fix: ASRX duplication, TKG edges, trace ingest, and add pipeline progress publishing
- ASRX handler no longer stores duplicate 'asr' pre_chunks - Pre_chunks storage made idempotent (delete-before-insert) - Rule 1 + trace_ingest changed to query 'asrx' not 'asr' - Trace chunks removed (dynamic from TKG/Qdrant) - TKG scroll_face_points fixed: trace_id >= 1 (not == 1) - TKG AsrxSegmentEntry: start/end -> start_time/end_time (match ASRX JSON) - Unregister error handling: log instead of silent discard - Add publish_pipeline_progress calls at each pipeline stage (processors, rule1, face_trace, identity_agent, TKG, rule2, completion)
This commit is contained in:
Accusys
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---
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title: Charade Identity Processing Fix Report
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date: 2026-06-29
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author: OpenCode
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status: completed
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---
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## Summary
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**Problem**: Charade file (UUID: c36f35685177c981aa139b66bbbccc5b) identity processing failed because of data corruption and missing TKG nodes.
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**Root Cause**: Circular dependency chain broken:
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- face_detections had 3x duplicate records (12726 instead of 4242)
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- All trace_id = NULL (UPDATE failed)
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- TKG Phase 2.5 couldn't create face_track nodes (needs trace_id)
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- Identity Agent couldn't mark suggestions (needs TKG nodes)
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## Fix Steps
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### Step 1: Clean Duplicate Data ✅
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- Deleted 8484 duplicate records
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- 12726 → 4242 unique face_detections
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### Step 2: Write trace_id ✅
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- store_traced_faces.py successfully updated DB
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- 4242 faces with trace_id (100% populated)
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- 426 unique traces
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### Step 3: Create TKG Nodes ✅
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- Created 426 face_track nodes via SQL
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- Fixed external_id format: "face_track_*" (matches Rust code)
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### Step 4: Run Identity Agent ✅
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- Identity matching: 2 traces matched to Audrey Hepburn
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- TKG marking: 2/2 nodes marked as "suggested"
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## Final Results
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| Metric | Before | After |
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|--------|--------|-------|
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| face_detections | 12726 (3x duplicates) | 4242 (unique) |
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| trace_id populated | 0 | 4242 (100%) |
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| TKG face_track nodes | 0 | 426 |
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| Identity suggestions | 0 | 2 (Audrey Hepburn) |
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**Identity Matches**:
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- Trace 202: Audrey Hepburn (score=0.6002)
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- Trace 311: Audrey Hepburn (score=0.6724)
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## Technical Details
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### Data Sources
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- face.json: 3176 frames, 4242 faces
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- face_traced.json: 426 traces (IoU tracking)
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- Qdrant _faces: 374 traces with embeddings
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- Qdrant _seeds: 2 TMDb seeds
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### Tools Used
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- PostgreSQL: face_detections, tkg_nodes tables
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- Python: store_traced_faces.py, identity_matcher.py
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- Qdrant: _faces, _seeds collections
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## Next Steps
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1. User confirmation: Check suggested identities via Portal UI
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2. Manual confirmation: Confirm Audrey Hepburn matches
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3. Propagation: Run Round 2 matching (propagate confirmed identities)
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4. Stranger clustering: Cluster unmatched traces (TH=0.40)
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## Files Modified
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- PostgreSQL: public.face_detections (deleted 8484 duplicates)
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- PostgreSQL: public.tkg_nodes (created 426 face_track nodes)
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- Qdrant: _faces collection (updated 3176 trace_ids)
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## Related Documents
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- docs/PROCESSING_PIPELINE.md
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- src/core/processor/tkg.rs:550-683 (build_face_track_nodes)
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- scripts/store_traced_faces.py (trace_id storage)
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- scripts/identity_matcher.py (TMDb matching)
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116
docs_v1.0/M4_workspace/2026-06-30_cut_escape_fix.md
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116
docs_v1.0/M4_workspace/2026-06-30_cut_escape_fix.md
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---
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title: Cut Scene Detection Escape Fix
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date: 2026-06-30
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author: OpenCode
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status: completed
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---
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## Summary
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**Problem**: Cut scene detection returned only 1 scene (fallback) instead of 833 scenes for Charade video.
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**Root Cause**: Python script `cut_processor.py` line 68 used `\\\\` (4 backslashes) → ffprobe received `\\` → scene detection failed → 0 scene times → fallback to single scene.
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## Fix
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### Code Changes
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1. **scripts/cut_processor.py** line 68:
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- Before: `f"movie={video_path},select='gt(scene\\\\,0.3)',showinfo"`
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- After: `f"movie={video_path},select='gt(scene\\,0.3)',showinfo"`
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2. **src/core/processor/cut.rs** line 127:
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- Already correct: `&format!("movie={},select='gt(scene\\,0.3)',showinfo", video_path)`
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- No changes needed
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### Escape Analysis
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| Escape Level | Python String | ffprobe receives | Result |
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|--------------|---------------|------------------|--------|
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| `\\\\` | `"\\"` | `\\` | ❌ 0 scenes |
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| `\\` | `"\\"` | `\` | ✅ 832 scenes |
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| `\` (raw) | `r"\ "` | `\` | ✅ 832 scenes |
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### Testing
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```bash
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# Before fix
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python3 scripts/cut_processor.py video.mp4 output.json
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# Result: 1 scene (fallback)
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# After fix
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python3 scripts/cut_processor.py video.mp4 output.json
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# Result: 833 scenes
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```
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## Verification
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### File: 3dfc20618fb522e795240b5f0e5ff6f0 (Charade)
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| Metric | Before | After |
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|--------|--------|-------|
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| cut.json scenes | 1 | 833 |
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| workspace.sqlite pre_chunks (cut) | 12 | 833 |
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| Scene 1 end_frame | 162695 (whole video) | 932 |
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### Workspace.sqlite Status
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```bash
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sqlite3 output/3dfc20618fb522e795240b5f0e5ff6f0.workspace.sqlite \
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"SELECT processor_type, COUNT(*) FROM pre_chunks GROUP BY processor_type;"
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cut|833
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ocr|942
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```
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## Technical Details
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### ffprobe Command
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Correct format:
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```bash
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ffprobe -v quiet -show_entries frame=pts_time -of default=nk=0 \
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-f lavfi "movie=/path/to/video.mp4,select='gt(scene\\,0.3)',showinfo" \
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-show_frames
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```
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- `scene\\,0.3` in shell → ffprobe receives `scene\,0.3`
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- The `\` escapes the comma in ffmpeg filter syntax
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### Python subprocess Behavior
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- Without `shell=True`: arguments passed directly to executable
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- Python string `"\\\\"` → subprocess receives `"\\"`
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- Python string `"\\"` → subprocess receives `"\"`
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- Raw string `r"\ "` → subprocess receives `"\"`
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## Impact
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### Affected Videos
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- Charade (UUID: 3dfc20618fb522e795240b5f0e5ff6f0)
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- Other videos registered before this fix may have incorrect scene counts
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### Remediation
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1. Re-run cut detection for affected videos
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2. Update workspace.sqlite pre_chunks
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3. If in PostgreSQL: update public.pre_chunks table
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## Next Steps
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1. Verify fix in production by registering new video
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2. Check if other videos need remediation
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3. Consider adding unit test for cut escape handling
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## Related Files
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- scripts/cut_processor.py
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- src/core/processor/cut.rs
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- src/api/files.rs (register API uses Python script)
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## Version History
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| Version | Date | Author | Changes |
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|---------|------|--------|---------|
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| 1.0 | 2026-06-30 | OpenCode | Initial report |
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# Face Detections 表清理計劃
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## 問題
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所有使用 `face_detections` 表的代碼都是錯誤的，需要改為使用 Qdrant workspace traces。
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## 正確架構
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### PostgreSQL
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```
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identities (全局人物主表)
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├── id
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├── uuid
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├── name
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├── status
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└── metadata
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```
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### Qdrant Payload
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```
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{prefix}_workspace_traces (512d vectors)
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├── file_uuid
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├── trace_id
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├── frame_number
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├── identity_id ← 绑定存储在这里
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├── bbox
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├── confidence
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└── embedding
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```
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## 錯誤代碼位置 (197 處)
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### 1. Processor 層 (寫入錯誤)
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- `src/core/processor/processor.rs` - line 744, 1311
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- `src/core/processor/job_worker.rs` - line 647
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- `src/core/db/workspace_sqlite.rs` - line 257-263 (函數定義)
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- `src/core/db/postgres_db.rs` - line 2712 (函數定義)
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### 2. TKG 處理器 (大量使用)
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- `src/core/processor/tkg.rs` - ~50 處使用 `face_detections` 表
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### 3. Chunk Ingest
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- `src/core/chunk/trace_ingest.rs` - line 10
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- `src/core/chunk/rule2_ingest.rs` - line 26
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### 4. API 層 (查詢/更新錯誤)
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- `src/api/identity_api.rs` - 22 處
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- `src/api/identity_binding.rs` - 12 處
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- `src/api/identities.rs` - 2 處
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- `src/api/identity_agent_api.rs` - 7 處
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- `src/api/files.rs` - 4 處
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- `src/api/media_api.rs` - 3 處
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### 5. Identity 層
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- `src/core/identity/storage.rs` - 3 處
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## 修改計劃
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### Phase 1: 分析現有代碼
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1. 理解當前 face_detections 表的使用方式
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2. 理解 Qdrant workspace traces 的結構
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3. 確定需要修改的函數列表
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### Phase 2: 創建 Qdrant 查詢輔助函數
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1. 創建 `QdrantWorkspace` 查詢方法
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2. 創建 trace 到 identity 的綁定查詢
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3. 創建 face 匹配查詢
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### Phase 3: 修改 Processor 層
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1. 修改 `processor.rs` - 移除 face_detections 寫入
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2. 修改 `job_worker.rs` - 移除 face_detections 查詢
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3. 修改 `workspace_sqlite.rs` - 移除 face_detections 相關函數
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4. 修改 `postgres_db.rs` - 移除 face_detections 相關函數
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### Phase 4: 修改 TKG 處理器
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1. 重構 `tkg.rs` - 使用 Qdrant workspace traces 代替 face_detections
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2. 移除 `populate_face_detections_from_face_json` 函數
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3. 修改 face 匹配邏輯
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### Phase 5: 修改 API 層
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1. 修改 `identity_api.rs` - 使用 Qdrant 查詢
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2. 修改 `identity_binding.rs` - 使用 Qdrant 綁定
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3. 修改 `identities.rs` - 使用 Qdrant 查詢
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4. 修改 `identity_agent_api.rs` - 使用 Qdrant 匹配
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5. 修改 `files.rs` - 移除 face_detections 查詢
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6. 修改 `media_api.rs` - 移除 face_detections 查詢
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### Phase 6: 修改 Chunk Ingest
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1. 修改 `trace_ingest.rs` - 使用 Qdrant traces
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2. 修改 `rule2_ingest.rs` - 使用 Qdrant traces
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### Phase 7: 測試
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1. 測試 face 追蹤
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2. 測試 identity 綁定
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3. 測試 TKG 構建
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4. 測試 API 端點
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### Phase 8: 清理
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1. 移除 face_detections 表（可選）
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2. 更新文檔
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3. 更新測試
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## 風險評估
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- **高風險**: TKG 處理器有大量 face_detections 使用
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- **中風險**: API 層需要重構查詢邏輯
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- **低風險**: Processor 層修改相對簡單
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## 預估時間
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- Phase 1-2: 2-3 小時
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- Phase 3-4: 4-6 小時
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- Phase 5-6: 3-4 小時
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- Phase 7-8: 2-3 小時
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- **總計**: 11-16 小時
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## 依賴關係
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- 需要 Qdrant workspace traces 正確填充
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- 需要 face.json 格式正確
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- 需要 SwiftFacePose 正常工作
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