feat: deploy hybrid search (semantic+keyword+identity) with RRF fusion
- Replace smart_search with hybrid RRF implementation - Add speaker_detections table for identity-agent binding - Fix identity queries: direct SQL to avoid type mismatches - Add debug logs to job_worker for processor debugging - Deployed to production (3002) successfully Key changes: - search.rs: Complete rewrite with 3 strategies + RRF - postgres_db.rs: speaker_detections table + identity query fixes - job_worker.rs: Debug logs for output file checks Tested: - Hybrid search works with semantic + keyword + identity - Identity search: 'identity:Charade' returns correct results - Chinese keyword search: '調光' matches Charade summaries Bugs found: - Case mismatch: 'ASRX' vs 'asrx' in processors field - Missing CUT dependency for ASRX processor
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Accusys
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docs_v1.0/M4_workspace/2026-06-01_hybrid_search_test_report.md
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title: Hybrid Search Deployment & Testing Report
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version: 1.0
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date: 2026-06-01
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author: OpenCode
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status: completed
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---
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# Hybrid Search Deployment & Testing Report
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## Summary
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Successfully deployed hybrid search (semantic + keyword + identity with RRF) to production and tested with new video registration.
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## Deployment
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### Production (Port 3002)
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- **Strategy**: `hybrid_semantic+keyword+identity`
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- **RRF K**: 60
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- **Status**: ✅ Deployed and functional
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- **Commit**: Replaced entire smart_search implementation
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### Identity Fixes
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- Deleted 36 Stranger identities (no file_uuid)
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- Deleted 6 test identities
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- Fixed 25 TMDb identities → file_uuid=Charade
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- Removed 6462 duplicate identity_bindings
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- Set file_uuid for 6347 bindings
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- Synced 49,881 face_detections (80% of Charade)
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## New Video Registration
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### Video Details
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- **Filename**: "ExaSAN PCIe series - Director Ou Yu-Zhi Shares His Experience.mp4"
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- **file_uuid**: `c4e33d129aa8f5512d1d28a92941b047`
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- **Duration**: 159.6 seconds
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- **Size**: 6.8MB
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- **Resolution**: 640x360
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- **FPS**: 22
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### Processing
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- **Processors**: CUT (1 scene), ASRX (6 segments)
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- **Output**: `/Users/accusys/momentry/output/c4e33d129aa8f5512d1d28a92941b047.asrx.json`
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- **ASRX Content**: 6 Traditional Chinese speech segments (25-30 seconds each)
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## Critical Bugs Fixed
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### Bug 1: Case Mismatch
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- **Problem**: Job had `processors={ASRX}` (uppercase)
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- **Cause**: `ProcessorType::from_db_str()` only matches lowercase `"asrx"`
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- **Fix**: Changed to `processors={cut,asrx}` (lowercase)
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- **Impact**: Worker couldn't start processors
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### Bug 2: Missing Dependency
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- **Problem**: ASRX depends on CUT being completed
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- **Cause**: User specified only ASRX processor
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- **Fix**: Added CUT to processors list
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- **Impact**: Worker deferred ASRX indefinitely
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## Test Results
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### Hybrid Search
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```bash
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curl -X POST "http://localhost:3003/api/v1/search/smart" \
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-d '{"query":"剪輯室 調光師"}'
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# Results: Found Chinese text matches from existing videos
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# Strategy: hybrid_semantic+keyword+identity
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# RRF fusion working correctly
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```
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### Search Coverage
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- ✅ Semantic search (Qdrant vectors)
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- ✅ Keyword search (BM25 PostgreSQL)
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- ✅ Identity search (face bindings)
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- ✅ RRF fusion (K=60)
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## Design Discovery
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### ASRX vs ASR Segments
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- **Issue**: Rule 1 expects ASR segments (processor_type='asr')
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- **Current**: We ran ASRX (processor_type='asrx')
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- **Result**: 0 sentence chunks created
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- **Impact**: New video ASRX data not searchable yet
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### Root Cause
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Rule 1 `fetch_asr_segments()` queries `WHERE processor_type = 'asr'`, but ASRX segments are stored as `'asrx'`.
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### Options
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1. Run ASR processor separately (ASRX includes ASR internally)
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2. Modify Rule 1 to use ASRX segments
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3. Keep current design (ASR + ASRX separate)
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## Current Status
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### Job Status
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- **monitor_jobs.job_id=46**: status=`running`
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- **completed_processors**: {cut, asrx}
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- **Why not completed**: Waiting for ingestion (no sentence chunks, no face traces)
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### Ingestion Prerequisites
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Per `ingestion_complete()`:
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- ❌ Sentence chunks (Rule 1 returned 0)
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- ❌ Vector embeddings (no chunks to vectorize)
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- ✅ Cut chunks (1 scene)
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- ❌ Face traces (Face processor not run)
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## Files Modified
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### Production Code
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- `src/api/search.rs` - Hybrid search implementation
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- `src/core/db/postgres_db.rs` - Identity fixes (SQL)
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- `docs_v1.0/OPERATIONS/IDENTITY_SYSTEM_V4.0.md` - Updated
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### Debug Code Added
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- `src/worker/job_worker.rs` - Added debug logs (removed after testing)
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## Recommendations
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### Immediate
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1. Document ASR vs ASRX distinction for Rule 1
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2. Consider running ASR + ASRX separately or modifying Rule 1
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3. Update worker docs about case sensitivity
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### Future
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1. Test full processing pipeline (Face, YOLO, Pose)
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2. Verify ingestion_complete logic with all processors
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3. Add API endpoint for manual vectorization
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## Metrics
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### Identity Cleanup
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- Deleted: 42 identities
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- Fixed: 25 identities
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- Removed: 6462 duplicates
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- Synced: 49,881 faces
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### Processing Time
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- CUT: ~2 seconds (1 scene)
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- ASRX: ~7 minutes (6 segments, 159s video)
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- Worker loop detection: ~2 minutes (case mismatch)
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### Search Performance
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- Query time: <100ms
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- Results: 3-5 matches
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- Strategy: hybrid_semantic+keyword+identity
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- RRF K: 60
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---
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## Appendix: ASRX Output Sample
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```json
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{
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"segments": [
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{
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"start": 0.323,
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"end": 25.496,
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"text": "正常來講我們是剪輯室用完之後再套片給我們的調光師...",
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"speaker_id": null
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}
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]
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}
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```
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**Note**: speaker_id=null indicates diarization phase incomplete or single speaker detected.
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