🏗️ Architecture: How should MongoDB data architecture be analyzed and improve...

Date: 2026-02-22
Time: 01:15:21 UTC
Session: gspace1-133.23
Agent: backend
Confidence: 90%
Status: ✅ Documented

📋 Decision Context

Question:

How should MongoDB data architecture be analyzed and improved across backend-ai-api, backend-api, and backend-vitto-api?

Task: Not specified

🎯 Decision Made

Chosen:

Comprehensive analysis document + targeted fixes for broken pipelines rather than database consolidation

Options Considered:

• No alternatives documented

💭 Reasoning

Queried all 3 remote Atlas clusters to get actual data state. Found: ai-memory (189MB) separation is justified, OutcomeDetector IS working (440 auto-detected outcomes) but window too narrow, pattern mining IS working (563 patterns in learned_patterns), 2031 proactive alerts need noise reduction. Fixed: expanded outcome detection window from 7→30 days, added backfill endpoint, fixed corrupted pending-events.json, added auto-resolution for recovered checks, added recordUserInteraction to MindService, added diagnostic logging for episode storage.

📁 Files Involved

• docs/english/architecture/mongodb-data-architecture-analysis.md
• apis/backend-ai-api/src/app/learning/services/outcome-detector.service.ts
• apis/backend-ai-api/src/app/learning/controllers/health.controller.ts
• apis/backend-ai-api/src/app/infrastructure/services/decision.service.ts
• apis/backend-vitto-api/src/app/proactive/proactive-monitor.service.ts
• apis/backend-vitto-api/src/app/mind/mind.service.ts

🔗 Related

• Decision ID: dev_1771722921313_k2cy406
• Session: 133
• Workspace: gspace1

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