lcbp3/specs/300-others/302-ai-model-revision/quickstart.md

Quickstart: AI Model Revision (ADR-023A)

Feature: 302-ai-model-revision Date: 2026-05-15

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Prerequisites

1. Desk-5439 มี Ollama พร้อมใช้งาน: ollama list ต้องแสดง gemma4:e4b และ nomic-embed-text
2. Qdrant instance running: http://QDRANT_HOST:6333/health → {"status":"ok"}
3. Redis 7 running (ใช้ instance เดิมกับ existing BullMQ)
4. @qdrant/js-client-rest ต้องติดตั้งใน backend: npm ls @qdrant/js-client-rest

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Environment Variables (เพิ่มใน backend/.env)

# AI Infrastructure
OLLAMA_HOST=http://192.168.10.XX:11434
OLLAMA_MODEL_MAIN=gemma4:e4b
OLLAMA_MODEL_EMBED=nomic-embed-text

# Qdrant
QDRANT_HOST=http://192.168.10.XX:6333
QDRANT_COLLECTION=lcbp3_documents

# OCR
OCR_CHAR_THRESHOLD=100
OCR_API_URL=http://localhost:8765   # PaddleOCR sidecar

# BullMQ Queue Names (for reference — hardcoded in code)
# ai-realtime: RAG Q&A, AI Suggest
# ai-batch: OCR, Extract, Embed

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Verification Scenarios (สำหรับ QA / UAT)

Scenario 1: Digital PDF Classification (Fast Path)

# 1. Upload a digital PDF via RFA form
# 2. Monitor BullMQ dashboard (if Bull Board installed)
# Expected: ai-realtime queue → ai-suggest job → completed within 30s
# Expected: ai-batch queue → embed-document job → completed in background
curl -H "Authorization: Bearer $TOKEN" http://localhost:3001/api/ai/jobs/status/$JOB_ID

Scenario 2: Scanned PDF (OCR Path)

# 1. Upload a scanned (image-only) PDF
# Expected: ai-batch queue → ocr job first → then ai-suggest
# OCR detection: extracted_chars < 100 per page triggers slow path

Scenario 3: RAG Query (Multi-tenancy Check)

# 1. Embed 2 docs in Project A, 1 doc in Project B
# 2. Query from Project A context
# Expected: results contain ONLY Project A docs
curl -X POST http://localhost:3001/api/ai/rag/query \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"projectPublicId":"<project-a-uuid>","question":"ค้นหา shop drawing หมายเลข SD-001"}'

Scenario 4: Legacy Migration (Admin Only)

# 1. Trigger via n8n (or direct API for testing):
curl -X POST http://localhost:3001/api/ai/migration/queue \
  -H "Authorization: Bearer $ADMIN_TOKEN" \
  -H "Idempotency-Key: SD-001-2026:batch-001" \
  -H "Content-Type: application/json" \
  -d '{"batchId":"batch-001","filename":"SD-001.pdf","tempPath":"/uploads/temp/SD-001.pdf"}'
# 2. Check migration_review_queue: status = PENDING
# 3. Admin Approve from UI → status = IMPORTED

Scenario 5: Typhoon Removal Verification

# After implementation, run:
grep -r "typhoon" backend/src --include="*.ts"
# Expected: NO results (file should be deleted)

Scenario 6: GPU Overload Prevention

# While ai-batch job is running, submit ai-realtime job
# Expected: ai-batch pauses; ai-realtime job completes; ai-batch resumes
# Observable via BullMQ dashboard or job status API

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Key Files to Modify (Priority Order)

Priority	File	Change
🔴 CRITICAL	backend/src/modules/ai/rag/typhoon.service.ts	DELETE
🔴 CRITICAL	backend/src/config/bullmq.config.ts	Add ai-batch queue
🔴 CRITICAL	specs/03-Data-and-Storage/deltas/14-add-migration-review-queue.sql	CREATE
🟡 HIGH	backend/src/modules/ai/services/qdrant.service.ts	Enforce projectPublicId param
🟡 HIGH	backend/src/modules/ai/processors/ai-batch.processor.ts	NEW — replace old processor
🟡 HIGH	backend/src/modules/ai/services/ocr.service.ts	NEW — auto-detect routing
🟢 NORMAL	frontend/app/(dashboard)/ai-staging/page.tsx	Add Migration Queue tab