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feat(ai): unify AI architecture, implement RAG and legacy migration
2026-05-15 11:10:44 +07:00

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Data Model: AI Model Revision (ADR-023A)

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


Entities & Schema Changes

1. ai_audit_logs (existing table — verify against schema)

ไม่มีการเพิ่ม column ใหม่ — ใช้ model_name column ที่มีอยู่แล้วบันทึก gemma4:e4b แทน gemma4:9b

Key fields (existing):

id               INT AUTO_INCREMENT
public_id        BINARY(16) → UUIDv7
document_id      INT FK documents.id
project_id       INT FK projects.id
job_type         VARCHAR(50)     -- 'classification', 'tagging', 'rag', 'embed'
model_name       VARCHAR(100)    -- 'gemma4:e4b', 'nomic-embed-text'
confidence_score DECIMAL(5,4)    -- 0.0000  1.0000
ai_suggestion_json   JSON
human_override_json  JSON NULL
status           ENUM('PENDING','PROCESSING','DONE','REJECTED')
created_at       DATETIME
updated_at       DATETIME

Index needed: (project_id, status, created_at) สำหรับ Admin Dashboard query


2. migration_review_queue (new table — ADR-009: SQL delta)

staging table สำหรับ Legacy Migration — เก็บ AI output รอ Admin review

-- Delta file: specs/03-Data-and-Storage/deltas/14-add-migration-review-queue.sql

CREATE TABLE migration_review_queue (
  id             INT           NOT NULL AUTO_INCREMENT,
  public_id      BINARY(16)    NOT NULL DEFAULT (UUID_TO_BIN(UUID(), TRUE)),
  batch_id       VARCHAR(100)  NOT NULL,                    -- n8n batch identifier
  idempotency_key VARCHAR(200) NOT NULL UNIQUE,             -- '<doc_number>:<batch_id>'
  original_filename VARCHAR(500) NOT NULL,
  storage_temp_path VARCHAR(1000) NOT NULL,                 -- path ใน temp storage ก่อน import
  ai_metadata_json  JSON       NOT NULL,                    -- AI suggestion (full)
  confidence_score  DECIMAL(5,4) NOT NULL,
  ocr_used          TINYINT(1)  NOT NULL DEFAULT 0,
  status         ENUM('PENDING','IMPORTED','REJECTED') NOT NULL DEFAULT 'PENDING',
  reviewed_by    INT           NULL,                        -- FK users.id (Admin who reviewed)
  reviewed_at    DATETIME      NULL,
  rejection_reason VARCHAR(500) NULL,
  created_at     DATETIME      NOT NULL DEFAULT CURRENT_TIMESTAMP,
  updated_at     DATETIME      NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
  PRIMARY KEY (id),
  UNIQUE KEY uq_idempotency (idempotency_key),
  INDEX idx_status_created (status, created_at),
  INDEX idx_batch (batch_id)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;

3. Qdrant Vector Structure (no DB change — external store)

Collection name: lcbp3_documents (shared collection, separated by payload filter)

Point structure:

{
  "id": "<uuid-v7>",
  "vector": [/* 768-dim from nomic-embed-text */],
  "payload": {
    "document_public_id": "019505a1-...",
    "project_public_id":  "019505a2-...",
    "chunk_index":        3,
    "page_number":        2,
    "chunk_text":         "...",
    "document_type":      "SHOP_DRAWING",
    "embedded_at":        "2026-05-15T10:00:00Z"
  }
}

Multi-tenancy filter (REQUIRED):

filter: {
  must: [
    { key: 'project_public_id', match: { value: projectPublicId } }
  ]
}

4. BullMQ Job Payload Interfaces

ai-realtime queue (RAG Q&A, AI Suggest):

interface AiRealtimeJobData {
  jobType: 'rag-query' | 'ai-suggest';
  documentPublicId: string;   // UUIDv7
  projectPublicId: string;    // UUIDv7 — required
  userId: number;             // INT internal ID (for audit only)
  payload: {
    // ai-suggest: { pdfPath: string; pages: 1-3 }
    // rag-query:  { question: string; topK: number }
  };
  idempotencyKey: string;
}

ai-batch queue (OCR, Extract, Embed):

interface AiBatchJobData {
  jobType: 'ocr' | 'extract-metadata' | 'embed-document';
  documentPublicId: string;   // UUIDv7
  projectPublicId: string;    // UUIDv7 — required
  payload: {
    // ocr:              { pdfPath: string }
    // extract-metadata: { textContent: string; maxPages: 3 }
    // embed-document:   { pdfPath: string; chunkSize: 512; overlap: 64 }
  };
  batchId?: string;           // สำหรับ Legacy Migration เท่านั้น
  idempotencyKey: string;
}

5. State Transitions

Document Upload Flow (new documents):

upload → temp → [ClamAV] → commit → [
  parallel:
    → ai-realtime: ai-suggest → (USER: confirm/edit) → ai_audit_logs
    → ai-batch: embed-document → Qdrant
]

Legacy Migration Flow:

n8n trigger → POST /api/ai/migration/queue → [
  ai-batch: ocr (if needed) + extract-metadata
] → migration_review_queue (PENDING)
                ↓
        Admin review (DMS UI)
                ↓
    IMPORTED → document insert → ai-batch: embed-document → Qdrant
    REJECTED → rejection_reason saved

ai-realtime ↔ ai-batch Coordination:

ai-realtime.active  → ai-batch.pause()
ai-realtime.completed/failed → ai-batch.resume()
ai-batch concurrency=1 (no parallel GPU jobs)

Schema Delta File

จะสร้างใน specs/03-Data-and-Storage/deltas/14-add-migration-review-queue.sql ตาม ADR-009