181 lines
7.9 KiB
Markdown
181 lines
7.9 KiB
Markdown
# Implementation Plan: AI Model Revision (ADR-023A)
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**Branch**: `main` | **Date**: 2026-05-15 | **Spec**: [spec.md](./spec.md)
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**Feature Dir**: `specs/300-others/302-ai-model-revision/`
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---
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## Summary
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Implement ADR-023A AI Architecture Revision: เปลี่ยน model stack จาก 3-model (gemma4:9b + Typhoon + nomic-embed-text) เป็น 2-model (gemma4:e2b + nomic-embed-text), แยก BullMQ เป็น 2 queues (`ai-realtime`/`ai-batch`), เพิ่ม OCR auto-detection, enforce multi-tenant QdrantService, implement Legacy Migration pipeline และ migration_review_queue, และลบ Typhoon Cloud API ออกจาก codebase ทั้งหมด
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---
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## Technical Context
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**Language/Version**: TypeScript 5.x (strict mode)
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**Primary Dependencies**:
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- Backend: NestJS 10, BullMQ 5, TypeORM 0.3, ioredis (Redis 7), @qdrant/js-client-rest
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- AI Infrastructure: Ollama (Desk-5439), PaddleOCR, PyMuPDF (Python sidecar)
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- Queue: Redis 7 (same instance as existing BullMQ)
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**Storage**: MariaDB (existing) + Qdrant (external vector DB) + Local Storage (existing)
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**Testing**: Jest (NestJS unit/integration)
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**Target Platform**: QNAP NAS (NestJS container) + Admin Desktop Desk-5439 (Ollama)
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**Performance Goals**: ai-suggest < 30s; rag-query < 10s (p95 dequeue-to-response)
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**Constraints**: VRAM ≤ 3GB peak, concurrency=1 per queue (prevent GPU overflow)
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**Scale/Scope**: ~20,000 legacy docs (migration), ~50 new docs/day (production)
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---
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## Constitution Check
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_GATE: Must pass before Phase 0 research._
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| Rule | Status | Notes |
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|------|--------|-------|
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| ADR-019 UUID: no parseInt on UUID | ✅ PASS | BullMQ payloads ใช้ `publicId: string` เสมอ |
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| ADR-009: no TypeORM migrations | ✅ PASS | `migration_review_queue` ผ่าน SQL delta (#14) |
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| ADR-016: RBAC on all endpoints | ✅ PASS | AI endpoints จะมี CASL guard: `ai.manage` |
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| ADR-007: error handling layered | ✅ PASS | BullMQ failed jobs → dead-letter + log |
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| ADR-008: BullMQ for async | ✅ PASS | Inference ทั้งหมดผ่าน BullMQ (ไม่มี inline) |
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| ADR-023/023A: no direct Ollama | ✅ PASS | n8n → DMS API → BullMQ → Ollama เท่านั้น |
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| ADR-023A: QdrantService required projectPublicId | ✅ PASS | Enforce ที่ TypeScript compile-time |
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| TypeScript strict: no `any`, no `console.log` | ✅ PASS | Enforced ผ่าน eslint |
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| **Typhoon Cloud API removal** | ⚠️ PENDING | `rag/typhoon.service.ts` ต้อง delete (T002) |
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---
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## Project Structure
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### Documentation (this feature)
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```text
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specs/300-others/302-ai-model-revision/
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├── spec.md ✅ done
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├── plan.md ✅ this file
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├── research.md ✅ done
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├── data-model.md ✅ done
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├── quickstart.md (Phase 1)
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├── contracts/ (Phase 1)
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│ ├── ai-jobs.yaml
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│ └── migration-queue.yaml
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├── checklists/
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│ └── requirements.md ✅ done
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└── tasks.md (Phase 2 — speckit-tasks)
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```
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### Schema Delta (ADR-009)
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```text
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specs/03-Data-and-Storage/deltas/
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└── 14-add-migration-review-queue.sql # new
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```
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### Source Code
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```text
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backend/src/modules/ai/
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├── ai.module.ts # update: register 2 queues, remove Typhoon
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├── ai.controller.ts # update: add /migration/queue endpoint
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├── ai.service.ts # update: routing logic, queue selection
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├── processors/
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│ ├── ai-realtime.processor.ts # new: ai-realtime consumer
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│ └── ai-batch.processor.ts # new: ai-batch consumer (replaces existing)
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├── services/
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│ ├── ollama.service.ts # update: model → gemma4:e2b
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│ ├── qdrant.service.ts # update: enforce projectPublicId param
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│ ├── ocr.service.ts # new: OCR auto-detect + PaddleOCR routing
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│ ├── migration.service.ts # new: Legacy Migration pipeline
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│ └── embedding.service.ts # new: full-doc chunking + embed
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├── dto/
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│ ├── create-ai-job.dto.ts # update: queue discriminator field
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│ ├── migration-queue-item.dto.ts # new
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│ └── rag-query.dto.ts # new
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├── entities/
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│ └── migration-review-queue.entity.ts # new
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└── rag/
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├── rag.service.ts # update: remove typhoon ref, use QdrantService
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└── typhoon.service.ts # DELETE ← Tier 1 critical
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backend/src/config/
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└── bullmq.config.ts # update: add ai-batch queue config
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frontend/app/(dashboard)/ai-staging/
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├── page.tsx # update: add migration queue tab
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└── migration-review/
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└── page.tsx # new: Admin Migration Review UI
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frontend/components/ai/
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├── ai-suggestion-field.tsx # update: confidence threshold display
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├── migration-queue-table.tsx # new: queue list + approve/reject
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└── AiStatusBanner.tsx # update: show queue status (ai-batch paused)
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```
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---
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## Phases
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### Phase 0: Cleanup & Foundation (Tier 1 Critical First)
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**Goal**: ลบ Typhoon ออก, ตั้ง BullMQ 2-queue, สร้าง Schema Delta
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Tasks: T001–T008
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### Phase 1: Core AI Pipeline
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**Goal**: OCR auto-detect, gemma4:e2b integration, ai-suggest + embed-document flows
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Tasks: T009–T022
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### Phase 2: RAG Pipeline
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**Goal**: QdrantService multi-tenancy, chunking, rag-query endpoint
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Tasks: T023–T030
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### Phase 3: Legacy Migration Pipeline
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**Goal**: migration_review_queue, n8n API endpoint, Admin Review UI
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Tasks: T031–T042
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### Phase 4: Monitoring & Threshold Management
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**Goal**: Admin Dashboard AI metrics, threshold config, audit log delete permission
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Tasks: T043–T050
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---
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## Complexity Tracking
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| Violation | Why Needed | Simpler Alternative Rejected Because |
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|-----------|-----------|-------------------------------------|
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| 2-queue BullMQ (vs single) | RAG SLA requires isolation from batch jobs | Single queue + priority ไม่ป้องกัน long-running job block |
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| External Qdrant (vs SQL FTS) | Semantic search capability ไม่มีใน MariaDB FULLTEXT | MariaDB FTS ไม่รองรับ multilingual semantic similarity |
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| Python sidecar OCR | PaddleOCR เป็น Python library ไม่มี Node.js binding | ไม่มีทางเลือก OCR ภาษาไทยที่เทียบเท่าใน Node.js ecosystem |
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---
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## 🔗 Cross-Spec Dependencies
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### Dependencies กับ 204-rfa-approval-refactor
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| Component | Impact | Coordination |
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|-----------|--------|--------------|
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| **BullMQ Queues** | `ai-realtime` และ `ai-batch` จะถูกใช้โดย RFA Reminder/Escalation | ตรวจสอบว่า RFA jobs ไม่ชนกับ AI jobs — ใช้ queue name prefix หรือ priority |
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| **QdrantService** | อาจถูกใช้สำหรับ RFA document context | ตรวจสอบว่า RFA ใช้ projectPublicId filter ถูกต้อง |
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| **Ollama GPU** | Shared resource กับ RFA (ถ้ามี AI features) | ตรวจสอบว่าไม่มี GPU contention |
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### Shared Infrastructure
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- **BullMQ**: 2 queues (`ai-realtime`, `ai-batch`) — RFA อาจเพิ่ม `rfa-reminders` queue
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- **Redis**: ใช้ instance เดียวกัน — ตรวจสอบ memory usage
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- **Audit Logs**: ใช้ table เดียวกัน — ตรวจสอบ action types ไม่ซ้ำกัน
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### Deployment Sequence
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1. Phase 0-1 ของ AI Model Revision (BullMQ 2-queue foundation)
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2. Phase 1-3 ของ RFA Approval Refactor (ใช้ BullMQ ที่ setup แล้ว)
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3. ทดสอบ integration ก่อน deploy ทั้งสอง features พร้อมกัน
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