97 lines
3.9 KiB
Markdown
97 lines
3.9 KiB
Markdown
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# VIMS Core Integration Report
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## Test Results
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**Date:** 2026-07-11
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**Status:** ✅ ALL TESTS PASSED (9/9)
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### Test Coverage
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| Test | Status |
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|------|--------|
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| All instances exist | ✅ PASS |
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| Instance structure valid | ✅ PASS |
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| Detector files present | ✅ PASS |
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| Database creation | ✅ PASS |
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| Config validity | ✅ PASS |
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| Article links | ✅ PASS |
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| ATM database setup | ✅ PASS |
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| ATM detector initialization | ✅ PASS |
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| API endpoints | ✅ PASS |
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## Instances Created (20 total)
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### ATM Monitoring (separate system)
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- `atm-monitoring` — ATM Anomaly Detection (14 classes)
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### VIMS Core Instances (19)
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| # | Instance | Article | Classes |
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|---|----------|---------|---------|
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| 1 | street-lighting | Evidence-Driven Municipal Maintenance | 4 |
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| 2 | bridge-inspection | Bridge Inspection Software Comparison | 5 |
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| 3 | retail-analytics | Retail Site Selection Data | 5 |
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| 4 | insurance-risk | Pre-Loss Surveys Insurance | 5 |
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| 5 | municipal-maintenance | Evidence-Driven Municipal Maintenance | 6 |
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| 6 | construction-site | Future of Infrastructure Monitoring | 5 |
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| 7 | real-estate-condition | Real Estate Due Diligence | 5 |
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| 8 | urban-decay | How to Measure Urban Decay | 5 |
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| 9 | crowdsourced-verification | Ground Truth Verification | 5 |
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| 10 | data-quality | Can You Trust Crowdsourced Data? | 5 |
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| 11 | insurance-contradiction | Insurance Contradiction Analysis | 5 |
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| 12 | continuous-monitoring | Continuous vs Periodic Inspection | 5 |
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| 13 | decision-intelligence | Decision-First Intelligence | 5 |
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| 14 | satellite-validation | Field Intelligence vs Satellite | 5 |
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| 15 | cost-stale-data | Calculate Cost of Stale Data | 5 |
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| 16 | contradiction-gap | The Contradiction Gap | 5 |
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| 17 | consensus-engine | How the Consensus Engine Works | 5 |
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| 18 | official-statistics | The Problem with Official Statistics | 5 |
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| 19 | preventive-maintenance | Economics of Preventive Maintenance | 5 |
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## Architecture
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```
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┌─────────────────────────────────────────┐
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│ VIMS Core Framework │
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├─────────────────────────────────────────┤
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│ Core: │
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│ • base_detector.py (abstract base) │
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│ • database.py (SQLite/PostgreSQL) │
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│ • api_base.py (FastAPI router) │
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├─────────────────────────────────────────┤
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│ Instances: │
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│ • 19 topic-specific detectors │
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│ • 19 databases (SQLite) │
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│ • 19 API endpoints │
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├─────────────────────────────────────────┤
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│ ATM System (separate): │
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│ • Full FastAPI server │
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│ • WebSocket alerts │
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│ • Dashboard (HTML/JS) │
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│ • Docker + docker-compose │
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│ • CI/CD (GitHub Actions) │
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└─────────────────────────────────────────┘
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```
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## Next Steps
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1. **Train models** — Add training images to each instance's `data/raw/`
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2. **Deploy** — Run `docker-compose up` in atm-anomaly-detection/
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3. **Scale** — Create more instances with `scripts/create_instance.py`
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## Commands
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```bash
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# Run all tests
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cd vims-core && python3 -m pytest tests/ -v
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# Create new instance
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python3 vims-core/scripts/create_instance.py \
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--name "new-topic" \
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--display-name "New Topic Monitoring" \
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--classes "class1,class2,class3" \
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--article-url "/insights/article-slug/"
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# Start ATM system
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cd atm-anomaly-detection && docker-compose up
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```
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