由 |
+| `POST /api/v1/assets/:uuid/process` | 實際是 `POST /api/v1/file/:file_uuid/process` |
+| `GET /api/v1/files/:uuid/snapshots` | 不存在 |
+| `POST /api/v1/files/:uuid/snapshots/migrate` | 不存在 |
+| `GET /api/v1/face/list` | 不存在 |
+| `POST /api/v1/face/recognize` | 不存在 |
+
+---
+
+## 路徑命名慣例
+
+| 資源 | 路由格式 | 參數 |
+|------|---------|------|
+| 檔案 | `/api/v1/file/:file_uuid` | 32 碼 hex string |
+| 身份 | `/api/v1/identity/:identity_uuid` | UUID v4 |
+| 資源 | `/api/v1/resource/...` | - |
+
+注意路徑使用**單數**（`file`, `identity`），與 RELEASE 文件的 `files`, `identities` 不同。
@@ -0,0 +1,216 @@
+---
+document_type: "reference_doc"
+service: "MOMENTRY_CORE"
+title: "Momentry Core Dev API 參考文件"
+date: "2026-05-06"
+version: "V1.1"
+status: "deprecated"
+owner: "Warren"
+---
+
+> ⚠️ **此文件為 V3.x 歷史參考，含已移除的路由。**
+> 請改用 `API_DICTIONARY_V1.0.0.md`（root）取得當前準確的 53 條 API 路由。
+created_by: "OpenCode"
+tags:
+  - "api"
+  - "reference"
+  - "dev"
+  - "v1.1"
+  - "restful"
+related_documents:
+  - "MOMENTRY_CORE_API_V1.0.0.md"
+  - "RELEASE/RELEASE_API_REFERENCE_v1.0.0.md"
+---
+
+# Momentry Core Dev API 參考文件
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-06 |
+| 文件版本 | V1.1 |
+| Base URL | `http://localhost:3003` |
+| 認證方式 | Header `X-API-Key`（部分端點需要） |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 |
+|------|------|------|--------|
+| V1.1 | 2026-05-06 | 從程式碼實際路由重新產生 53 端點清單 | OpenCode |
+| V1.0 | 2026-04-30 | 原始文件，含多個不存在之端點 | OpenCode |
+
+---
+
+## 認證
+
+- **Header**: `X-API-Key: <your_api_key>`
+- 目前 `/api/v1/auth/login` 回傳固定 demo Key: `muser_test_001`
+- Protected routes 透過 `api_key_validation` middleware 驗證
+- Public routes（免 Key）: `/health`, `/health/detailed`, `/api/v1/auth/login`
+
+---
+
+## 端點列表
+
+總計 **53 個註冊路由**（另有 1 個定義但未掛載）。
+
+### 1. 系統與認證（System & Auth）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 1 | GET | `/health` | 基本健康檢查（回傳 status/version/uptime） | ❌ |
+| 2 | GET | `/health/detailed` | 詳細健康狀態（含 PG/Redis/Qdrant/MongoDB 各別延遲） | ❌ |
+| 3 | POST | `/api/v1/auth/login` | 登入（固定 demo/demo，回傳 API Key） | ❌ |
+| 4 | POST | `/api/v1/auth/logout` | 登出 | ✅ |
+
+### 2. 檔案管理（File Management）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 5 | GET | `/api/v1/files` | 檔案列表（支援分頁、status、q、uuid 過濾） | ✅ |
+| 6 | GET | `/api/v1/file/:file_uuid` | 檔案詳細資訊（含 probe_json、metadata） | ✅ |
+| 7 | POST | `/api/v1/files/register` | 從磁碟註冊新檔案（支援 pattern 批次註冊） | ✅ |
+| 8 | POST | `/api/v1/unregister` | 取消註冊檔案 | ✅ |
+| 9 | GET | `/api/v1/files/scan` | 掃描 SFTPGo demo 目錄中的新檔案 | ✅ |
+| 10 | GET | `/api/v1/file/:file_uuid/probe` | 取得/快取 ffprobe 資訊 | ✅ |
+| 11 | POST | `/api/v1/file/:file_uuid/process` | 啟動處理 pipeline（建立 monitor job） | ✅ |
+| 12 | GET | `/api/v1/file/:file_uuid/chunks` | 列出 pre_chunks | ✅ |
+| 13 | GET | `/api/v1/progress/:uuid` | 即時處理進度（來自 Redis PubSub） | ✅ |
+| 14 | GET | `/api/v1/jobs` | 任務列表（支援分頁、status 過濾） | ✅ |
+
+### 3. 搜尋（Search）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 15 | POST | `/api/v1/search/visual` | 視覺搜尋 | ✅ |
+| 16 | POST | `/api/v1/search/visual/class` | 依物件類別過濾搜尋 | ✅ |
+| 17 | POST | `/api/v1/search/visual/density` | 依視覺密度搜尋 | ✅ |
+| 18 | POST | `/api/v1/search/visual/stats` | 視覺統計資料 | ✅ |
+| 19 | POST | `/api/v1/search/visual/combination` | 視覺組合搜尋（多條件） | ✅ |
+| 20 | POST | `/api/v1/search/smart` | 智慧搜尋（語意向量） | ✅ |
+| 21 | POST | `/api/v1/search/universal` | 通用搜尋 | ✅ |
+| 22 | POST | `/api/v1/search/frames` | 影格搜尋 | ✅ |
+
+### 4. 身份管理（Identity）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 23 | GET | `/api/v1/identities` | 身份列表 | ✅ |
+| 24 | POST | `/api/v1/identity` | 建立身份（從 face.json 建立參考向量） | ✅ |
+| 25 | GET | `/api/v1/identity/:identity_uuid` | 身份詳細資訊 | ✅ |
+| 26 | DELETE | `/api/v1/identity/:identity_uuid` | 刪除身份 | ✅ |
+| 27 | GET | `/api/v1/identity/:identity_uuid/files` | 該身份出現的所有檔案 | ✅ |
+| 28 | GET | `/api/v1/identity/:identity_uuid/chunks` | 該身份的時間軸片段 | ✅ |
+| 29 | POST | `/api/v1/identity/:identity_uuid/bind` | 綁定信號至身份 | ✅ |
+| 30 | POST | `/api/v1/identity/:identity_uuid/unbind` | 解除綁定 | ✅ |
+| 31 | POST | `/api/v1/identity/:from_uuid/mergeinto` | 合併身份（將 from 合併至目標） | ✅ |
+
+### 5. 臉部（Face）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 32 | GET | `/api/v1/faces/candidates` | 臉部候選列表（未綁定者） | ✅ |
+
+### 6. 媒體串流（Media）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 33 | GET | `/api/v1/file/:file_uuid/video` | 影片串流 | ✅ |
+| 34 | GET | `/api/v1/file/:file_uuid/video/bbox` | 含 Bounding Box 的影片串流 | ✅ |
+| 35 | GET | `/api/v1/file/:file_uuid/trace/:trace_id/video` | 特定 trace 的影片片段 | ✅ |
+| 36 | GET | `/api/v1/file/:file_uuid/thumbnail` | 影片縮圖 | ✅ |
+
+### 7. 檔案身份關聯（File-Identity）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 37 | GET | `/api/v1/file/:file_uuid/identities` | 該檔案的所有關聯身份 | ✅ |
+
+### 8. Agent
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 38 | POST | `/api/v1/agents/translate` | 翻譯 Agent | ✅ |
+| 39 | POST | `/api/v1/agents/identity/analyze` | 身份分析 Agent | ✅ |
+| 40 | POST | `/api/v1/agents/identity/suggest` | 身份合併建議 | ✅ |
+| 41 | GET | `/api/v1/agents/identity/status` | 身份 Agent 狀態 | ✅ |
+| 42 | POST | `/api/v1/agents/suggest/clustering` | 聚類建議 | ✅ |
+| 43 | POST | `/api/v1/agents/suggest/merge` | 合併建議 | ✅ |
+| 44 | POST | `/api/v1/agents/5w1h/analyze` | 5W1H 分析 | ✅ |
+| 45 | POST | `/api/v1/agents/5w1h/batch` | 5W1H 批量分析 | ✅ |
+| 46 | GET | `/api/v1/agents/5w1h/status` | 5W1H 狀態 | ✅ |
+
+### 9. 資源管理（Resource）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 47 | POST | `/api/v1/resource/register` | 註冊運算資源 | ✅ |
+| 48 | POST | `/api/v1/resource/heartbeat` | 資源心跳回報 | ✅ |
+| 49 | GET | `/api/v1/resources` | 資源列表 | ✅ |
+
+### 10. 統計與設定（Stats & Config）
+
+| # | Method | Path | 說明 | 需 Key |
+|---|--------|------|------|--------|
+| 50 | GET | `/api/v1/stats/ingest` | 攝取統計（video/chunk 計數） | ✅ |
+| 51 | GET | `/api/v1/stats/sftpgo` | SFTPGo 使用者狀態 | ✅ |
+| 52 | GET | `/api/v1/stats/inference` | 推理叢集健康狀態 | ✅ |
+| 53 | POST | `/api/v1/config/cache` | 切換快取開關 | ✅ |
+| 54 | POST | `/api/v1/config/auto-pipeline` | 註冊後自動處理 | ✅ |
+| 55 | POST | `/api/v1/config/watcher-auto-register` | Watcher 自動註冊 | ✅ |
+
+---
+
+## 未掛載的端點（定義了 handler 但未註冊路由）
+
+| Handler | 位置 | 說明 |
+|---------|------|------|
+| `POST /api/v1/file/:file_uuid/face_trace/sortby` | `trace_agent_api.rs` | 定義了 `trace_agent_routes()` 但從未被 `server.rs` merge |
+
+---
+
+## 程式碼中存在 handler 但未註冊路由的端點
+
+下列 handler 有實作但**沒有對應的 `.route()` 呼叫**，無法透過 HTTP 存取：
+
+- `GET /api/v1/assets/:uuid/status` — `get_asset_status`
+- `GET /api/v1/jobs/:job_id` — `get_job`
+- `GET /api/v1/rules/:rule/status` — `get_rule_status`
+- `GET /api/v1/videos/:uuid/details` — `video_details`
+- `DELETE /api/v1/videos/:uuid` — `delete_video`
+- `POST /api/v1/search` — `search`（語意搜尋）
+- `POST /api/v1/search/hybrid` — `hybrid_search`
+- `POST /api/v1/search/bm25` — `search_bm25`
+- `GET /api/v1/lookup` — `lookup`
+- `POST /api/v1/search/smart` — `search_smart`（server.rs 版，實際註冊的是 search.rs 版）
+
+---
+
+## 與 V1.0 文件的差異
+
+V1.0 文件（`MOMENTRY_CORE_API_V1.0.0.md`）宣稱的端點中有以下**不存在於實際程式碼**：
+
+| 文件宣稱 | 實際狀況 |
+|----------|---------|
+| `DELETE /api/v1/videos/:uuid` | handler 存在但未註冊路由 |
+| `POST /api/v1/search` | handler 存在但未註冊路由 |
+| `POST /api/v1/search/hybrid` | handler 存在但未註冊路由 |
+| `POST /api/v1/assets/:uuid/process` | 實際是 `POST /api/v1/file/:file_uuid/process` |
+| `GET /api/v1/files/:uuid/snapshots` | 不存在 |
+| `POST /api/v1/files/:uuid/snapshots/migrate` | 不存在 |
+| `GET /api/v1/face/list` | 不存在 |
+| `POST /api/v1/face/recognize` | 不存在 |
+
+---
+
+## 路徑命名慣例
+
+| 資源 | 路由格式 | 參數 |
+|------|---------|------|
+| 檔案 | `/api/v1/file/:file_uuid` | 32 碼 hex string |
+| 身份 | `/api/v1/identity/:identity_uuid` | UUID v4 |
+| 資源 | `/api/v1/resource/...` | - |
+
+注意路徑使用**單數**（`file`, `identity`），與 RELEASE 文件的 `files`, `identities` 不同。
@@ -0,0 +1,241 @@
+---
+document_type: "reference_doc"
+service: "MOMENTRY_CORE"
+title: "Momentry Core V1.0.0 API 參考文件"
+date: "2026-04-30"
+version: "V1.0"
+status: "superseded"
+owner: "Warren"
+created_by: "OpenCode"
+tags:
+  - "api"
+  - "reference"
+  - "v1.0.0"
+  - "marcom"
+  - "restful"
+  - "endpoint"
+  - "file-centric"
+ai_query_hints:
+  - "Momentry Core V1.0.0 API 參考文件的主要內容是什麼？"
+  - "查詢 V1.0.0 API 列表包含哪些端點？"
+  - "Marcom 團隊如何使用 API Reference？"
+  - "API 的 Progressive Workflow 範例"
+  - "Momentry API 的檔案管理與搜尋功能"
+  - "API 的 Progressive Workflow 操作步驟"
+  - "API 的檔案管理與搜尋功能"
+related_documents:
+  - "STANDARDS/DOCS_STANDARD.md"
+  - "DEV_API_V1.0/API_REFERENCE_v1.0.0.md"
+  - "API_DICTIONARY_V1.0.0.md"
+  - "API_USAGE_DEMO_V1.0.0.md"
+  - "PRODUCTION_VERIFICATION_V1.0.0.md"
+---
+
+# Momentry Core V1.0.0 API 參考文件
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-04-30 |
+| 文件版本 | V1.0 |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-04-30 | 創建 V1.0.0 API 列表，移除過時端點 | OpenCode | OpenCode |
+| V1.1 | 2026-05-06 | 被 DEV_API_REFERENCE_v1.0.0.md 取代（實際路由與此文件有大量差異） | OpenCode | OpenCode |
+
+---
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| file_uuid | 媒體檔案（影片/圖片/音訊）的唯一 32 碼 SHA256 識別碼 |
+| identity_uuid | 全域人物身份識別碼，跨檔案關聯同一人物 |
+| Chunk | 可搜尋單位，由 Rule 組合 pre_chunks 產出 |
+| Snapshot | 臉部或場景的快取快照，需 migrate 後供 UI 使用 |
+| API Key | 認證方式，透過 Header `X-API-Key` 傳遞 |
+
+## 概述
+
+本文檔定義 Momentry Core **V1.0.0** 版本供 **Marcom 團隊** 使用的 API 列表與開發範例。此列表已移除舊版、冗餘及內部使用的端點，確保前端開發使用的是標準且穩定的介面。
+
+---
+
+## 🚀 設計原則 (Design Principles)
+
+### 1. Clear API (介面清晰化)
+*   **去蕪存菁**: 嚴格區分 **Public** (公開) 與 **Internal** (內部) 端點。舊版冗餘路徑（如 `/api/v1/videos`, `/api/v1/probe`）已全面移除或合併。
+*   **標準化回應**: 所有列表型 API 均回傳統一結構 `{ "success": true, "data": [...], "total": N }`。
+*   **命名規範**: 採用 RESTful 風格，資源以複數名詞或明確動作命名（如 `files`, `identities`）。
+
+### 2. File-Centric (以檔案為核心)
+*   **唯一識別**: 每個媒體檔案（影片/圖片/音訊）均由 **32 碼 UUID** (`file_uuid`) 唯一標識。
+*   **生命週期**: `File` 是所有資料的根節點。所有的 `Chunk` (片段), `Snapshot` (快照), `Jobs` (任務) 皆隸屬於特定的 `File`。
+*   **操作模式**: 前端應優先呼叫 `GET /api/v1/files` 取得清單，再透過 `POST /api/v1/files/:uuid/snapshots/migrate` 載入詳細資源。
+
+### 3. Global Identity (全域身份識別)
+*   **跨檔案關聯**: `Identity` 代表一個獨立的人物或角色，不受單一檔案限制。
+*   **綁定機制 (Binding)**: 透過 `POST /api/v1/identities/bind`，我們可以將多個檔案中偵測到的臉部 (`face`) 或聲音 (`speaker`) 聚合到同一個 `Identity` 下。
+*   **資料聚合**: 查詢某個 `Identity` 即可看到該人物在所有歷史檔案中的軌跡 (`/api/v1/identities/:uuid/files`)。
+
+---
+
+## 當前狀態
+
+| 項目 | 狀態 |
+|------|------|
+| API 版本 | V1.0.0 |
+| 開發環境 Port | 3003 |
+| 正式環境 Port | 3002 |
+| 認證方式 | Header `X-API-Key` |
+
+---
+
+## 1. API Dictionary (端點清單)
+
+### 1.1 系統與認證 (System & Auth)
+| Method | Endpoint | 說明 |
+| :--- | :--- | :--- |
+| `GET` | `/health` | 基本健康檢查 |
+| `POST` | `/api/v1/auth/login` | 登入以取得 API Key |
+
+### 1.2 檔案管理 (File Management)
+*主要入口：瀏覽與管理資產*
+| Method | Endpoint | 說明 |
+| :--- | :--- | :--- |
+| `GET` | `/api/v1/files` | **列出所有檔案** (支援分頁) |
+| `GET` | `/api/v1/files/:uuid` | 取得檔案詳情 (包含 probe_json, metadata) |
+| `POST` | `/api/v1/files/register` | 從磁碟註冊新檔案 |
+| `DELETE`| `/api/v1/videos/:uuid` | **刪除影片** 及其關聯資料 |
+
+### 1.3 搜尋與檢索 (Search & Retrieval)
+| Method | Endpoint | 說明 |
+| :--- | :--- | :--- |
+| `POST` | `/api/v1/search` | **語意搜尋** (Text-based, 使用 Embedding) |
+| `POST` | `/api/v1/search/hybrid` | 混合搜尋 (Vector + BM25 關鍵字) |
+| `POST` | `/api/v1/search/visual` | 視覺搜尋 (尋找物件/形狀) |
+| `POST` | `/api/v1/search/visual/class`| 依物件類別過濾 (如 "person", "car") |
+
+### 1.4 身份與人物管理 (Identity Management)
+*跨影片的人物/角色關聯*
+| Method | Endpoint | 說明 |
+| :--- | :--- | :--- |
+| `GET` | `/api/v1/identities` | **列出所有身份** (人物/角色) |
+| `GET` | `/api/v1/identities/:uuid` | 取得身份詳情 (名稱, 品質, 來源) |
+| `GET` | `/api/v1/identities/:uuid/files`| 列出該身份出現的所有檔案 |
+| `GET` | `/api/v1/identities/:uuid/chunks`| 列出特定的時間軸片段 (Chunks) |
+| `POST` | `/api/v1/identities/bind` | 將臉部/聲音訊號綁定至身份 |
+
+### 1.5 臉部與快照 (Face & Snapshots)
+| Method | Endpoint | 說明 |
+| :--- | :--- | :--- |
+| `GET` | `/api/v1/face/list` | 列出特定影片中偵測到的所有臉部 |
+| `POST` | `/api/v1/face/recognize` | 對指定影片觸發臉部辨識流程 |
+| `GET` | `/api/v1/files/:uuid/snapshots` | 檢查快照快取狀態 (Hot/Cold) |
+| `POST` | `/api/v1/files/:uuid/snapshots/migrate`| **載入快照至記憶體** (UI 顯示快圖前需呼叫) |
+
+### 1.6 任務與代理人 (Jobs & Agents)
+| Method | Endpoint | 說明 |
+| :--- | :--- | :--- |
+| `GET` | `/api/v1/progress/:uuid` | 檢查即時處理進度 |
+| `POST` | `/api/v1/assets/:uuid/process` | 觸發處理流程 (ASR, YOLO, 等) |
+| `POST` | `/api/v1/agents/identity/analyze` | AI Agent: 分析身份重複情況 |
+
+---
+
+## 2. Progressive Workflow Examples (操作範例)
+
+此章節展示典型的使用者操作情境：**尋找影片 → 處理 → 搜尋 → 人物綁定**。
+
+### Phase 1: 瀏覽與檢視
+*使用者瀏覽檔案庫以尋找目標影片。*
+
+**Step 1: 登入**
+```bash
+curl -s -X POST http://localhost:3003/api/v1/auth/login \
+  -H "Content-Type: application/json" \
+  -d '{"username": "demo", "password": "demo"}'
+# 回應範例: { "api_key": "muser_test_001..." }
+```
+
+**Step 2: 列出檔案**
+```bash
+curl -s "http://localhost:3003/api/v1/files?page=1&page_size=5" \
+  -H "X-API-Key: muser_test_001"
+# 回應範例: { "success": true, "data": [ { "file_uuid": "...", "file_name": "Demo.mp4" ... } ] }
+```
+
+### Phase 2: 處理與監控
+*使用者決定分析該影片的臉部與語音內容。*
+
+**Step 3: 觸發處理**
+```bash
+curl -s -X POST "http://localhost:3003/api/v1/assets/{file_uuid}/process" \
+  -H "X-API-Key: muser_test_001" \
+  -H "Content-Type: application/json" \
+  -d '{}'
+# 啟動 ASR, 臉部偵測等處理器
+```
+
+**Step 4: 檢查進度**
+```bash
+curl -s "http://localhost:3003/api/v1/progress/{file_uuid}" \
+  -H "X-API-Key: muser_test_001"
+# 回應範例: { "overall_progress": 50, "processors": [...] }
+```
+
+### Phase 3: 搜尋內容
+*使用者搜尋影片中的特定內容。*
+
+**Step 5: 語意搜尋 (文字描述)**
+```bash
+curl -s -X POST "http://localhost:3003/api/v1/search" \
+  -H "X-API-Key: muser_test_001" \
+  -H "Content-Type: application/json" \
+  -d '{"query": "一個人拿著紅色的信封", "uuid": "{file_uuid}"}'
+# 回應範例: 符合文字描述的片段列表
+```
+
+### Phase 4: 身份管理 (GUI 開發重點)
+*使用者發現了一張臉，確認該人物，並將其綁定到已知身份。*
+
+**Step 6: 載入快照 (Migrate Snapshots)**
+*在 GUI 渲染大量臉部縮圖前，必須先將快取載入記憶體以加速讀取。*
+```bash
+curl -s -X POST "http://localhost:3003/api/v1/files/{file_uuid}/snapshots/migrate" \
+  -H "X-API-Key: muser_test_001" \
+  -H "Content-Type: application/json" \
+  -d '{"parent_uuid": "{file_uuid}"}'
+# 回應範例: { "success": true, "migrated_types": ["faces", ...] }
+```
+
+**Step 7: 綁定臉部到身份 (Bind Face)**
+*假設偵測到臉部 `face_123`，欲綁定至身份 `uuid_identity`。*
+```bash
+curl -s -X POST "http://localhost:3003/api/v1/identities/bind" \
+  -H "X-API-Key: muser_test_001" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "identity_id": null,
+    "name": "Cary Grant",
+    "binding_type": "face",
+    "binding_value": "face_123"
+  }'
+```
+
+---
+
+## 3. 棄用聲明 (Deprecation Notices)
+
+以下端點已在 V1.0.0 移除或棄用，**請勿**在新的開發中使用。
+
+*   `GET /api/v1/videos` (列表) → 已取代為 `GET /api/v1/files`
+*   `POST /api/v1/register` → 已取代為 `POST /api/v1/files/register`
+*   `POST /api/v1/probe` → 已取代為 `GET /api/v1/files/:uuid`
+*   `GET /api/v1/people/...` → 已合併為 `GET /api/v1/identities/...`
+*   `/api/v1/n8n/search/...` → 僅供內部 n8n 工作流使用 (請使用標準 `/api/v1/search`)
@@ -0,0 +1,145 @@
+# Physical Scene Analysis v1.0.0
+
+將 CUT processor 從「場景切換偵測」升級為「場景物理特徵分析」。
+
+## 流程
+
+```
+CUT (現有)                Physical Analysis (新增)
+┌──────────────┐         ┌──────────────────────┐
+│ scenedetect  │ ──→     │ ffmpeg signalstats   │
+│ frame_range  │         │ ffmpeg ebur128       │
+│ scene_050    │         │ ffmpeg tblend        │
+│ scene_051    │         │ 逐 scene 計算特徵     │
+└──────────────┘         └──────────┬───────────┘
+                                     │
+                                     ▼
+                            ┌──────────────────┐
+                            │ scene_050.json    │
+                            │ scene_051.json    │ ← 原 JSON + 物理特徵
+                            └──────────────────┘
+```
+
+## API
+
+### POST /api/v1/file/:file_uuid/physical/analyze
+
+對已註冊的影片執行物理特徵分析。
+
+#### Request
+
+```json
+{
+  "features": ["luminance", "loudness", "silence", "motion", "color"],
+  "bin_scenes": true,
+  "time_range": [0, 5954]
+}
+```
+
+| 參數 | 類型 | 預設 | 說明 |
+|------|------|------|------|
+| `features` | string[] | 全部 | 指定要分析的特徵 |
+| `bin_scenes` | bool | true | 以 scene 為 bucket（vs 固定時間間隔） |
+| `time_range` | [float,float] | 全片 | 分析區間 |
+
+#### Response
+
+```json
+{
+  "file_uuid": "3abeee81...",
+  "duration": 5954,
+  "feature_count": 1130,
+  "features": {
+    "luminance": {
+      "unit": "Y_channel_mean",
+      "global_avg": 45.2,
+      "global_min": 16.0,
+      "global_max": 128.0,
+      "data": [
+        {"scene": 1, "t_start": 0, "t_end": 34.68, "value": 51.3, "contrast": 23.7},
+        {"scene": 2, "t_start": 34.72, "t_end": 38.92, "value": 33.2, "contrast": 12.3}
+      ]
+    },
+    "loudness": {
+      "unit": "LUFS",
+      "global_avg": -23.1,
+      "global_max": -10.3,
+      "data": [
+        {"scene": 1, "t_start": 0, "t_end": 34.68, "value": -28.5, "peak": -16.2},
+        {"scene": 2, "t_start": 34.72, "t_end": 38.92, "value": -18.5, "peak": -12.1}
+      ]
+    },
+    "silence": {
+      "data": [
+        {"scene": 1, "count": 1, "total_duration": 29.9, "ratio": 0.86},
+        {"scene": 2, "count": 0, "total_duration": 0, "ratio": 0}
+      ]
+    },
+    "motion": {
+      "unit": "frame_diff_mean",
+      "data": [
+        {"scene": 1, "value": 0.12},
+        {"scene": 2, "value": 0.45}
+      ]
+    },
+    "color": {
+      "unit": "dominant_temp",
+      "data": [
+        {"scene": 1, "temp": 5600, "dominant": "warm"},
+        {"scene": 2, "temp": 3200, "dominant": "cool"}
+      ]
+    }
+  },
+  "anomalies": [
+    {"scene": 1, "type": "extreme_silence", "value": 0.86, "description": "片頭靜音 86%"},
+    {"scene": 8, "type": "black_frame", "value": 16.0, "description": "fade-to-black 轉場"}
+  ]
+}
+```
+
+## 實作
+
+### 單一 ffmpeg 命令（全片）
+
+```bash
+ffmpeg -i input.mp4 \
+  -vf "signalstats,select='gt(scene,0.3)',metadata=print" \
+  -af "ebur128=framelog=verbose" \
+  -f null - 2>&1 | python3 scripts/parse_physical_features.py
+```
+
+### 逐 scene 分析（搭配 CUT 輸出）
+
+CUT 輸出已知 scene boundaries，可以只對關鍵幀算特徵：
+
+```bash
+# 對每個 scene 取 middle frame 算亮度
+ffmpeg -i input.mp4 -vf "select='eq(n,1366)+eq(n,1607)'" \
+  -vsync 0 -f image2 /tmp/frames/%d.jpg
+```
+
+### Post-Processing Pipeline 整合
+
+在 `processor.rs` 中新增一個 processor type `physical`：
+
+```rust
+ProcessorType::Physical => {
+    let output = physical_analysis(uuid, &video_path).await?;
+    db.store_physical_features(uuid, &output).await?;
+}
+```
+
+### DB Schema
+
+```sql
+CREATE TABLE dev.physical_features (
+  id BIGSERIAL PRIMARY KEY,
+  file_uuid VARCHAR(32) NOT NULL,
+  scene_number INT NOT NULL,
+  feature_type VARCHAR(20) NOT NULL,  -- luminance | loudness | silence | motion | color
+  value FLOAT NOT NULL,
+  metadata JSONB DEFAULT '{}',
+  created_at TIMESTAMPTZ DEFAULT NOW()
+);
+CREATE INDEX idx_physical_file ON dev.physical_features(file_uuid);
+```
@@ -0,0 +1,102 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "ASRX Processor V1.0.0"
+date: "2026-05-02"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+parent: "PROCESSOR_SELECTION_V1.0.0.md"
+tags:
+  - "momentry"
+  - "core"
+  - "processor"
+  - "asrx"
+  - "speaker-diarization"
+  - "speechbrain"
+  - "v1.0.0"
+ai_query_hints:
+  - "ASRX 使用 SpeechBrain ECAPA-TDNN 進行說話者日誌化"
+  - "ASRX 從 Pyannote 遷移至自定義 SpeechBrain，快 6 倍"
+  - "ASRX 不需要 HuggingFace token（相較 Pyannote）"
+  - "ASRX Charade 6879s 長片輸出 1118 segments, 8 說話人"
+  - "ASRX 依賴 ASR processor 的轉錄結果"
+related_documents:
+  - "PROCESSOR_SELECTION_V1.0.0.md"
+  - "../ASR_V1.0.0.md"
+  - "../CUT_V1.0.0.md"
+  - "../VOICE_EMBEDDING_FLOW_V1.0.0.md"
+  - "../VECTOR_SPEC_V1.0.0.md"
+---
+
+# ASRX Processor V1.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-02 |
+| 文件版本 | V1.0 |
+
+**狀態**: ⚠️ 80% | **模型**: SpeechBrain ECAPA-TDNN | **GPU**: 否
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| ASRX | 進階語音處理，包含說話者日誌化（Speaker Diarization） |
+| Speaker Diarization | 說話者日誌化，區分「誰在什麼時候說話」 |
+| ECAPA-TDNN | SpeechBrain 提供的說話人辨識模型，產出 192-D embedding |
+| VAD | Voice Activity Detection，語音活動檢測（使用 Silero） |
+| Spectral Clustering | 頻譜聚類，將 embedding 分群以區分不同說話人 |
+
+---
+
+## 選型過程
+
+| 指標 | Pyannote-based（原始） | Custom SpeechBrain（新） |
+|------|----------------------|------------------------|
+| Pipeline | VAD → Whisper → Align → Diarize | VAD (Silero) → ECAPA-TDNN → Spectral Clustering |
+| 處理時間 | 4.79s（輸出為空） | **1.66s** (96.25x) |
+| 比 Pyannote 快 | 基準 | **6x 更快** |
+| HuggingFace token | ✅ **需要** | ❌ **不需要** |
+| 重疊語音 | ✅ 支援 | ❌ 不支援 |
+
+**決策**: 因 pyannote.audio 需要 HuggingFace token、import 錯誤頻繁、輸出為空，已改為自定義 SpeechBrain 實作。
+
+---
+
+## 處理時間分解（Custom SpeechBrain）
+
+| 步驟 | 時間 | 佔比 |
+|------|------|------|
+| VAD (Silero) | 0.41s | 24.7% |
+| Speaker embedding (ECAPA-TDNN) | 1.15s | 69.3% |
+| Spectral clustering | 0.10s | 6.0% |
+
+---
+
+## Charade 長片（6879s）
+
+| 指標 | 值 |
+|------|-----|
+| Segments | 1118 |
+| 說話人數 | 8 |
+| 匹配率 | 99.82% |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+
+## 資源預估
+
+| 資源 | 值 |
+|------|-----|
+| CPU | 0.8 |
+| 記憶體 | 2048 MB |
+| GPU | 不使用 |
+| 依賴 | ASR |
@@ -0,0 +1,243 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "ASR Processor V1.0.0"
+date: "2026-05-02"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+parent: "PROCESSOR_SELECTION_V1.0.0.md"
+tags:
+  - "momentry"
+  - "core"
+  - "processor"
+  - "asr"
+  - "whisper"
+  - "speech-recognition"
+  - "v1.0.0"
+ai_query_hints:
+  - "ASR 使用 faster-whisper/small 模型及 INT8 CPU 量化"
+  - "ASR 以 CUT 場景邊界為基礎分段處理長片"
+  - "ASR 每個 segment 記錄 scene_number 對應 CUT 場景序號"
+  - "ASR 處理 159.6s 影片約 12.68s，即時倍率 12.6x"
+  - "ASR 依賴 CUT processor 的場景邊界輸出"
+related_documents:
+  - "PROCESSOR_SELECTION_V1.0.0.md"
+  - "../CUT_V1.0.0.md"
+  - "../ASRX_V1.0.0.md"
+  - "../STORY_V1.0.0.md"
+  - "../CHUNK_DEFINITION_V1.0.0.md"
+---
+
+# ASR Processor V1.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-02 |
+| 文件版本 | V1.0 |
+
+**狀態**: ✅ 100% | **模型**: faster-whisper/small | **GPU**: 否
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| ASR | Automatic Speech Recognition，自動語音辨識 |
+| faster-whisper | 基於 OpenAI Whisper 的優化版本，支援 INT8 CPU 量化 |
+| segment | Whisper 輸出的語音片段，包含 start/end/time/text |
+| scene_number | CUT 場景序號（1-based），標示 segment 所屬場景 |
+| real-time factor | 即時倍率，處理時間與影片時長的比值 |
+
+---
+
+## 選型過程
+
+| 模型 | 參數 | 大小 | English WER | Chinese CER | 速度 |
+|------|------|------|-------------|-------------|------|
+| tiny | 39M | ~40MB | 9.5% | 15.0% | ~1x RT |
+| base | 74M | ~75MB | 7.3% | 11.2% | ~1.5x RT |
+| **small** | **244M** | **~250MB** | **5.5%** | **8.4%** | **~2x RT** |
+| medium | 769M | ~800MB | 4.3% | 6.4% | ~3x RT |
+| large-v3 | 1.5B | ~1.5GB | 3.5% | 4.9% | ~5x RT |
+
+**決策**: small 在準確率與速度間取得最佳平衡，經實驗驗證最少要使用 small 才能較好處理多語種及台灣腔國語。
+
+---
+
+## 效能實測（ExaSAN 159.6s 影片）
+
+| 指標 | 值 |
+|------|-----|
+| 處理時間 | 12.68s |
+| 即時倍率 | 12.6x |
+| 輸出 | 78~79 segments, ~15KB |
+
+---
+
+## 長片分段處理
+
+對於長片（如 Charade 6879s），ASR 以 CUT processor 產出的場景邊界為基礎分段處理：
+
+1. CUT 先產出 `{file_uuid}.cut.json`（含 `scenes[]`，每個有 `start_time`/`end_time`）
+2. ASR 讀取 CUT JSON，依 `scene_number` 順序對每個場景萃取音訊
+3. 每個場景分別用 Whisper 轉錄
+4. 合併結果，每個 segment 記錄所屬的 `scene_number`
+
+每個 segment 的 JSON 格式：
+```json
+{
+  "start": 12.5,
+  "end": 15.3,
+  "text": "Hello world",
+  "scene_number": 42
+}
+```
+
+`scene_number` 是在該 `file_uuid` 下的 CUT 場景序號（1-based）。
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+
+---
+
+## 資源預估
+
+| 資源 | 值 |
+|------|-----|
+| CPU | 1.0（一個完整核心） |
+| 記憶體 | 2048 MB（長片因分段處理，實際低於此值） |
+| GPU | 不使用（INT8 CPU 量化） |
+| 依賴 | 無 |
+
+---
+
+## Swift ASR (Apple Speech Framework) 實驗記錄
+
+### 選型結論
+
+使用現有做法（faster-whisper small），Swift ASR 不取代 Whisper。
+
+> **注意**：Apple Speech Framework 會隨著 macOS / Siri 版本更新而改善。每次主要 macOS 版本更新時（如 macOS 15→16），應重新執行 `scripts/compare_segmentation.py` 對比 Swift vs Whisper 的品質差異，以評估是否可切換。
+
+### POC 狀態
+
+Swift processor 位於 `scripts/swift_processors/`，已編譯。Apple Speech Framework 在記憶體（11MB vs 1.1GB）和速度（4.19s vs 17.46s）有優勢，但準確度不足。
+
+### 效能對比（Charade 60s 片段）
+
+| 指標 | Swift (Speech Framework) | Python (faster-whisper small) |
+|------|------------------------|-------------------------------|
+| **RTF** | 0.07 (14x) | 0.29 (3.4x) |
+| **記憶體** | 11MB | 1.1GB |
+| **Segments** | 18（句子級） | 23（句子級） |
+| **品質** | 漏字较多（"Let's see"→"And see"） | 準確 |
+| **語音分離改善** | Demucs +35s，僅小幅改善 | 不需要 |
+
+### 已知問題
+
+1. 語言自動偵測順序錯誤（先試 zh-TW），需指定 `--language en-US`
+2. RunLoop timeout 已修復（改為 semaphore 等待 callback）
+3. 逐字輸出已合併（94 → 18 segments）
+
+### 相關檔案
+
+```
+scripts/swift_processors/
+├── Package.swift
+├── asr_swift.swift
+├── asrx_swift.swift
+├── entitlements.plist
+└── .build/debug/asr_swift
+```
+
+---
+
+## Speaker Diarization (ASRX) 選型記錄
+
+### 現有方案：Python ASRX (ECAPA-TDNN + Spectral Clustering)
+
+使用 SpeechBrain ECAPA-TDNN 提取 192-D speaker embedding，搭配 spectral clustering 進行語者分離。
+
+| 指標 | 值 |
+|------|-----|
+| Embedding 維度 | 192-D |
+| Charade 偵測 speaker 數 | 10（正確區分 narrator、主角、配角） |
+| 總 ASRX pre_chunks | 5,848 |
+| Qdrant collection | `{prefix}_voice` |
+| 依賴 | 需 ASR 完成後執行（時間對齊） |
+| 輸出 | segments 含 `speaker_id`, `start_time`, `end_time` |
+
+### Swift SFSpeechAnalyzer 評估
+
+**目標**：使用 Apple 內建 Speech Framework（ANE 加速）取代 Python ASRX。
+
+| API | macOS 14 可用性 | 說明 |
+|-----|----------------|------|
+| `SFSpeechRecognizer` | ✅ | 語音辨識 |
+| `SFSpeechAnalyzer` | ✅ 存在 | 語音分析，但無暴露 speaker embedding |
+| `SFSpeechRecognitionMetadata` | ✅ 存在 | 辨識中繼資料，但 speaker 資訊為空 |
+| `SFSpeakerEmbedding` | ❌ | Speaker embedding API 不存在 |
+| `SFSpeakerIdentification` | ❌ | Speaker 識別 API 不存在 |
+| KVC 取 speaker metadata | ❌ | 透過 KVC 也無法取得 speaker 資訊 |
+
+**結論：目前不可行。** Apple 尚未在 macOS 14 上開放 Speaker Recognition API 給開發者使用。
+
+### 選型結論
+
+維持 Python ASRX (ECAPA-TDNN) 方案。待未來 macOS 版本開放 Speaker Recognition API 後重新評估。
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+| V1.1 | 2026-05-04 | 新增 Swift ASR 實驗記錄與 Speaker Diarization 選型記錄 | OpenCode | deepseek-chat |
+| V1.2 | 2026-05-04 | 新增 Text Embedding ANE 加速可行性研究 | OpenCode | deepseek-chat |
+
+---
+
+## Text Embedding ANE 加速研究
+
+### 背景
+
+ASR 產出的 sentence chunk 需要 embedding（用於 semantic search / RAG）。  
+目前使用 Ollama `nomic-embed-text-v2-moe`（768-D, 多語言，MIT license，CPU/GPU）。
+
+### 研究目標
+
+評估是否可用 Apple ANE 方案取代 Ollama embedding，降低 CPU 負載。
+
+### 選項評估
+
+| 方案 | 模型 | Dimension | 多語言 | ANE | 狀態 |
+|------|------|-----------|--------|-----|------|
+| **Apple NLEmbedding (sentence)** | 系統內建 | 未知 | ✅ 宣稱支援 | ✅ 原生 ANE | ❌ macOS 26.4.1 無模型檔 |
+| **Apple NLEmbedding (word)** | GloVe | 300D | ❌ 僅英文 | ✅ | ❌ dim 不足，無多語言 |
+| **Apple NLContextualEmbedding** | Transformer | 未知 | 未知 | ✅ | ❌ API 不可用 |
+| **CoreML custom (MiniLM)** | BERT-based | 384D | ✅ 50+ languages | ✅ | ❌ torch.jit.trace 失敗 |
+| **Ollama nomic-embed-text** | nomic-ai | 768D | ✅ 多語言 | ❌ | ✅ 現行方案 |
+
+### 測試結論 (2026-05-04)
+
+1. **NLEmbedding default**: dim=0, 所有 vector 回傳 nil。macOS 26.4.1 未預裝 sentence embedding 模型。
+2. **NLEmbedding word (GloVe)**: dim=300, 僅英文。法文/中文 dim=0（不支援）。
+3. **NLContextualEmbedding**: API compile error，方法不存在於公開 header。
+4. **CoreML 自轉 MiniLM**: `torch.jit.trace` 對 BERT 架構拋出 `Placeholder storage not allocated on MPS` 及 `dictconstruct` op 未支援。
+5. **Ollama nomic-embed**: 效能 ~6M embeddings/sec，768D 多語言，已整合穩定。
+
+### 建議
+
+維持 Ollama `nomic-embed-text-v2-moe`。  
+ANE text embedding 待以下條件成熟後重新評估：
+- Apple 開放 NLEmbedding 多語言 sentence 模型下載
+- 或 coremltools 支援 BERT `dictconstruct` op
+- 或 Apple 發布預訓練 CoreML 多語言 embedding 模型
@@ -0,0 +1,80 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "Caption Processor V1.0.0"
+date: "2026-05-02"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+parent: "PROCESSOR_SELECTION_V1.0.0.md"
+tags:
+  - "momentry"
+  - "core"
+  - "processor"
+  - "caption"
+  - "moondream2"
+  - "image-captioning"
+  - "v1.0.0"
+ai_query_hints:
+  - "Caption 使用 Moondream2 進行本地圖像描述生成"
+  - "Caption 已從 GPT-4o 雲端 API 本地化為 Moondream2"
+  - "Caption Moondream2 模型約 1.8GB，完全本地執行"
+  - "Caption 處理速度約 5s/frame"
+  - "Caption 備援方案為 YOLO + OCR + Scene 串接"
+related_documents:
+  - "PROCESSOR_SELECTION_V1.0.0.md"
+  - "../SCENE_V1.0.0.md"
+  - "../STORY_V1.0.0.md"
+  - "../YOLO_V1.0.0.md"
+  - "../OCR_V1.0.0.md"
+---
+
+# Caption Processor V1.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-02 |
+| 文件版本 | V1.0 |
+
+**狀態**: ✅ 100% | **模型**: Moondream2 | **GPU**: 否
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| Caption | 圖像描述生成，為每個場景產出文字敘述 |
+| Moondream2 | HuggingFace transformers 提供的本地圖像描述模型 |
+| GPT-4o | （已移除）先前使用的雲端 API 方案 |
+| local deployment | 完全本地執行，不依賴任何雲端 API |
+| fallback | 備援方案：YOLO + OCR + Scene 結果串接 |
+
+---
+
+## 選型過程
+
+| 指標 | GPT-4o（已移除） | Moondream2（新） |
+|------|-----------------|-----------------|
+| 速度 | 2s/frame | 5s/frame |
+| 品質 | 高 | 良好 |
+| 依賴 | ✅ 雲端 API Key | ❌ 完全本地 |
+
+**決策**: 已從 GPT-4o 雲端 API 本地化為 Moondream2（HuggingFace transformers, ~1.8GB）。備援方案為 YOLO + OCR + Scene 結果串接。
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+
+## 資源預估
+
+| 資源 | 值 |
+|------|-----|
+| CPU | - |
+| 記憶體 | ~1.8 GB（模型載入後） |
+| GPU | 不使用 |
+| 依賴 | Scene |
@@ -0,0 +1,179 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "CUT Processor (Scene Cut Detection) V1.0.0"
+date: "2026-05-03"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+parent: "PROCESSOR_SELECTION_V1.0.0.md"
+tags:
+  - "momentry"
+  - "core"
+  - "processor"
+  - "cut"
+  - "scene-detection"
+  - "pyscenedetect"
+  - "v1.0.0"
+ai_query_hints:
+  - "CUT 場景檢測的輸出結構與檔案後綴規則"
+  - "CUT 的 cut_count 與 cut_max_duration 用途"
+  - "長影片動態調度如何將 Face 移到 ASR 前"
+  - "CUT 與 Scene 的執行階段（register 同步）"
+  - "CUT 輸出 JSON 結構（start_time/end_time）"
+related_documents:
+  - "PROCESSORS/SCENE_V1.0.0.md"
+  - "PROCESSOR_SELECTION_V1.0.0.md"
+  - "PROCESSORS/ASR_V1.0.0.md"
+  - "PROCESSORS/FACE_V1.0.0.md"
+  - "CHUNK_DEFINITION_V1.0.0.md"
+---
+
+# CUT Processor (Scene Cut Detection) V1.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-03 |
+| 文件版本 | V1.0 |
+
+**狀態**: ✅ 100% | **模型**: PySceneDetect (ContentDetector) | **GPU**: 否
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| CUT | 場景切換檢測，使用 PySceneDetect ContentDetector |
+| scene boundary | 場景邊界，以 start_time/end_time 定義 |
+| cut_count | 場景數量，register 階段寫入 DB |
+| cut_max_duration | 最長場景秒數，用於長影片動態調度 |
+| ContentDetector | 基於幀差異的場景切換檢測演算法 |
+
+---
+
+## 選型過程
+
+無 ML 模型，基於幀差異的場景切換檢測。門檻值 threshold=27 為實驗最佳值。
+
+---
+
+## 輸出結構
+
+CUT 產出 `{file_uuid}.cut.json`，結構如下：
+
+```json
+{
+  "scenes": [
+    { "start_time": 0.0, "end_time": 120.5 },
+    { "start_time": 120.5, "end_time": 245.0 }
+  ]
+}
+```
+
+---
+
+## 執行階段
+
+CUT 在 **register 階段同步執行**（`register_single_file`），不做 worker pipeline 排程。完成後寫入 DB 欄位：
+- `cut_done: bool` — 是否完成
+- `cut_count: i32` — 場景數量
+- `cut_max_duration: f64` — 最長場景秒數
+
+---
+
+## 狀態後綴
+
+| 後綴 | 意義 | 行為 |
+|------|------|------|
+| `.cut.json` | 完成 | 直接載入使用 |
+| `.cut.json.tmp` | 執行中 | 跳過、等待 |
+| `.cut.json.err` | 失敗 | 跳過、不重試 |
+
+---
+
+## 長影片動態調度
+
+當 `cut_count ≤ 3 && cut_max_duration > 600s`（如會議紀錄長鏡頭），Worker 自動調整 pipeline 順序：
+- **Face 移到 ASR 前面**，先用 face detection 找出人物進出點
+- 後續可用 face 分佈切分長 scene，輔助 ASR 分段
+
+---
+
+## 效能實測
+
+**ExaSAN 159.6s 影片**:
+| 指標 | 值 |
+|------|-----|
+| 處理時間 | 0.08s |
+| 即時倍率 | 2036.5x（最快的 processor） |
+| 輸出 | 52 bytes |
+
+**Charade 長片（6879s, 412343 幀）**:
+| 指標 | 值 |
+|------|-----|
+| 場景數 | 1331 |
+| 輸出 | 217 KB |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-03 | 初始版本 | OpenCode | deepseek-chat |
+
+---
+
+## 資源預估
+
+| 資源 | 值 |
+|------|-----|
+| CPU | 0.5 |
+| 記憶體 | 512 MB |
+| GPU | 不使用 |
+
+---
+
+## Swift AVFoundation 替代評估
+
+### POC 目標
+
+使用 AVFoundation 逐幀 histogram 分析取代 Python PySceneDetect（ContentDetector），目標利用 ANE 加速。
+
+### 測試結果（Charade 60s clip, 3597 frames, 59.9fps）
+
+| 指標 | Python PySceneDetect | Swift AVFoundation (luminance histogram) |
+|------|---------------------|------------------------------------------|
+| **Scenes 偵測** | **3** ✅ 合理 | **63** ❌ 過度敏感 |
+| **處理時間** | **7.93s** | 15.42s |
+| **RTF** | **0.132** (7.6x) | 0.257 (3.9x) |
+| **記憶體** | ~512MB | 極低（系統框架） |
+| **演算法** | ContentDetector（adaptive threshold + frame normalization） | 單純 histogram diff（64 bins luminance） |
+
+### 問題分析
+
+1. **準確度** — 63 vs 3 scenes。簡單的 luminance histogram diff 對 camera movement、lighting change 過度敏感。PySceneDetect 的 ContentDetector 使用 adaptive threshold + 幀正規化，穩定性高很多。
+2. **速度** — 15.42s vs 7.93s。AVAssetReader 必須 sequential decode 所有 frames，無法像 ffmpeg 那樣 efficient frame skipping。
+
+### 選型結論
+
+| 項目 | 方案 |
+|------|------|
+| **Scene Cut Detection** | Python PySceneDetect **維持現狀** |
+
+### 相關檔案
+
+```
+scripts/swift_processors/swift_cut_test.swift
+```
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-03 | 初始版本 | OpenCode | deepseek-chat |
+| V1.1 | 2026-05-04 | 新增 Swift AVFoundation 替代評估記錄 | OpenCode | deepseek-chat |
+| 依賴 | 無 |
@@ -0,0 +1,159 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "Face Embedding 產出流程 V2.0.0"
+date: "2026-05-04"
+version: "V2.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+tags:
+  - "momentry"
+  - "core"
+  - "face"
+  - "embedding"
+  - "pgvector"
+  - "qdrant"
+  - "v2.0.0"
+ai_query_hints:
+  - "Face Embedding 的完整處理流程（Vision detection → CoreML FaceNet → pgvector + Qdrant）"
+  - "V2.0 使用 Apple Vision Framework 取代 InsightFace detection"
+  - "V2.0 使用 CoreML FaceNet (MIT) 產出 512-D embedding"
+  - "Face processor 的輸出結構與 embedding 欄位說明"
+  - "Qdrant face collection 的 payload 結構與點位 ID 規則"
+  - "Face embedding 使用 Cosine 距離計算"
+  - "Face detection 使用 ANE（Apple Vision Framework），embedding 使用 ANE（CoreML FaceNet）"
+  - "face_detections 表與 Qdrant 的資料同步方式"
+related_documents:
+  - "../VECTOR_SPEC_V1.0.0.md"
+  - "../PROCESSORS/FACE_V1.0.0.md"
+  - "../PROCESSOR_SELECTION_V1.0.0.md"
+  - "../CHUNK_DEFINITION_V1.0.0.md"
+  - "../MOMENTRY_CORE_API_V1.0.0.md"
+---
+
+# Face Embedding 產出流程 V2.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-04 |
+| 文件版本 | V2.0 |
+
+## V2.0 變更摘要
+
+| 項目 | V1.x | V2.0 |
+|------|------|------|
+| **Detection** | InsightFace SCRFD-10G (CPU, 450%) | **Apple Vision VNDetectFaceRectangles** (ANE, ~0%) |
+| **Pose** | InsightFace 2D landmarks → angle | **Apple Vision VNDetectFaceLandmarks** (roll/yaw/pitch) |
+| **Embedding** | CoreML FaceNet 512-D (ANE) | 同左，MIT license |
+| **CPU usage** | 450%+ | **~0%** |
+| **Script** | `face_processor.py` | **`face_processor_vision.py` + `swift_face`** |
+
+## 處理流程
+
+```
+1. swift_face (Vision/ANE)
+   ├── AVAssetReader 逐幀讀取
+   ├── VNDetectFaceRectanglesRequest  → bbox (x, y, w, h) + confidence
+   ├── VNDetectFaceLandmarksRequest   → roll, yaw, pitch
+   └── 輸出: {uuid}_detect.json
+
+2. face_processor_vision.py
+   ├── 讀取 detect.json
+   ├── cv2 逐幀 crop face by bbox
+   ├── CoreML FaceNet → 512-D embedding (ANE)
+   ├── classify_pose(roll, yaw) → frontal/three_quarter/profile
+   └── 輸出: {uuid}.face.json (FaceResult format)
+
+3. Rust pipeline (job_worker.rs)
+   ├── 讀取 face.json → FaceResult struct
+   ├── store_face_chunks() → pre_chunks table
+   └── store_face_embeddings_to_qdrant() → Qdrant
+
+4. Post-Face (job_worker.rs)
+   ├── store_traced_faces.py
+   │   ├── face_tracker.py (IoU + embedding) → trace_id
+   │   └── INSERT face_detections (trace_id + bbox + embedding pgvector)
+   ├── sync_face_embeddings() → Qdrant face points
+   └── cluster_face_embeddings() / search_similar_faces() → pgvector query
+```
+
+## 輸出結構
+
+### face.json (FaceResult)
+
+```json
+{
+  "frame_count": 6872,
+  "fps": 59.94,
+  "frames": [
+    {
+      "frame": 30,
+      "timestamp": 0.5,
+      "faces": [
+        {
+          "x": 917, "y": 125, "width": 181, "height": 250,
+          "confidence": 0.88,
+          "embedding": [0.01, -0.04, 0.12, ...],  // 512-D
+          "pose_angle": {"angle": "frontal", "roll": 2.5, "yaw": -5.0, "pitch": 1.2},
+          "landmarks": null,
+          "attributes": null
+        }
+      ]
+    }
+  ]
+}
+```
+
+### face_detections (PostgreSQL + pgvector)
+
+| 欄位 | 型別 | 說明 |
+|------|------|------|
+| `file_uuid` | VARCHAR | 來源影片 |
+| `frame_number` | BIGINT | 幀編號 |
+| `trace_id` | INTEGER | 跨幀追蹤 ID（face_tracker 分配） |
+| `bbox` | JSONB | `{"x", "y", "width", "height"}` |
+| `confidence` | DOUBLE | 檢測信心度 |
+| `embedding` | VECTOR(512) | pgvector index (ivfflat, cosine) |
+| `identity_id` | BIGINT | 綁定的 identity（可為 NULL） |
+
+### Qdrant Payload (momentry_dev/dev collection)
+
+```json
+{
+  "file_uuid": "1a04db97...",
+  "trace_id": 0,
+  "frame_number": 825,
+  "type": "face_embedding"
+}
+```
+
+## Vector 規格
+
+| 屬性 | 值 |
+|------|-----|
+| 模型 | CoreML FaceNet (InceptionResnetV1, VGGFace2) |
+| License | MIT |
+| 維度 | 512 |
+| 距離 | Cosine |
+| Index | pgvector ivfflat (lists=100) |
+| Qdrant | Cosine distance, shared collection |
+
+## 來源 Processor 資源預估
+
+| 資源 | V1.x (InsightFace) | V2.0 (Vision + FaceNet) |
+|------|--------------------|-------------------------|
+| Detection 模型 | IntegrationFace SCRFD-10G (~150MB) | Apple Vision (系統內建) |
+| Embedding 模型 | CoreML FaceNet (90MB) | 同左 |
+| CPU | 450%+ | **~0%** |
+| 記憶體 | ~1.5GB | **<50MB** |
+| ANE | 僅 embedding | **detection + embedding** |
+| Total time (2hr film, interval=30) | ~1.3hr | **~40min** |
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 (InsightFace) | OpenCode | deepseek-chat |
+| V2.0 | 2026-05-04 | Apple Vision detection + CoreML FaceNet embedding | OpenCode | deepseek-chat |
@@ -0,0 +1,373 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "Face Processor V1.0.0"
+date: "2026-05-02"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+parent: "PROCESSOR_SELECTION_V1.0.0.md"
+tags:
+  - "momentry"
+  - "core"
+  - "processor"
+  - "face"
+  - "insightface"
+  - "face-detection"
+  - "v1.0.0"
+ai_query_hints:
+  - "Face 使用 InsightFace buffalo_l 進行人臉偵測與辨識"
+  - "Face 在 ExaSAN 159.6s 影片上僅需 1.22s，即時倍率 130.5x"
+  - "Face 支援 GPU 加速，CoreML 可達 50~80 FPS"
+  - "Face 輸出 512-D embedding 用於比對"
+  - "Face 不再使用 Haar Cascade fallback，強制使用 InsightFace"
+related_documents:
+  - "PROCESSOR_SELECTION_V1.0.0.md"
+  - "../FACE_EMBEDDING_FLOW_V1.0.0.md"
+  - "../CUT_V1.0.0.md"
+  - "../VECTOR_SPEC_V1.0.0.md"
+  - "../CHUNK_DEFINITION_V1.0.0.md"
+---
+
+# Face Processor V1.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-02 |
+| 文件版本 | V1.0 |
+
+**狀態**: ✅ 100% | **模型**: InsightFace buffalo_l | **GPU**: 是
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| Face Detection | 人臉偵測，使用 InsightFace SCRFD-10G |
+| Face Recognition | 人臉辨識，使用 ArcFace w600k_r50 產出 512-D embedding |
+| embedding | 向量嵌入，用於人臉比對與搜尋 |
+| CoreML | Apple Silicon 上的 GPU 加速方案 |
+| LFW | Labeled Faces in the Wild，人臉辨識基準資料集 |
+
+---
+
+## 選型過程
+
+| 模型 | 類型 | 大小 | 檢測率 | 辨識率 | Embedding |
+|------|------|------|--------|--------|-----------|
+| **InsightFace Buffalo_l** | **完整套件** | **~150MB** | **97.3% mAP** | **99.77% (LFW)** | **512-D ✅** |
+| MediaPipe BlazeFace | 輕量檢測 | 1~2MB | 95.2% mAP | 無 | ❌ |
+| OpenCV Haar Cascade | 傳統 ML | 900KB | 70~85% | 無 | ❌ |
+
+**關鍵決策**: 舊版 Haar Cascade fallback 會產生全鏈路失敗（0 embeddings），已改為強制使用 InsightFace。
+
+---
+
+## 效能實測（ExaSAN 159.6s 影片）
+
+| 指標 | 值 |
+|------|-----|
+| 處理時間 | 1.22s |
+| 即時倍率 | 130.5x |
+| 輸出 | 49 frames, 67 faces |
+
+---
+
+## GPU 加速
+
+| 平台 | FPS |
+|------|-----|
+| CoreML (Apple Silicon) | 50~80 FPS |
+| CUDA (NVIDIA) | 80~120 FPS |
+| CPU | 15~20 FPS |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+
+---
+
+## 資源預估
+
+| 資源 | 值 |
+|------|-----|
+| CPU | 0.6 |
+| 記憶體 | 1536 MB |
+| GPU | 支援（`uses_gpu = true`） |
+| 依賴 | 無 |
+
+---
+
+## Apple Vision Framework 實驗記錄
+
+### POC 目標
+
+評估 Apple Vision Framework 是否可取代 InsightFace（buffalo_l）進行臉部處理，目標是利用 ANE 加速降低記憶體使用。
+
+### 測試結果
+
+測試環境：macOS 14, Apple Silicon M4, 使用 `VNDetectFaceRectanglesRequest` + `VNDetectFaceLandmarksRequest` + `VNDetectFaceCaptureQualityRequest`。
+
+| 功能 | Vision Framework | InsightFace (buffalo_l) |
+|------|----------------|------------------------|
+| **Face Detection** | ✅ 通過（1 face, conf=0.88） | ✅ |
+| **Face Landmarks** | ✅ 6+6 eye pts, 8 nose pts | ✅ 106 pts |
+| **Capture Quality** | ✅ score=0.5327 | ❌ 無 |
+| **Face Embedding (512-D)** | ❌ **不可用** | ✅ ArcFace 512-D |
+| **照片 metadata（年齡/性別）** | ❌ 不可用 | ✅ |
+| **ANE 加速** | ✅ 是 | ❌ CPU only |
+| **處理時間** | ⚡ 0.31s | ~0.5-1s |
+| **記憶體** | ✅ 低（系統框架） | ~1.5GB |
+
+### 關鍵發現
+
+`VNFaceprint` class 存在但無法透過公開 API 或 KVC 取得 face embedding 資料。Vision Framework 提供了高品質的臉部偵測和特徵點定位，但**無法提取用於 face matching 的向量 embedding**。
+
+### 選型結論
+
+| 用途 | 方案 |
+|------|------|
+| **Face Detection** | Vision Framework **可取代** InsightFace（更輕量、更快） |
+| **Face Landmarks** | Vision Framework **可取代** |
+| **Face Embedding** | InsightFace **維持現狀**（Vision Framework 無法取代） |
+| **Face Recognition** | InsightFace **維持現狀** |
+
+若未來 Apple 開放 `VNFaceprint` 的 embedding 資料，可重新評估全面切換。
+
+### 相關檔案
+
+```
+scripts/swift_processors/face_vision_test.swift
+```
+
+---
+
+## MediaPipe Face 評估
+
+### 測試狀態
+
+MediaPipe 0.10.33 已安裝，提供 Face Detection (BlazeFace) + Face Landmarker (468 mesh)。
+
+| 功能 | API | 狀態 |
+|------|-----|------|
+| Face Detection | `mediapipe.tasks.python.vision.face_detector` | ✅ 可用 |
+| Face Mesh | `mediapipe.tasks.python.vision.face_landmarker` | ✅ 468 3D landmarks |
+| Face Embedding | 無 | ❌ 不支援 |
+
+### 三方案比較
+
+| 功能 | MediaPipe | Vision Framework | InsightFace |
+|------|-----------|-----------------|-------------|
+| **Face Detection** | ✅ BlazeFace (~2MB) | ✅ VNDetectFaceRectangles | ✅ RetinaFace |
+| **Bounding Box** | ✅ | ✅ | ✅ |
+| **Keypoints** | ✅ **6 點** (eyes+nose+mouth) | ❌ | ✅ 106 點 |
+| **Face Mesh** | ✅ **468 點** (獨立模型) | ❌ | ❌ |
+| **512-D Embedding** | ❌ | ❌ | ✅ **ArcFace** |
+| **Age/Gender** | ❌ | ❌ | ✅ |
+| **Capture Quality** | ❌ | ✅ score 0.06~0.25 | ❌ |
+| **速度** | ⚡ 極快 (mobile optimized) | ⚡ ANE 加速 | 🐢 CPU bound |
+| **模型大小** | ~2MB | 系統內建 | ~150MB |
+| **跨平台** | ✅ Linux/Windows/macOS | ❌ Apple only | ✅ |
+
+### 選型結論
+
+| 用途 | 建議方案 |
+|------|---------|
+| **Face Detection** | MediaPipe 或 Vision Framework（速度快、輕量） |
+| **Face Mesh / 468 landmarks** | MediaPipe（唯一方案） |
+| **Face Embedding (512-D)** | InsightFace **維持現狀** |
+| **Age/Gender** | InsightFace **維持現狀** |
+
+MediaPipe 和 Vision Framework 在 detection 層級相當，兩者都遠快於 InsightFace。但最終 embedding extraction 仍需 InsightFace。
+
+### 分段實施建議
+
+若要以 Swift/Vision 加速 face pipeline：
+
+```
+Swift face_detector (ANE, fast)
+  └── 輸出 {file_uuid}.bbox.json (face_id, bbox, timestamp)
+
+Python embed_extractor (InsightFace, only on detected crops)
+  └── 讀取 .bbox.json → crop face region
+    → InsightFace 提取 512-D embedding
+    → 產出完整 {file_uuid}.face.json
+```
+
+---
+
+## FaceNet-PyTorch CoreML Embedding 實驗
+
+### 動機
+
+InsightFace 的 buffalo_l pre-trained weights 使用 CC BY-NC-SA 4.0 license，商用有爭議。需要一個 MIT/Apache 2.0 licensed 的 face embedding 方案。
+
+### 測試結果
+
+使用 Facenet-PyTorch (`facenet-pytorch`, MIT license) 的 InceptionResnetV1 (pretrained on VGGFace2)，匯出 ONNX 並轉換為 CoreML。
+
+| 步驟 | 時間 | 產出 |
+|------|------|------|
+| 模型載入 | 10.5s | InceptionResnetV1, 512-D output |
+| ONNX 匯出 | 1.2s | `/tmp/facenet512.onnx` (90MB) |
+| CoreML 轉換 | 6s | `/tmp/facenet512.mlpackage` (90MB) |
+
+### 效能對比
+
+| 指標 | PyTorch (CPU) | CoreML (CPU/GPU/ANE) |
+|------|--------------|---------------------|
+| **推論時間 (avg)** | 30.9ms | **4.8ms** ⚡ |
+| **加速比** | 1x | **6.4x** |
+| **Embedding 維度** | 512-D | 512-D |
+| **Normalized** | ✅ norm=1.0 | ✅ norm=1.0 |
+| **精度比對 (cosine)** | 1.0 | **0.999532** ✅ |
+
+### License 確認
+
+| 元件 | License | 商用 |
+|------|---------|------|
+| Facenet-PyTorch 原始碼 | **MIT** | ✅ |
+| VGGFace2 weights | 研究用，但可重新訓練 | ✅ (自有資料訓練後) |
+| ONNX Runtime | MIT | ✅ |
+| CoreML | macOS 內建 | ✅ |
+| InsightFace buffalo_l (現行) | CC BY-NC-SA 4.0 | ❌ **有爭議** |
+
+### 結論
+
+Facenet-PyTorch CoreML 模型可完全取代 InsightFace 的 embedding extraction，MIT license 無商用障礙，且 CoreML 推論快 6.4 倍。
+
+### 整合入 Face Processor
+
+`scripts/face_processor.py` 已整合 CoreML FaceNet 作為 embedding extractor：
+
+| 項目 | 實作 |
+|------|------|
+| **Detection** | InsightFace buffalo_l（維持不變） |
+| **Embedding** | CoreML FaceNet（`models/facenet512.mlpackage`）✅ 已取代 |
+| **Fallback** | CoreML 失敗時自動回退到 InsightFace embedding |
+| **啟動載入** | script 初始化時一次載入 CoreML model（~2s） |
+| **推論流程** | 對每個 detected face crop → resize 160x160 → normalize → CoreML infer → 512-D embedding |
+| **Metadata** | 輸出記錄 `embedding_method: coreml_facenet` |
+
+Model 檔案路徑：`models/facenet512.mlpackage`（專案根目錄）
+
+### 相關檔案
+
+```
+models/facenet512.mlpackage   # CoreML model (90MB, MIT license)
+/tmp/facenet512.onnx          # ONNX format (90MB, for reference)
+scripts/face_processor.py     # Face processor with CoreML integration
+```
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+| V1.1 | 2026-05-04 | 新增 Apple Vision Framework + MediaPipe + FaceNet CoreML 整合記錄 | OpenCode | deepseek-chat |
+| V2.0 | 2026-05-04 | Apple Vision 取代 InsightFace detection；CoreML FaceNet 維持 embedding | OpenCode | deepseek-chat |
+
+---
+
+## V2.0 Architecture: Vision Detection + CoreML FaceNet Embedding
+
+### 架構變更
+
+V1.x 使用 InsightFace 同時做 detection + embedding（CPU bound, 450%+ CPU）。  
+V2.0 將 detection 移至 Apple Vision Framework（ANE），embedding 維持 CoreML FaceNet（ANE），CPU 歸零。
+
+```
+V1.x:
+  face_processor.py
+    ├── InsightFace buffalo_l (CPU, 450%)  → detection + bbox + landmarks
+    └── CoreML FaceNet (ANE)               → 512-D embedding
+
+V2.0:
+  face_processor_vision.py
+    ├── swift_face (Vision/ANE)  → VNDetectFaceRectanglesRequest  → bbox
+    │                              → VNDetectFaceLandmarksRequest  → pose (roll, yaw, pitch)
+    └── CoreML FaceNet (ANE)     → 512-D embedding on cropped face
+```
+
+### 處理流程
+
+```
+1. swift_face <video> <output_detect.json> --sample-interval 30
+   ├── AVAssetReader 逐幀讀取
+   ├── VNDetectFaceRectanglesRequest  → bbox (x, y, w, h) + confidence
+   ├── VNDetectFaceLandmarksRequest   → roll, yaw, pitch + 76-point mesh
+   └── 每幀輸出: {"frame": N, "timestamp": S, "faces": [{bbox, confidence, pose}]}
+
+2. Python 讀取 detect.json，逐幀:
+   ├── cv2 seek to frame → crop face by bbox
+   ├── resize 160x160 → normalize [-1,1]
+   └── CoreML FaceNet predict → 512-D embedding
+
+3. 組裝 face.json (FaceResult format):
+   ├── frame_count, fps
+   └── frames: [{frame, timestamp, faces: [{x,y,w,h, embedding, pose_angle}]}]
+```
+
+### 效能對比
+
+| 指標 | V1.x (InsightFace) | V2.0 (Vision + FaceNet) |
+|------|--------------------|-------------------------|
+| Detection CPU | 450%+ | **~0%** (ANE) |
+| Embedding CPU | ~5% | **~0%** (ANE) |
+| 記憶體 | ~1.5GB | **<50MB** |
+| Detection 精度 | SCRFD-10G, 97.3% mAP | Vision, ~95% |
+| Embedding | CoreML FaceNet 512-D (6.4x) | 同左 |
+| 總處理時間 (2hr film) | ~1.3hr | **~40min** (sample=30) |
+
+### Pose Angle 分類
+
+swift_face 從 Vision landmarks 提取 roll/yaw/pitch，Python 端分類：
+
+| roll/yaw 範圍 | Pose Angle |
+|---------------|------------|
+| \|yaw\|<15, \|roll\|<15 | frontal |
+| yaw > 30 | profile_right |
+| yaw < -30 | profile_left |
+| 其他 | three_quarter |
+
+### 損壞幀處理 (2026-05-04)
+
+部分影片來源（如從網路下載的老電影）包含損壞的 h264 GOP，解碼時會產生異常尺寸的 CVPixelBuffer（如 250×250 而非 1920×1080），導致 Vision detection crash。
+
+**修復**：swift_face 以 `do/catch` 包裹 `VNImageRequestHandler.perform()`，異常幀 skip 並記錄到 stderr：
+```
+[SwiftFace] Skipping corrupted frame 288660
+```
+
+已知損壞幀：Charade (1963) frame 288,660。
+
+### 相關檔案
+
+```
+scripts/swift_processors/swift_face.swift       # Vision detection (ANE), 損壞幀 skip
+scripts/face_processor_vision.py                # V2.0 processor (Vision + CoreML)
+scripts/face_processor.py                       # V1.x (InsightFace, deprecated) — now V2.0
+scripts/store_traced_faces.py                   # Post-process: trace + DB store
+scripts/utils/face_tracker.py                   # IoU + embedding cross-frame tracker
+models/facenet512.mlpackage                     # CoreML FaceNet (MIT)
+src/core/processor/face.rs                      # Rust FaceResult struct
+src/worker/job_worker.rs                        # Pipeline trigger (trace store + Qdrant)
+src/core/db/postgres_db.rs                      # cluster_face_embeddings(), search_similar_faces()
+src/core/db/qdrant_db.rs                        # sync_face_embeddings(), upsert_face_embedding()
+migrations/029_add_trace_id_to_face_detections.sql  # trace_id column
+migrations/030_create_tkg_graph_tables.sql      # TKG nodes/edges
+```
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+| V1.1 | 2026-05-04 | 新增 Apple Vision Framework + MediaPipe + FaceNet CoreML 整合記錄 | OpenCode | deepseek-chat |
+| V2.0 | 2026-05-04 | Apple Vision 取代 InsightFace detection；CoreML FaceNet 維持 embedding | OpenCode | deepseek-chat |
+| V2.1 | 2026-05-04 | 損壞幀 skip 處理；已知 Charade frame 288,660 異常 | OpenCode | deepseek-chat |
@@ -0,0 +1,125 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "OCR Processor V1.0.0"
+date: "2026-05-02"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+parent: "PROCESSOR_SELECTION_V1.0.0.md"
+tags:
+  - "momentry"
+  - "core"
+  - "processor"
+  - "ocr"
+  - "paddleocr"
+  - "optical-character-recognition"
+  - "v1.0.0"
+ai_query_hints:
+  - "OCR 使用 PaddleOCR PP-OCRv4 模型支援 80+ 語言"
+  - "OCR 處理 159.6s 影片全幀約 36.87s，即時倍率 4.3x"
+  - "OCR 輸出 102 frames, 234 texts, 65KB"
+  - "OCR 不使用 GPU，CPU 使用率 0.8"
+  - "OCR 精度 > 95%，支援繁體中文"
+related_documents:
+  - "PROCESSOR_SELECTION_V1.0.0.md"
+  - "../YOLO_V1.0.0.md"
+  - "../CAPTION_V1.0.0.md"
+  - "../VISUAL_CHUNK_V1.0.0.md"
+  - "../CHUNK_DEFINITION_V1.0.0.md"
+---
+
+# OCR Processor V1.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-02 |
+| 文件版本 | V1.0 |
+
+**狀態**: ✅ 100% | **模型**: PaddleOCR PP-OCRv4 | **GPU**: 否
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| OCR | Optical Character Recognition，光學字元辨識 |
+| PaddleOCR | 百度開發的 OCR 引擎，PP-OCRv4 為最新版本 |
+| PP-OCRv4 | PaddleOCR 第四代模型，支援 80+ 語言 |
+| real-time factor | 即時倍率，處理時間與影片時長的比值 |
+| full-frame processing | 全幀處理模式，對影片每一幀進行 OCR |
+
+---
+
+## 選型過程
+
+選擇 PaddleOCR 原因：
+- 支援 80+ 語言（含繁體中文）
+- 精度 > 95%
+- EasyOCR 經測試不如 PaddleOCR
+
+---
+
+## 效能實測（ExaSAN 159.6s 影片, 全幀處理）
+
+| 指標 | 值 |
+|------|-----|
+| 處理時間 | 36.87s |
+| 即時倍率 | 4.3x |
+| 輸出 | 102 frames, 234 texts, 65KB |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+
+## 資源預估
+
+| 資源 | 值 |
+|------|-----|
+| CPU | 0.8 |
+| 記憶體 | 1024 MB |
+| GPU | 不使用 |
+| 依賴 | 無 |
+
+---
+
+## Apple Vision Framework 替代實作
+
+### POC 結果
+
+| 指標 | Python PaddleOCR (PP-OCRv4) | Swift Vision (VNRecognizeTextRequest) |
+|------|----------------------------|---------------------------------------|
+| **文字偵測** | 多筆低品質 ("1", "48219 %,") | **9 blocks, conf=1.0~0.3** ("A08S2-TS", "4101") |
+| **速度/幀** | 慢（batch 處理） | **0.43s / 幀** (640x360) |
+| **記憶體** | ~1GB（PaddleOCR 模型） | **低**（系統框架） |
+| **語言** | 80+ | **30 種**（含 zh-Hans/Hant） |
+| **ANE 加速** | ❌ CPU only | ✅ **是** |
+| **逐幀處理** | 需要 batch 加速 | ✅ 獨立快速 |
+
+### 選型結論
+
+Vision Framework OCR 在速度、記憶體、準確度上均優於 PaddleOCR，且使用 ANE 加速。
+
+**決定**: 以 Swift Vision OCR 取代 Python PaddleOCR。
+
+### 實作
+
+`scripts/swift_processors/swift_ocr.swift` 為完整 OCR processor，支援：
+- 影片逐幀 / 取樣處理
+- JSON 輸出格式與 Python 版相容
+- 可透過 `ocr_processor.py` wrapper 被 PythonExecutor 呼叫
+- 自動語言偵測
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+| V1.1 | 2026-05-04 | 以 Apple Vision Framework 取代 PaddleOCR | OpenCode | deepseek-chat |
@@ -0,0 +1,133 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "Pose Processor V1.0.0"
+date: "2026-05-02"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+parent: "PROCESSOR_SELECTION_V1.0.0.md"
+tags:
+  - "momentry"
+  - "core"
+  - "processor"
+  - "pose"
+  - "mediapipe"
+  - "pose-estimation"
+  - "v1.0.0"
+ai_query_hints:
+  - "Pose 使用 MediaPipe Pose (pose_landmarker_heavy, 33 keypoints)"
+  - "Pose 處理 159.6s 影片全幀約 65.87s，即時倍率 2.4x"
+  - "Pose 輸出 1853 frames, 2341 persons, 603KB"
+  - "Pose 支援 GPU 加速（uses_gpu = true）"
+  - "Pose 與 YOLO 同為處理瓶頸之一"
+related_documents:
+  - "PROCESSOR_SELECTION_V1.0.0.md"
+  - "../YOLO_V1.0.0.md"
+  - "../FACE_V1.0.0.md"
+  - "../CUT_V1.0.0.md"
+  - "../CHUNK_DEFINITION_V1.0.0.md"
+---
+
+# Pose Processor V1.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-02 |
+| 文件版本 | V1.0 |
+
+**狀態**: ✅ 100% | **模型**: MediaPipe Pose | **GPU**: 是
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| Pose Estimation | 姿態估計，偵測人體關鍵點位置 |
+| MediaPipe | Google 開發的跨平台 ML 解決方案 |
+| keypoint | 關鍵點，pose_landmarker_heavy 輸出 33 個關鍵點 |
+| landmarker_heavy | MediaPipe 的精確模式，準確度最高但速度較慢 |
+| bottleneck | 處理瓶頸，Pose 與 YOLO 同為最耗時的 processor |
+
+---
+
+## 選型過程
+
+使用 MediaPipe Pose（pose_landmarker_heavy, 33 keypoints）。
+
+---
+
+## 效能實測（ExaSAN 159.6s 影片, 全幀處理）
+
+| 指標 | 值 |
+|------|-----|
+| 處理時間 | 65.87s |
+| 即時倍率 | 2.4x（瓶頸之一，與 YOLO 相當） |
+| 輸出 | 1853 frames, 2341 persons, 603KB |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+
+## 資源預估
+
+| 資源 | 值 |
+|------|-----|
+| CPU | 0.4 |
+| 記憶體 | 1024 MB |
+| GPU | 支援（`uses_gpu = true`） |
+| 依賴 | 無 |
+
+---
+
+## Apple Vision Framework 替代實作
+
+### POC 結果
+
+使用 `VNDetectHumanBodyPoseRequest`（ANE 加速）取代 MediaPipe/YOLOv8 Pose。
+
+測試影片：Thunderbolt ExaSAN at CCBN (24fps, sample_interval=90)
+
+| 指標 | YOLOv8 Pose (CPU) | Vision Framework (ANE) |
+|------|-------------------|----------------------|
+| **Per frame** | **45ms** | **9ms** ⚡ |
+| **加速比** | 1x | **5x** |
+| **Joints** | 17 keypoints (COCO) | **19 joints** |
+| **ANE 加速** | ❌ CPU only | ✅ **是** |
+| **記憶體** | ~1GB (PyTorch) | 極低（系統框架） |
+| **Joint 品質** | ✅ 標準 COCO | neck/shoulders 高 conf |
+
+### 選型結論
+
+Vision Framework body pose 在速度（5x）和資源使用上均優於 YOLOv8 Pose，且 ANE 加速不佔 CPU。
+
+**決定**: 以 Apple Vision Framework `VNDetectHumanBodyPoseRequest` 取代 YOLOv8 Pose。
+
+### 實作
+
+`scripts/swift_processors/swift_pose.swift` 為完整 Pose processor，支援：
+- 影片逐幀 / 取樣處理
+- 輸出格式相容於 Rust `PoseResult` struct
+- 可透過 `pose_processor.py` wrapper 被 PythonExecutor 呼叫
+- ANE 加速，19 joints（neck, shoulders, elbows, wrists, hips, knees, ankles, root, nose, eyes, ears）
+
+### 相關檔案
+
+```
+scripts/swift_processors/swift_pose.swift        # Vision Framework pose processor
+scripts/swift_processors/pose_benchmark.swift     # Benchmark test
+```
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+| V1.1 | 2026-05-04 | 以 Apple Vision Framework 取代 YOLOv8 Pose | OpenCode | deepseek-chat |
@@ -0,0 +1,95 @@
+---
+document_type: "processor-spec"
+service: "MOMENTRY_CORE"
+title: "Scene Processor (Scene Classification) V1.0.0"
+date: "2026-05-03"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+parent: "PROCESSOR_SELECTION_V1.0.0.md"
+tags:
+  - "momentry"
+  - "core"
+  - "processor"
+  - "scene"
+  - "places365"
+  - "scene-classification"
+  - "v1.0.0"
+ai_query_hints:
+  - "Scene 分類的模型選型與效能實測"
+  - "Scene 的執行階段與檔案後綴檢查規則"
+  - "Scene 與 CUT 的依賴關係（已移除 ASR）"
+  - "Scene 輸出為 pre_chunks 供 Rule 3 parent chunk 使用"
+  - "load_scene_from_file 直接載入 JSON 不入庫"
+related_documents:
+  - "PROCESSORS/CUT_V1.0.0.md"
+  - "PROCESSOR_SELECTION_V1.0.0.md"
+  - "PROCESSORS/CAPTION_V1.0.0.md"
+  - "PROCESSORS/STORY_V1.0.0.md"
+  - "CHUNK_DEFINITION_V1.0.0.md"
+---
+
+# Scene Processor (Scene Classification) V1.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-03 |
+| 文件版本 | V1.0 |
+
+**狀態**: ✅ 100% | **模型**: MIT Places365 (ResNet18) | **GPU**: 否
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| Scene Classification | 場景分類，辨識影片畫面的場景類型 |
+| Places365 | MIT 開發的場景辨識資料集與模型（365 個場景類別） |
+| ResNet18 | 殘差網路架構，輕量級分類模型 |
+| pre_chunks | 原始元件的資料表，Scene 輸出供 Rule 3 使用 |
+| parent chunk | 聚合多個 child chunks 的上層 chunk，由 Rule 3 產出 |
+
+## 選型過程
+
+初始使用 ImageNet（產生 scene_XXX 類別索引），後升級至 Places365 以獲得具名場景類別（如 living_room, beach, airport），準確率 85~90%。
+
+## 執行階段
+
+Scene 在 **register 階段同步執行**（`register_single_file`）。Worker 中重入時檢查後綴：
+- `.scene.json` → 從檔案載入（不入庫 pre_chunks）
+- `.scene.json.tmp` → 跳過（回傳空結果）
+- `.scene.json.err` → 跳過（回傳空結果）
+
+載入函數：`load_scene_from_file(path: &str) -> SceneClassificationResult`
+
+## 與 CUT 的關係
+
+Scene 與 ASR 無關（純視覺分類），已移除對 ASR 的依賴。CUT 為 Scene 的唯一前置依賴。
+
+## 輸出用途
+
+Scene 為 **pre_chunks**（scene boundary），供 Rule 3 產生 parent chunk。Rule 3 需要 CUT + Scene 的 boundary 來產生複合 parent chunk。
+
+## 效能實測（ExaSAN 159.6s 影片, 取樣間隔=2s）
+
+| 指標 | 值 |
+|------|-----|
+| 處理時間 | 4.09s |
+| 即時倍率 | 39.0x |
+| 取樣數 | 79 samples |
+
+## Charade 長片（6879s）
+
+| 指標 | 值 |
+|------|-----|
+| 處理時間 | 313.3s（5.2 分鐘） |
+
+## 資源預估
+
+| 資源 | 值 |
+|------|-----|
+| CPU | 0.3 |
+| 記憶體 | 512 MB |
+| GPU | 不使用 |
+| 依賴 | CUT, ASR |
@@ -0,0 +1,80 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "Story Processor V1.0.0"
+date: "2026-05-02"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+parent: "PROCESSOR_SELECTION_V1.0.0.md"
+tags:
+  - "momentry"
+  - "core"
+  - "processor"
+  - "story"
+  - "template-aggregator"
+  - "narrative"
+  - "v1.0.0"
+ai_query_hints:
+  - "Story 使用模板聚合從 ASR+YOLO+Scene 產生結構化敘述"
+  - "Story 已從 GPT-4 雲端 API 本地化為模板聚合"
+  - "Story 處理速度 <0.1s/chunk，極快"
+  - "Story 完全不依賴雲端 API，完全本地執行"
+  - "Story 依賴 Scene 和 Caption processor 的輸出"
+related_documents:
+  - "PROCESSOR_SELECTION_V1.0.0.md"
+  - "../SCENE_V1.0.0.md"
+  - "../CAPTION_V1.0.0.md"
+  - "../ASR_V1.0.0.md"
+  - "../CHUNK_DEFINITION_V1.0.0.md"
+---
+
+# Story Processor V1.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-02 |
+| 文件版本 | V1.0 |
+
+**狀態**: ✅ 100% | **模型**: 模板聚合 | **GPU**: 否
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| Story Processor | 從 ASR + YOLO + Scene 結果產生結構化敘述的處理器 |
+| Template Aggregation | 使用預定義模板組合資料，非 LLM 生成 |
+| GPT-4 | （已移除）先前使用的雲端 API 方案 |
+| local deployment | 完全本地執行，不依賴任何雲端 API |
+| structured narrative | 結構化敘述，以固定格式組織的故事描述 |
+
+---
+
+## 選型過程
+
+| 指標 | GPT-4（已移除） | 模板（新） |
+|------|----------------|------------|
+| 速度 | 3s/chunk | **<0.1s/chunk** |
+| 品質 | 自然語言 | 結構化格式 |
+| 依賴 | ✅ 雲端 API Key | ❌ 完全本地 |
+
+**決策**: 已從 GPT-4 雲端 API 本地化為模板聚合，從 ASR + YOLO + Scene 結果產生結構化敘述。
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+
+## 資源預估
+
+| 資源 | 值 |
+|------|-----|
+| CPU | - |
+| 記憶體 | - |
+| GPU | 不使用 |
+| 依賴 | Scene, Caption |
@@ -0,0 +1,74 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "VisualChunk Processor V1.0.0"
+date: "2026-05-02"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+parent: "PROCESSOR_SELECTION_V1.0.0.md"
+tags:
+  - "momentry"
+  - "core"
+  - "processor"
+  - "visual-chunk"
+  - "rule-aggregator"
+  - "yolo"
+  - "v1.0.0"
+ai_query_hints:
+  - "VisualChunk 是規則驅動的聚合器，非 ML 模型"
+  - "VisualChunk 將 YOLO 結果組合成視覺分片"
+  - "VisualChunk 依賴 YOLO processor 的偵測結果"
+  - "VisualChunk CPU 使用率低（0.3），記憶體 512 MB"
+  - "VisualChunk 是 Scene 和 Story processor 的前置依賴"
+related_documents:
+  - "PROCESSOR_SELECTION_V1.0.0.md"
+  - "../YOLO_V1.0.0.md"
+  - "../SCENE_V1.0.0.md"
+  - "../STORY_V1.0.0.md"
+  - "../CHUNK_DEFINITION_V1.0.0.md"
+---
+
+# VisualChunk Processor V1.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-02 |
+| 文件版本 | V1.0 |
+
+**狀態**: ✅ 整合 | **模型**: 無（規則聚合） | **GPU**: 否
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| VisualChunk | 規則驅動的聚合器，將 YOLO 結果組合成視覺分片 |
+| Rule Aggregation | 使用預設規則而非 ML 模型進行資料組合 |
+| Visual Chunk | 視覺分片，包含 YOLO 偵測物件的時間區間 |
+| pre_chunks | 原始元件表，VisualChunk 的輸出會寫入此表 |
+| dependency chain | 依賴鏈：YOLO → VisualChunk → Scene → Story |
+
+---
+
+## 說明
+
+非 ML 模型，是規則驅動的聚合器，將 YOLO 結果組合成視覺分片。
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+
+## 資源預估
+
+| 資源 | 值 |
+|------|-----|
+| CPU | 0.3 |
+| 記憶體 | 512 MB |
+| GPU | 不使用 |
+| 依賴 | YOLO |
@@ -0,0 +1,139 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "Voice Embedding 產出流程 V1.0.0"
+date: "2026-05-02"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+tags:
+  - "momentry"
+  - "core"
+  - "voice"
+  - "embedding"
+  - "asrx"
+  - "qdrant"
+  - "v1.0.0"
+ai_query_hints:
+  - "Voice Embedding 的完整處理流程（音軌 → ECAPA-TDNN → Qdrant）"
+  - "ASRX Processor 的三階段處理：音軌預處理 → ASR segments 載入 → Speaker Diarization"
+  - "Worker store_asrx_chunks 的步驟與 pre_chunks 寫入規則"
+  - "Qdrant voice collection 的 payload 結構與欄位定義"
+  - "Voice embedding 的 192-D ECAPA-TDNN 向量規格（L2 normalize）"
+  - "Voice embedding 使用 Cosine 距離計算與 L2 歸一化"
+  - "SpeechBrain ECAPA-TDNN 的資源預估與處理速度"
+  - "Voice embedding 與 ASR 處理器的依賴關係"
+related_documents:
+  - "../VECTOR_SPEC_V1.0.0.md"
+  - "../PROCESSORS/ASRX_V1.0.0.md"
+  - "../PROCESSORS/ASR_V1.0.0.md"
+  - "../PROCESSOR_SELECTION_V1.0.0.md"
+  - "../MOMENTRY_CORE_API_V1.0.0.md"
+---
+
+# Voice Embedding 產出流程 V1.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-02 |
+| 文件版本 | V1.0 |
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| Voice Embedding | 語音向量嵌入，由 ECAPA-TDNN 產出 192-D 向量 |
+| ECAPA-TDNN | SpeechBrain 提供的說話人辨識模型 |
+| L2 normalize | 向量歸一化，確保所有向量單位長度 |
+| Spectral Clustering | 頻譜聚類，將語音 embedding 分群以區分說話人 |
+| segment_index | 在 asrx 輸出 segments 中的索引編號 |
+| speaker_id | 說話人標籤（如 SPEAKER_0, SPEAKER_1） |
+
+## 處理流程
+
+```
+1. Video → ffmpeg 萃取音軌 → 16kHz mono WAV
+       │
+       ▼
+2. ASRX Processor (asrx_processor_custom.py)
+   │
+   ├── Stage 1: 音軌預處理
+   │   ├── ffprobe 列出所有音軌
+   │   ├── 選擇最佳音軌（優先英語）
+   │   └── ffmpeg 轉為 16kHz mono WAV
+   │
+   ├── Stage 2: 載入 ASR segments
+   │   └── 從 {file_uuid}.asr.json 讀取 segments
+   │
+   ├── Stage 3: Speaker Diarization (SelfASRXFixed.process_with_segments)
+   │   ├── 對每個 ASR segment 取出音訊片段
+   │   ├── ECAPA-TDNN 產出 192-D embedding
+   │   ├── 正規化 embeddings
+   │   └── 譜聚類 → speaker label
+   │
+   ├── 輸出: {file_uuid}.asrx.json
+   │   ├── segments: [start_time, end_time, speaker_id]
+   │   └── embeddings: [[192-D float array], ...]
+   │
+       ▼
+3. Worker store_asrx_chunks()
+   ├── 解析 AsrxResult
+   ├── 寫入 pre_chunks 表
+   └── 寫入 voice embeddings 到 Qdrant
+       │
+       ▼
+4. Qdrant `momentry_dev_voice`
+   └── 每個 segment 一個 vector
+```
+
+## Qdrant Payload 結構
+
+```json
+{
+  "file_uuid": "dd61fda85fee441fdd00ab5528213ff7",
+  "speaker_id": "SPEAKER_0",
+  "segment_index": 0,
+  "start_frame": 9,
+  "end_frame": 441,
+  "start_time": 0.3,
+  "end_time": 14.7
+}
+```
+
+| 欄位 | 型別 | 說明 |
+|------|------|------|
+| `file_uuid` | string | 來源影片識別碼 |
+| `speaker_id` | string | 說話人標籤（如 SPEAKER_0） |
+| `segment_index` | integer | 在 segments 中的索引 |
+| `start_frame` | integer | 起始幀 |
+| `end_frame` | integer | 結束幀 |
+| `start_time` | float | 起始時間（秒） |
+| `end_time` | float | 結束時間（秒） |
+
+## Vector 規格
+
+| 屬性 | 值 |
+|------|-----|
+| 模型 | SpeechBrain ECAPA-TDNN |
+| 維度 | 192 |
+| 距離計算 | Cosine |
+| 歸一化 | 是（L2 normalize） |
+
+## 來源 Processor 資源預估
+
+| 資源 | 值 |
+|------|-----|
+| 模型 | SpeechBrain ECAPA-TDNN (~80MB) |
+| CPU | 0.8 |
+| 記憶體 | 2048 MB |
+| GPU | 不使用 |
+| 處理速度 | 57x real-time (M4 Mac Mini) |
+| 依賴 | ASR（需 ASR JSON 完成後才能啟動） |
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
@@ -0,0 +1,178 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "YOLO Processor V1.0.0"
+date: "2026-05-02"
+version: "V1.0"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+parent: "PROCESSOR_SELECTION_V1.0.0.md"
+tags:
+  - "momentry"
+  - "core"
+  - "processor"
+  - "yolo"
+  - "object-detection"
+  - "yolov8"
+  - "v1.0.0"
+ai_query_hints:
+  - "YOLO 使用 yolov8n (nano) 模型進行物件偵測"
+  - "YOLO 在 M4 Mac Mini 上可達 100~200 FPS"
+  - "YOLO 支援 GPU 加速（MPS），可快 2~5 倍"
+  - "YOLO 輸出 4.3 MB 含偵測結果"
+  - "YOLO 是 VisualChunk 和 Scene 的依賴"
+related_documents:
+  - "PROCESSOR_SELECTION_V1.0.0.md"
+  - "../VISUAL_CHUNK_V1.0.0.md"
+  - "../POSE_V1.0.0.md"
+  - "../OCR_V1.0.0.md"
+  - "../CHUNK_DEFINITION_V1.0.0.md"
+---
+
+# YOLO Processor V1.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-02 |
+| 文件版本 | V1.0 |
+
+**狀態**: ✅ 100% | **模型**: YOLOv8n (nano) | **GPU**: 是
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| YOLO | You Only Look Once，即時物件偵測演算法 |
+| YOLOv8n | Ultralytics YOLO 第八代 nano 版本，最小最快 |
+| object detection | 物件偵測，辨識影像中的物體類別與位置 |
+| MPS | Metal Performance Shaders，Apple Silicon GPU 加速 |
+| bottleneck | 處理瓶頸，YOLO 與 Pose 同為最耗時的 processor |
+
+---
+
+## 選型過程
+
+| 模型 | 參數 | 大小 | 速度 | 精度 |
+|------|------|------|------|------|
+| **yolov8n (nano)** | **3.2M** | **6.2MB** | **最快** | **較低** |
+| yolov8s (small) | 11.2M | - | 快 | 中等 |
+| yolov8m (medium) | 25.9M | - | 中 | 高 |
+| yolov8l (large) | 43.7M | - | 慢 | 很高 |
+| yolov8x (x-large) | 68.2M | - | 最慢 | 最高 |
+
+**決策**: 預設使用 `yolov8n.pt`（nano），在 M4 Mac Mini 上可達 100~200 FPS。可透過配置檔切換至更大模型。
+
+---
+
+## 效能實測（ExaSAN 159.6s 影片, 全幀處理）
+
+| 指標 | 值 |
+|------|-----|
+| 處理時間 | 65.72s |
+| 即時倍率 | 2.4x（瓶頸之一） |
+| 輸出 | 4.3 MB |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+
+## 資源預估
+
+| 資源 | 值 |
+|------|-----|
+| CPU | 0.3 |
+| 記憶體 | 1024 MB |
+| GPU | 支援（`yolo_processor_mps.py` 可使用 MPS，快 2~5 倍） |
+| 依賴 | 無 |
+
+---
+
+## Apple Vision Framework 替代評估
+
+### POC 目標
+
+評估 Apple Vision Framework 是否可取代 YOLOv8n 進行物件偵測，目標是利用 ANE 加速降低記憶體與處理時間。
+
+### 測試結果
+
+測試影像：展場人物場景（640x360）、人物訪談場景（1920x1080）
+
+| Vision 功能 | 測試結果 | YOLOv8n 對應 | 可取代 |
+|------------|---------|-------------|--------|
+| **VNClassifyImageRequest** | `people:0.94`, `adult:0.94`, `sign:0.40` | 場景分類（目前用 Places365） | ✅ **可取代 Scene processor** |
+| **VNDetectHumanRectanglesRequest** | 2 persons, conf=0.68~0.76 | YOLO 'person' 類別 | ✅ **可取代 person 檢測** |
+| **VNDetectHumanBodyPoseRequest** | 19 joints (neck, shoulders, wrists) | MediaPipe Pose | ✅ **可取代 Pose processor** |
+| **VNDetectHumanHandPoseRequest** | 1 hand, conf=1.0 | 無對應 | ✅ 新功能 |
+| **VNGenerateObjectnessBasedSaliency** | 1 region, 無 class label | 無對應 | ⚠️ 僅顯著性區域 |
+| **一般物件偵測 (car/dog/bottle/chair...)** | ❌ **無此 API** | YOLO 80 COCO 類別 | ❌ **無法取代** |
+
+### 關鍵限制
+
+Vision Framework **沒有通用物件偵測器**。YOLOv8n 可偵測 80 個 COCO 類別（person, car, dog, bottle, chair, tv 等），Vision Framework 僅能偵測「人物」相關（人體、姿勢、手勢）和場景分類，無法辨識具體物體類別。
+
+### 選型結論
+
+| 用途 | 方案 |
+|------|------|
+| **人物偵測** | Vision Framework **可取代**（更快、更輕量） |
+| **一般物件偵測（car/dog/bottle）** | YOLOv8n **維持現狀**（Vision Framework 無法取代） |
+| **場景分類** | Vision Framework **可取代** MIT Places365 |
+| **姿態估計** | Vision Framework **可取代** MediaPipe Pose |
+
+若僅需 person 類別，Vision Framework 可完全取代 YOLO。但若需要其他 79 個 COCO 類別，YOLOv8n 仍是必要方案。
+
+### 相關檔案
+
+```
+scripts/swift_processors/vision_object_test.swift
+```
+
+---
+
+## CoreML 加速實驗
+
+### 動機
+
+YOLOv8n 使用 PyTorch CPU 推論（67ms/frame）且 **AGPL-3.0 License 有商用限制**。改用 YOLOv5n（**Apache 2.0**）+ CoreML 轉換，可同時解決 License 和效能問題。
+
+### 測試結果
+
+| 引擎 | License | Per frame | 加速比 | ANE |
+|------|---------|-----------|--------|-----|
+| **YOLOv8 PyTorch CPU** | AGPL-3.0 | 67ms | 1x | ❌ |
+| **YOLOv8 CoreML** | AGPL-3.0 | 13ms | 5.3x | ✅ |
+| **YOLOv5 PyTorch CPU** | **Apache 2.0** | 59ms | 1x | ❌ |
+| **YOLOv5 CoreML** ⭐ | **Apache 2.0** | **13ms** | **4.5x** | ✅ |
+
+**決定**: 以 YOLOv5 CoreML（`yolov5nu.mlpackage`）取代 YOLOv8。
+
+### 實作
+
+`yolo_processor.py` 模型載入順序：
+1. `yolov5nu.mlpackage`（CoreML, ANE）→ 優先使用
+2. `yolov5nu.pt`（PyTorch CPU）→ fallback
+3. 自動下載（若無本地檔案）
+
+### 相關檔案
+
+```
+yolov5nu.mlpackage  # CoreML model (5.2MB, Apache 2.0)
+yolov5nu.pt         # PyTorch weights (5.3MB, Apache 2.0)
+scripts/yolo_processor.py
+```
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+| V1.1 | 2026-05-04 | 以 YOLOv5 CoreML (Apache 2.0) 取代 YOLOv8 (AGPL) + Vision Framework 評估 | OpenCode | deepseek-chat |
+| V1.1 | 2026-05-04 | 新增 Apple Vision Framework 替代評估記錄 | OpenCode | deepseek-chat |
@@ -0,0 +1,201 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "Processor 選型與資源預估 V1.0.0"
+date: "2026-05-02"
+version: "V1.1"
+status: "active"
+owner: "Warren"
+created_by: "OpenCode"
+tags:
+  - "momentry"
+  - "core"
+  - "processor"
+  - "model-selection"
+  - "resource-estimation"
+  - "v1.0.0"
+ai_query_hints:
+  - "processor 的選型原因與實驗報告"
+  - "各 processor 的資源預估與模型資訊"
+  - "processor 之間的依賴關係"
+  - "模型選擇的比較與決策"
+  - "processor 檔案狀態後綴規則（json/tmp/err）"
+  - "Job 完成條件與必要 processor 定義"
+related_documents:
+  - "PROCESSORS/ASR_V1.0.0.md"
+  - "PROCESSORS/FACE_V1.0.0.md"
+  - "PROCESSORS/YOLO_V1.0.0.md"
+  - "PROCESSORS/CUT_V1.0.0.md"
+  - "CHUNK_DEFINITION_V1.0.0.md"
+---
+
+# Processor 選型與資源預估 V1.0.0
+
+| 項目 | 內容 |
+|------|------|
+| 建立者 | OpenCode |
+| 建立時間 | 2026-05-02 |
+| 文件版本 | V1.1 |
+
+---
+
+## 關鍵術語定義
+
+| 術語 | 定義 |
+|------|------|
+| Processor | 處理器，負責特定類型媒體分析的 Python 腳本 |
+| Pipeline | 處理管線，定義 processor 的執行順序與依賴關係 |
+| PythonExecutor | 統一執行 Python 腳本的 Rust 封裝層 |
+| real-time factor | 即時倍率，處理時間與影片時長的比值 |
+| resource estimation | 資源預估，包含 CPU/記憶體/GPU 的使用量 |
+| Job | 處理任務，包含多個 processor 的執行與狀態管理 |
+
+## 總覽
+
+| Processor | 狀態 | 模型 | 依賴 | GPU | CPU | 記憶體 | 文件 |
+|-----------|------|------|------|-----|-----|--------|------|
+| ASR | ✅ 100% | faster-whisper (small) | 無 | 否 | 1.0 | 2048 MB | [詳細](./PROCESSORS/ASR_V1.0.0.md) |
+| CUT | ✅ 100% | PySceneDetect | 無 | 否 | 0.5 | 512 MB | [詳細](./PROCESSORS/CUT_V1.0.0.md) |
+| YOLO | ✅ 100% | YOLOv5n (CoreML ANE) | 無 | 是 | 0.1 | 512 MB | [詳細](./PROCESSORS/YOLO_V1.0.0.md) |
+| OCR | ✅ 100% | Swift Vision VNRecognizeTextRequest | 無 | 是 (ANE) | 0.1 | 64 MB | [詳細](./PROCESSORS/OCR_V1.0.0.md) |
+| Face | ✅ 100% | InsightFace + CoreML FaceNet | 無 | 是 (ANE) | 0.3 | 512 MB | [詳細](./PROCESSORS/FACE_V1.0.0.md) |
+| Pose | ✅ 100% | Swift Vision VNDetectHumanBodyPoseRequest | 無 | 是 (ANE) | 0.1 | 64 MB | [詳細](./PROCESSORS/POSE_V1.0.0.md) |
+| ASRX | ⚠️ 80% | SpeechBrain ECAPA-TDNN | ASR | 否 | 0.8 | 2048 MB | [詳細](./PROCESSORS/ASRX_V1.0.0.md) |
+| Scene | ✅ 100% | MIT Places365 | CUT | 否 | 0.3 | 512 MB | [詳細](./PROCESSORS/SCENE_V1.0.0.md) |
+| VisualChunk | ✅ 整合 | 規則聚合（無模型） | YOLO | 否 | 0.3 | 512 MB | [詳細](./PROCESSORS/VISUAL_CHUNK_V1.0.0.md) |
+| Caption | ✅ 100% (本地化) | Moondream2 | Scene | 否 | - | - | [詳細](./PROCESSORS/CAPTION_V1.0.0.md) |
+| Story | ✅ 100% (本地化) | 模板聚合 | Scene, Caption | 否 | - | - | [詳細](./PROCESSORS/STORY_V1.0.0.md) |
+
+---
+
+## Processor 依賴關係圖 （V4.1）
+
+```
+CUT ───→ Scene
+  │
+ASR ───→ ASRX
+  │
+YOLO ─→ VisualChunk
+```
+
+> **註（V4.1）**：CUT 和 Scene 在 register 階段同步執行，Worker pipeline 中 Scene 依賴僅 CUT（已移除 ASR）。長影片（scene ≤ 3, max > 600s）時 Face 動態移到 ASR 前。
+
+## 檔案狀態後綴
+
+所有 processor 輸出檔案使用統一的後綴規則：
+
+| 後綴 | 意義 | 行為 |
+|------|------|------|
+| `.json` | 完成 | 直接載入使用 |
+| `.json.tmp` | 執行中 | 跳過、等待 |
+| `.json.err` | 失敗 | 跳過、不重試 |
+
+此規則由 `PythonExecutor` 統一處理（`executor.rs:150-279`）。
+
+## Job 完成條件（V4.1）
+
+| 條件 | 結果 |
+|------|------|
+| 所有 processor 完成 | ✅ Job completed |
+| 必要 processor (cut/asr/yolo) 完成，其餘失敗 | ✅ Job completed（非必要失敗不卡住） |
+| 必要 processor 任一失敗 | ❌ Job failed |
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本，含選型實驗報告與資源預估 | OpenCode | deepseek-chat |
+| V1.1 | 2026-05-03 | CUT 新增 cut_count/cut_max_duration；Scene 移除 ASR 依賴；長影片 Face 動態調度；Job 完成條件放寬 | OpenCode | deepseek-chat |
+
+---
+
+## Frame Scheduling 架構（V4.1）
+
+### 問題
+
+目前每個 processor 各自獨立呼叫 ffmpeg 從影片中萃取 frames，導致重複的 ffmpeg 解碼開銷：
+
+```
+YOLO: ffmpeg extract → detect → write
+OCR:  ffmpeg extract → OCR    → write  ← ffmpeg again
+Face: ffmpeg extract → detect → write  ← ffmpeg again
+Pose: ffmpeg extract → detect → write  ← ffmpeg again
+```
+
+對長片（6879s），每個 processor 的 ffmpeg overhead 約 15~30s，總計浪費 ~75s。
+
+### 解決方案：共享 Frame Cache + 並發調度
+
+```
+Pipeline Phase 1 (順序):
+  CUT → Scene → ASR → ASRX
+
+Frame Cache Phase (一次 ffmpeg):
+  ffmpeg extract → shared frame directory
+    ├── frame_00001.jpg
+    ├── frame_00002.jpg
+    └── ...
+
+Pipeline Phase 2 (並發 on shared frames):
+  tokio::join!(
+    OCR  (Swift Vision → frame dir)
+    Face (CoreML FaceNet → frame dir)
+    Pose (Swift Vision → frame dir)
+    YOLO (CoreML → frame dir)
+  )
+```
+
+### 實作模組
+
+| 模組 | 檔案 | 說明 |
+|------|------|------|
+| `FrameManager` | `src/core/frame_cache.rs` | 負責 ffmpeg extract、管理 frame 目錄生命週期 |
+| `ProcessorTask.frame_dir` | `src/worker/processor.rs` | 傳遞共享 frame 目錄路徑給 child process |
+| `MOMENTRY_FRAME_DIR` | env var | Worker 設此 env var，processor 讀取後跳過 ffmpeg |
+
+### V1 實作狀態
+
+| 項目 | 狀態 |
+|------|------|
+| `FrameManager::extract()` | ✅ 完成 — 一次 ffmpeg 產出 shared frame directory |
+| `MOMENTRY_FRAME_DIR` 環境變數傳遞 | ✅ `start_processor` 在 spawn 前設定 |
+| Swift OCR (`swift_ocr.swift`) | ✅ 若 `MOMENTRY_FRAME_DIR` 有值則跳過 ffmpeg |
+| Swift Pose (`swift_pose.swift`) | ✅ 同上 |
+| Python Face (`face_processor.py`) | ⏳ 待實作 |
+| Python YOLO (`yolo_processor.py`) | ⏳ 待實作 |
+
+### 流程
+
+```rust
+// job_worker.rs
+let frame_needed = [OCR, Face, Pose, Yolo].any_in(processors_to_run);
+if frame_needed {
+    let fm = FrameManager::extract(video, sample_interval).await;
+    // fm.dir → /tmp/frames_{hash}/ 含全部 .jpg
+}
+// processor.rs
+start_processor(task) {
+    if let Some(dir) = task.frame_dir {
+        std::env::set_var("MOMENTRY_FRAME_DIR", dir);
+    }
+    tokio::spawn(async move { run_processor(...) });
+}
+```
+
+### 效益
+
+| 指標 | 改善 |
+|------|------|
+| ffmpeg 呼叫次數 | 4次 → **1次** |
+| 累積 extract overhead | ~75s → **~15s** |
+| OCR/Face/Pose/YOLO 總執行時間 | 順序 N 倍 → **約等於最慢的 processor** |
+
+---
+
+## 版本歷史
+
+| 版本 | 日期 | 目的 | 操作人 | 工具/模型 |
+|------|------|------|--------|-----------|
+| V1.0 | 2026-05-02 | 初始版本 | OpenCode | deepseek-chat |
+| V1.1 | 2026-05-03 | CUT 新增 cut_count/cut_max_duration；Scene 移除 ASR 依賴；長影片 Face 動態調度；Job 完成條件放寬 | OpenCode | deepseek-chat |
+| V1.2 | 2026-05-04 | 新增 Frame Scheduling 架構 + V1 實作（FrameManager、env var 傳遞、Swift OCR/Pose 支援） | OpenCode | deepseek-chat |
@@ -0,0 +1,191 @@
+---
+document_type: "rca_report"
+service: "MOMENTRY_CORE"
+title: "RCA: Audrey Hepburn Identity 時序衝突 — Trace 39 & Trace 45"
+date: "2026-05-06"
+version: "V1.0"
+status: "completed"
+severity: "HIGH"
+author: "OpenCode"
+---
+
+# RCA: Audrey Hepburn Identity 時序衝突
+
+**Severity**: HIGH — 導致同一 Identity 下混入不同人物的 trace，clustering 精準度受損
+
+**時間線**: 2026-05-06, identity clustering runner_v2 執行後發現
+
+---
+
+## 1. 現象 (Symptom)
+
+Audrey Hepburn identity 下的 trace 39 和 trace 45 出現時間重疊（8 個共同 frame，18600–19020），同一幀內有兩個不同人的 face detection 被歸類為同一 identity。
+
+| Frame | Trace 39 位置 | Trace 45 位置 |
+|-------|-------------|-------------|
+| 18600 | (236, 432) 83×83px | (1242, 339) 135×135px |
+| 18660 | (244, 429) 81×81px | (1246, 311) 144×144px |
+| ... | ... | ... |
+| 19020 | (247, 435) 78×78px | (1243, 313) 155×155px |
+
+兩個人在同一幀的畫面左側和右側，**不可能是同一人**。
+
+---
+
+## 2. 數據分析 (Data Analysis)
+
+### 2.1 Embedding 相似度
+
+| 比對 | Cosine Similarity | 判定 |
+|------|------------------|------|
+| Trace 39 vs Audrey Hepburn TMDb ref | 0.375 | 弱 match（< 0.55 threshold） |
+| Trace 45 vs Audrey Hepburn TMDb ref | 0.169 | 極弱 match（< 0.3） |
+| Trace 39 vs Trace 45 | 0.121 | **明顯不同人**（same person > 0.85） |
+
+### 2.2 兩個 trace 都不該通過 Stage 1
+
+| Stage | Threshold | Trace 39 | Trace 45 |
+|-------|-----------|----------|----------|
+| Stage 1 (TMDb face-level) | face_sim ≥ 0.55 | ❌ 0.375 | ❌ 0.169 |
+
+兩個 trace 都沒有通過 Stage 1 的 TMDb 門檻。
+
+### 2.3 Stage 1b composite scoring 導致誤綁
+
+Stage 1b 使用複合分數：
+
+```
+composite = avg_sim × speaker_weight × (0.4 + 0.6 × match_ratio)
+bind if: composite > 0.35
+```
+
+| 因素 | 影響 |
+|------|------|
+| `speaker_weight` | 1.0 + 0.3 × speaker_count / max_count |
+| `match_ratio` | 個別 face sim ≥ 0.55 的比例 |
+
+Trace 39 的 avg_sim 只有 0.375，但 speaker_weight（×1.3）和 match_ratio 加成後，composite score 超過 0.35 門檻，因而被誤綁。
+
+---
+
+## 3. 根因 (Root Cause)
+
+### 3.1 Primary: Composite threshold 太低
+
+Stage 1b composite threshold 設定為 0.35，過低。即使 embedding 相似度只有 0.375（遠低於 0.55 的 face-level threshold），靠 speaker weighting + match ratio 加成也能通過。
+
+### 3.2 Secondary: 汙染擴散 (Contamination)
+
+一旦 trace 39 被誤綁（因 weak composite pass），它的 14 個 face embeddings 全部加入 Audrey Hepburn 的 reference set。這汙染了 reference set，使後續 trace（如 trace 45，cosine 僅 0.169）也能通過 iterative enrichment 的複合評分。
+
+```
+Stage 1b Round 1: trace 39 誤綁 → 14 faces 加入 reference
+Stage 1b Round 2: trace 45 被拉入 → 汙染 reference → 更多誤綁
+```
+
+### 3.3 Contributing: 無時序碰撞檢查
+
+Clustering 階段沒有檢查同一 identity 的兩個 trace 是否同時出現。若有此檢查，可立即發現 trace 39 和 trace 45 的衝突。
+
+---
+
+## 4. 影響範圍 (Impact)
+
+| 項目 | 數值 |
+|------|------|
+| 受影響 identity | Audrey Hepburn（id=9） |
+| 受影響 traces | trace 39 (14 faces) + trace 45 (8 faces) |
+| 總受影響 faces | 22 |
+| 同 identity 其他衝突 | 待全掃描確認 |
+
+---
+
+## 5. 修復方案 (Corrective Actions)
+
+| # | 措施 | 優先 | 說明 |
+|---|------|------|------|
+| 1 | 提升 composite threshold | 🔴 | 從 0.35 → 0.50，或加入 `avg_sim ≥ 0.30` 絕對下限 |
+| 2 | 加入時序碰撞檢查 | 🔴 | SQL: 同 identity 兩 trace 時間重疊 → 自動 split |
+| 3 | 加入 contamination guard | 🟡 | 每 round 限制 reference set 新加入數量，或定期 purge 低分 reference |
+| 4 | 修復已汙染 identity | 🟡 | 對 Audrey Hepburn 跑 collision scan，unbind 衝突 trace |
+
+### 5.1 時序碰撞檢查 SQL
+
+```sql
+SELECT i.name, a.trace_id, b.trace_id, a.frame_number
+FROM face_detections a
+JOIN face_detections b 
+  ON a.file_uuid = b.file_uuid 
+ AND a.frame_number = b.frame_number 
+ AND a.trace_id < b.trace_id
+JOIN identities i 
+  ON a.identity_id = i.id AND b.identity_id = i.id
+WHERE a.identity_id IS NOT NULL;
+```
+
+### 5.2 Runner 參數調整
+
+```json
+{
+    "stage1b_composite_threshold": 0.50,  // was 0.35
+    "stage1b_min_face_similarity": 0.30,  // new
+    "enable_temporal_collision_check": true  // new
+}
+```
+
+---
+
+## 6. 驗證 (Verification)
+
+修復後需重跑 identity clustering，確認：
+1. Trace 39 和 45 不再被綁到 Audrey Hepburn
+2. 時序碰撞檢查正確分離衝突 trace
+3. Coverage 無顯著下降
+
+---
+
+## 7. 時間線 (Timeline)
+
+| 時間 | 事件 |
+|------|------|
+| 2026-05-06 13:30 | runner_v2 執行，671 traces bound |
+| 2026-05-06 14:15 | trace_quality_agent 發現時序衝突 |
+| 2026-05-06 14:30 | RCA 分析完成 |
+
+---
+
+## 8. 驗證結果 (Verification)
+
+### 8.1 參數修正後重跑
+
+| 參數 | 修復前 | 修復後 |
+|------|--------|--------|
+| `stage1b_composite_threshold` | 0.35 | 0.50 |
+| `stage1b_min_face_similarity` | 無 | 0.30 |
+| `enable_temporal_collision_check` | 無 | true |
+
+### 8.2 Trace 39 & 45 結果
+
+| | 修復前 | 修復後 |
+|---|--------|--------|
+| Trace 39 bound to | Audrey Hepburn | **Ned Glass** |
+| Trace 45 bound to | Audrey Hepburn | Audrey Hepburn |
+| 同 identity 碰撞 | 114 pairs | **0 — 已分離** |
+
+### 8.3 整體影響
+
+| 指標 | 修復前 | 修復後 |
+|------|--------|--------|
+| DB writes | 4059 | 3971 |
+| 精準度提升 | — | 88 faces removed |
+| Coverage | 99.4% | 99.4% (維持) |
+
+## 9. 結論 (Conclusion)
+
+**根因**: Stage 1b composite threshold 過低導致弱 match 被誤綁。
+
+**修復**: threshold 0.35→0.50 + min_face_similarity=0.30。
+
+**驗證**: Trace 39 和 45 已分離，碰撞歸零。
+
+**結案**: CLOSED — 根因已解決。
@@ -0,0 +1,84 @@
+---
+document_type: "experiment_report"
+service: "MOMENTRY_CORE"
+title: "Identity Clustering Agent 研究報告（含品質檢查 + 綁定分析）"
+date: "2026-05-06"
+version: "V1.1"
+status: "completed"
+---
+
+# Identity Clustering Agent 研究報告
+
+## 1. 綁定流程架構
+
+Runner 採用雙階段策略：
+
+```
+             ┌── Stage 1: TMDb Direct Match ──┐
+             │  來源: identities.face_embedding │
+             │  模型: CoreML FaceNet 512-dim   │
+             │  門檻: face_sim ≥ 0.55          │
+             │  條件: ≥60% faces match         │
+             │  結果: 294 traces (43.4%)       │
+             └────────────────────────────────┘
+                          │
+                          ▼
+             ┌── Stage 1b: Iterative Enrichment ─┐
+             │  來源: bound trace multi-angle ref │
+             │  機制: 每 trace 取 top-3 faces    │
+             │  門檻: composite ≥ 0.50           │
+             │  下限: min_face_similarity ≥ 0.30 │
+             │  Round 1: 196 traces → Round 5: 1 │
+             │  結果: 363 traces (53.6%)         │
+             └───────────────────────────────────┘
+                          │
+                          ▼
+             ┌── Stage 2: Centroid Clustering ──┐
+             │  剩餘 trace 用 adaptive threshold │
+             │  結果: 20 traces grouped         │
+             └───────────────────────────────────┘
+```
+
+**總覆蓋率**: 677/677 traces (100%)，其中 657 traces bound to 8 TMDb identities，20 traces clustered。
+
+## 2. TMDb Direct Match vs Iterative Enrichment
+
+| 特性 | Stage 1 (TMDb) | Stage 1b (Iterative) |
+|------|---------------|---------------------|
+| 參考來源 | identities.face_embedding | bound trace faces |
+| embedding 品質 | TMDb 官方照片（單一視角） | 影片中 multi-angle（3 視角） |
+| 門檻 | 0.55 face_sim + 0.60 ratio | 0.50 composite + 0.30 min_sim |
+| 受門檻修正影響 | ❌ 否 | ✅ 是（0.35→0.50） |
+| 精準度 | 高（TMDb 照片 = ground truth） | 中（可能汙染，參考 RCA） |
+| traces bound | 294 (43.4%) | 363 (53.6%) |
+| 風險 | 低 | 汙染擴散（RCA: trace 39/45） |
+
+## 3. Trace 品質檢查
+
+### 3.1 取樣密度檢查
+
+1886/2347 traces (80.4%) < 4 frames。需 swift_face dense scan。
+
+### 3.2 人臉驗證
+
+DeepFace 測試 10 traces 全為 human。Apple Vision confidence + landmarks 可替代 DeepFace。
+
+### 3.3 Embedding 品質
+
+Top 10 traces intra-trace variance: 從 0.041 (excellent) 到 0.334 (likely split)。
+
+### 3.4 時序碰撞
+
+修復前: Audrey Hepburn 有 114 處同 identity 碰撞。  
+修復後: threshold 0.35→0.50 + min_sim 0.30，碰撞歸零。
+
+## 4. 修復後整體影響
+
+| 指標 | 修復前 | 修復後 | Δ |
+|------|--------|--------|-----|
+| DB writes | 4059 | 3971 | -88 |
+| Coverage | 99.4% | 99.4% | — |
+| Collision (Audrey) | 114 | 0 | -114 |
+| Avg composite threshold | 0.35 | 0.50 | +0.15 |
+| Min face similarity guard | 無 | 0.30 | new |
+DOC
@@ -0,0 +1,322 @@
+---
+document_type: "spec"
+service: "MOMENTRY_CORE"
+title: "UUID Encoding Rules V1.0"
+date: "2026-05-05"
+version: "V1.0"
+status: "design"
+owner: "Warren"
+created_by: "OpenCode"
+tags:
+  - "momentry"
+  - "core"
+  - "uuid"
+  - "encoding"
+  - "v1.0"
+ai_query_hints:
+  - "UUID encoding rules for identities, files, resources, jobs"
+  - "Deterministic UUID v5 for cross-system identity matching"
+  - "file_uuid 32-char birth UUID (hash of MAC+time+path+name)"
+  - "identity_uuid 32-char stripped UUIDv5"
+related_documents:
+  - "../DUAL_EMBEDDING_PIPELINE_V1.0.0.md"
+  - "../CHUNK_DEFINITION_V1.0.0.md"
+---
+
+# UUID Encoding Rules V1.0
+
+## 目的
+
+統一系統內所有資源的 UUID 編碼規則，確保跨系統不衝突、可追溯、無語意歧義。
+
+## 各資源 UUID 規則
+
+| 資源 | 欄位 | 產生方式 | 長度 | 編碼意義 |
+|------|------|---------|------|---------|
+| **File** | `file_uuid` | Birth UUID: `SHA256(MAC + registration_time + canonical_path + filename)` | 32 | MAC + 時間 + 路徑 + 檔名 → 內容相同但不同機器/時間仍不同 |
+| **Identity** | `identity_uuid` | UUIDv5: `UUIDv5(NS, source:external_id)` | 32 | source + external_id → 跨系統唯一確定 |
+| **Job** | `job_uuid` | UUIDv4 random | 32 | 每次執行獨立 |
+| **Resource** | `resource_uuid` | UUIDv5: `UUIDv5(NS, hostname:resource_id)` | 32 | hostname + resource_id → 同主機同 ID 不變 |
+
+## Identity UUIDv5 編碼規則
+
+### 意義
+
+`identity_uuid` = source + external_id 的確定性映射。  
+同一來源系統的同一外部 ID → 永遠相同 UUID。  
+跨系統合併 identity 時不衝突。
+
+### Namespace
+
+```
+MOMENTRY_IDENTITY_NS = "6ba7b810-9dad-11d1-80b4-00c04fd430c8"  // Standard DNS namespace
+```
+
+### Source-specific encoding
+
+| Source | External ID | UUIDv5 Input | 碰撞機率 |
+|--------|------------|-------------|---------|
+| `tmdb` | `"285"` (person_id) | `"tmdb:285"` | 0（同 source 同 id 同 UUID） |
+| `manual` | user-assigned name | `"manual:Cary Grant"` | 0（同名同 source） |
+| `face_cluster` | `file_uuid + cluster_id` | `"cluster:384b0ff...:cluster_0"` | 極低（跨 file） |
+
+### 優點
+
+1. **跨系統確定性**：無論哪台機器、哪次執行，同一個 TMDb actor 永遠拿到相同 UUID
+2. **合併安全**：兩套系統產生的 identity 集合可以直接合併，UUID 不衝突
+3. **可追溯**：從 UUID 本身無法反推 source（單向 hash），但透過 DB metadata 查得到來源
+4. **零碰撞**：不同 source + different external_id → different UUID
+
+### 現有資料遷移
+
+```
+1. 讀取所有 identities
+2. 計算 UUIDv5("tmdb:{tmdb_id}") 為新的 identity_uuid
+3. 手動註冊的 identities 用 UUIDv5("manual:{name}")
+4. 更新 face_detections.identity_id 指向新 UUID
+5. 更新 chunks metadata
+```
+
+## File UUID (保持不變)
+
+File UUID = `SHA256(MAC + registration_time + canonical_path + filename)` 的前 128 bits，32 hex chars。  
+跨系統不變（同檔案不同機器註冊，UUID 不同但可追溯）。**不更改。**
+
+## Job UUID (升級)
+
+目前用 `INTEGER auto-increment`（單機安全，多機碰撞）。  
+改為 `UUIDv4`（32 hex），支援多機 worker 並行。
+
+## Resource UUID (新增)
+
+目前用 `resource_id` 字串（任意）。  
+改為 `UUIDv5(namespace, hostname:resource_id)`（32 hex），支援多機註冊不碰撞。
+
+### Resource 分類
+
+| 類別 | resource_type | 說明 | 目前實例 |
+|------|--------------|------|---------|
+| `compute` | worker, server | 運算節點 | momentry_playground worker/server |
+| `storage` | postgres, mongodb, redis, qdrant, mariadb | 資料儲存 | localhost 服務 |
+| `ai` | ollama, llama_cpp, embedding | AI/ML 推理服務 | Ollama serve, llama-server |
+| `proxy` | caddy, sftpgo | 反向代理/檔案服務 | Caddy, SFTPGo |
+| `web` | wordpress, php-fpm | 前端 portal | WordPress |
+| `external` | tmdb, n8n | 外部 API 整合 | TMDb API, n8n |
+
+### Resource 生命週期欄位
+
+| 欄位 | 型別 | 說明 | 範例 |
+|------|------|------|------|
+| `resource_uuid` | 32 hex | UUIDv5 唯一識別 | `a4f288...` |
+| `resource_type` | enum | compute/storage/ai/proxy/external | `ai` |
+| `resource_subtype` | string | ollama, llama_cpp, postgres... | `ollama` |
+| `hostname` | string | 執行主機 | `mac-studio.local` |
+| `port` | int | service port | `11434` |
+| `started_at` | timestamp | 啟動時間 | `2026-05-05T10:00:00Z` |
+| `stopped_at` | timestamp | 停止時間 (NULL=運行中) | `NULL` |
+| `config` | jsonb | 執行參數/環境設定 | `{"model":"nomic-embed-text-v2-moe","dim":768}` |
+| `install_source` | string | 安裝來源 | `homebrew`, `docker`, `binary`, `source` |
+| `install_path` | string | 安裝路徑 | `/opt/homebrew/opt/ollama` |
+| `location` | string | 實體位置/網路位置 | `localhost`, `rackserver-01` |
+| `status` | enum | running/stopped/error/unknown | `running` |
+
+### 目前 service 實例
+
+| resource_type | subtype | port | license | 商用 |
+|--------------|---------|------|---------|------|
+| ai | ollama | 11434 | MIT | ✅ |
+| ai | llama_cpp | 8081 | MIT | ✅ |
+| storage | postgres | 5432 | PostgreSQL | ✅ |
+| storage | mongodb | 27017 | SSPL v1 | ⚠️ 非 OSI 開源。內部使用不受限制，不可轉售為 DB 服務 |
+| storage | redis | 6379 | RSALv2 / SSPL | ⚠️ 7.4+ 雙授權。內部使用不受限制，不可轉售為雲端服務 |
+| storage | qdrant | 6333 | Apache 2.0 | ✅ |
+| proxy | caddy | 443 | Apache 2.0 | ✅ |
+| proxy | sftpgo | 8080 | AGPL-3.0 | ⚠️ 網路服務觸發 copyleft。未修改原始碼風險較低，商用建議評估替代方案 |
+
+### sftpgo 替代方案
+
+sftpgo 提供 SFTP + HTTP file serve + Web UI + user management。可依需求分層替代：
+
+| 功能 | 替代方案 | License | 說明 |
+|------|---------|---------|------|
+| HTTP file serve | **Caddy** `file_server` | Apache 2.0 ✅ | 已運行中。一行 config 即可提供目錄服務 |
+| WebDAV | **Caddy** `webdav` plugin | Apache 2.0 ✅ | 如需 WebDAV 掛載 |
+| SFTP protocol | **OpenSSH** `internal-sftp` | MIT ✅ | macOS 內建，無需額外安裝 |
+| User management | **Caddy** `basicauth` | Apache 2.0 ✅ | 基本 auth 已夠用 |
+| Web admin UI | 不需要 | — | 若只需 file serve，Web UI 非必要 |
+
+**建議**：先用 Caddy `file_server` 取代 HTTP 端，SFTP 用 OpenSSH。sftpgo 可在商用授權前逐步退役。Caddy 已處理 TLS、reverse proxy、basic auth，不需要 sftpgo 的重複功能。
+
+```caddyfile
+# 範例：Caddy 替代 sftpgo file serve，含 user 管制
+files.momentry.ddns.net {
+    root * /Users/accusys/momentry/var/sftpgo/data
+    
+    # 管制方式三選一：
+    
+    # 1. Basic Auth（最簡單）
+    basicauth {
+        demo $2a$14$hashed_password_here
+    }
+    
+    # 2. JWT Token（via forward_auth）
+    # forward_auth localhost:9001 {
+    #     uri /api/v1/auth/verify
+    #     copy_headers Authorization
+    # }
+    
+    # 3. IP Whitelist（內網 only）
+    # @allowed remote_ip 192.168.1.0/24 127.0.0.1
+    
+    file_server browse
+    import common_log sftpgo_access
+}
+```
+
+### User 管制方式比較
+
+| 方式 | 複雜度 | 適用場景 |
+|------|--------|---------|
+| **basicauth** | 低 | 少數固定 user，密碼 hash 存在 config |
+| **forward_auth** | 中 | 由 momentry API 統一驗證 token |
+| **IP whitelist** | 低 | 內網服務，不開放外部 |
+| compute | worker | — | MIT | ✅ |
+| compute | server | 3002/3003 | MIT | ✅ |
+| external | tmdb | — | TMDb ToS | ⚠️ 替代方案：手動上傳、自有演員資料庫 |
+| external | n8n | 5678 | Sustainable Use | ⚠️ 商用需付費 |
+| web | wordpress | 80/443 | GPL-2.0 | ✅ portal 前端 |
+| storage | mariadb | 3306 | GPL-2.0 | ✅ WordPress DB 後端 |
+| web | wordpress | 443 (caddy) | GPLv2 | ✅ |
+| web | php | 9000 (php-fpm) | PHP License | ✅ |
+| storage | mariadb | 3306 | GPLv2 | ✅ |
+
+### Log 路徑
+
+每個 service 的 log 位於 `/Users/accusys/momentry/var/{service}/log/`：
+
+| service | stdout log | error log |
+|---------|-----------|-----------|
+| sftpgo | `var/sftpgo/log/stdout.log` | `var/sftpgo/log/stderr.log` |
+| n8n | `var/n8n/n8n-main.log` | `var/n8n/n8n-main-error.log` |
+| mariadb | `var/mariadb/ddl_recovery.log` | `var/mariadb/tc.log` |
+
+momentry core 本身（playground / production）目前 log 到 `/tmp/`（開發）或 systemd journal（生產）。應統一遷移到：
+
+| 環境 | Port | Log 目錄 |
+|------|------|---------|
+| dev | 3003 | `/Users/accusys/momentry/log/dev/` |
+| public (production) | 3002 | `/Users/accusys/momentry/log/public/` |
+
+每個環境下的 log 命名：
+```
+momentry/log/dev/
+  ├── momentry.log        # API server stdout
+  ├── momentry.error.log  # API server stderr
+  ├── worker.log          # Worker stdout
+  ├── worker.error.log    # Worker stderr
+  ├── processor/
+  │   ├── face.log        # Face processor output
+  │   └── asr.log         # ASR processor output
+  └── agent/
+      ├── story.log
+      └── identity.log
+
+momentry/log/public/
+  └── (same structure)
+```
+
+### 隔離原則
+
+| 規則 | 說明 |
+|------|------|
+| 永不交叉 | dev log 不寫入 public，反之亦然 |
+| 環境識別 | 從 log 路徑即可判斷來源環境 |
+| 獨立 rotation | 各自獨立的 logrotate 規則 |
+| 清除安全 | 清除 dev log 不影響 public |
+
+## URL Path 規範
+
+所有 UUID 在 URL 中使用 **32-char hex（無 dash）** 格式：
+
+```
+GET /api/v1/files/384b0ff44aaaa1f14cb2cd63b3fea966        ← file
+GET /api/v1/identities/3f5d1e09ce86c27aa631162052ec9c97    ← identity
+GET /api/v1/jobs/942d0bdf5d6fb6ac18b47deb031e60c3          ← job
+```
+
+### 為何 strip dash
+
+1. **一致**：file_uuid 為 32 hex（無 dash），統一風格
+2. **短**：URL 從 36 → 32 chars
+3. **容錯**：input 端兩種格式都接受，output 端統一 strip
+
+## 設計說明
+
+### File UUID 為何用 Birth UUID 而非 Content Hash
+
+Content hash（MD5/SHA256 of file content）適用於「相同內容 = 相同檔案」的場景。但 momentry 的情境是：
+- 同一影片可能有不同 cut 版本（廣告、預告、完整版）
+- 同一影片在不同機器上註冊應區分（追蹤來源）
+- 需要追溯「哪台機器在何時註冊了哪個檔案」
+
+因此用 Birth UUID = `SHA256(MAC + time + path + filename)`，而非 content hash。
+
+### Identity 第一參考面取得
+
+TMDb 只是取得第一張參考照片的 **手段之一**，不是唯一來源：
+
+```
+1. TMDb (或其他來源) → 下載照片
+2. 提取 face embedding → 寫入 identities.face_embedding
+3. 刪除照片（不留原始檔案）
+4. 用這個 embedding 找到第一個 matching video trace
+5. 從 video trace 中取 3 個最佳影片臉 → 取代外部 embedding → 成為 identity reference
+```
+
+之後 identity reference 全部來自影片臉，不再依賴外部照片。
+
+**⚠️ TMDb 商用授權**：TMDb API 有商用限制。若產品上線需處理授權，或改用替代方案：
+1. 手動上傳參考照片
+2. 跨檔案 identity merge（從已有 traces 取 reference）
+3. 自有演員資料庫
+
+跨系統合併 identity 時，需要知道「TMDb actor 285」在不同系統上是否為同一個人。UUIDv5 提供確定性映射：
+- `tmdb:285` → 永遠是 `cc6b8c2569ff5dec8f9e33164c7756b3`
+- 任何系統、任何時間計算都得到相同結果
+- 不需要 central registry，mathematically guaranteed
+
+### 現有資料遷移策略
+
+Identity UUID 遷移非破壞性：舊 UUID 保留在 `metadata.legacy_uuid`，新 UUID 寫入 `identities.uuid`。向下相容查詢。
+
+## UUID 與獨立工作空間
+
+每個資源的 working space、輸入、產出各自獨立，互不汙染：
+
+| 資源 | UUID | Working Space | 輸入 | 產出 |
+|------|------|--------------|------|------|
+| **File** | `file_uuid` | `output_dev/{uuid}/` | `{video_path}` | `{uuid}.cut.json, .asr.json, .face.json, ...` |
+| **Identity** | `identity_uuid` | `dev.identities` table | face_detections, voice_embeddings, TMDb API, manual input | `identities.face_embedding`, `identities.voice_embedding`, `identity_bindings`, `file_identities` |
+| **Job** | `job_uuid` | `dev.monitor_jobs` + `dev.processor_results` | `processors[]` list | `processor_results.status`, log entries |
+| **Resource** | `resource_uuid` | `var/{resource}/log/` | config, exec_path | log files, heartbeat records |
+
+File 的工作空間在 filesystem，Identity/Job/Resource 在 DB。各自目錄/table 獨立，刪除一個不影響其他。
+
+## Dev / Public 完整隔離表
+
+| 資源 | dev | public |
+|------|-----|--------|
+| DB Schema | `dev.*` | `public.*` |
+| Qdrant | `momentry_dev_*` | `momentry_*` |
+| Redis prefix | `momentry_dev:` | `momentry:` |
+| Output dir | `output_dev/` | `output/` |
+| Log | `log/dev/` | `log/public/` |
+| Resource UUID | `UUIDv5(hostname:xxx_dev)` | `UUIDv5(hostname:xxx)` |
+| Port | 3003 | 3002 |
+| .env file | `.env.development` | `.env` |
+
+## 版本歷史
+
+| 版本 | 日期 | 變更 |
+|------|------|------|
+| V1.0 | 2026-05-05 | Book UUID (file), UUIDv5 (identity), UUIDv4 (job), UUIDv5 (resource)。Resource 分類與生命週期。 |
--- a/Show More
+++ b/Show More
				`@@ -1 +0,0 @@`
				`/Users/accusys/momentry_core_0.1/output/a1b10138a6bbb0cd.cut.json`