feat(ai-admin-console): complete implementation and resolve lint compilation errors

backend/src/modules/ai/processors/ai-batch.processor.ts

@@ -2,17 +2,30 @@
 // Change Log
 // - 2026-05-15: เพิ่ม processor สำหรับ ai-batch queue ตาม ADR-023A.
 // - 2026-05-15: เพิ่ม EmbeddingService สำหรับ embed-document logic (T022).
+// - 2026-05-21: เพิ่มการรองรับ sandbox-rag และ sandbox-extract สำหรับ Superadmin sandbox.
+// - 2026-05-21: พัฒนาระบบประมวลผล sandbox-extract พร้อมเชื่อมต่อ OcrService, OllamaService และ Redis cache
+// - 2026-05-21: แก้ไข ESLint unused variable สำหรับ parseError ใน catch block
 
 import { Processor, WorkerHost } from '@nestjs/bullmq';
 import { Logger } from '@nestjs/common';
 import { Job } from 'bullmq';
 import { InjectRepository } from '@nestjs/typeorm';
 import { Repository } from 'typeorm';
+import { InjectRedis } from '@nestjs-modules/ioredis';
+import Redis from 'ioredis';
 import { Attachment } from '../../../common/file-storage/entities/attachment.entity';
 import { QUEUE_AI_BATCH } from '../../common/constants/queue.constants';
 import { EmbeddingService } from '../services/embedding.service';
+import { AiRagService } from '../ai-rag.service';
+import { OcrService } from '../services/ocr.service';
+import { OllamaService } from '../services/ollama.service';
 
-export type AiBatchJobType = 'ocr' | 'extract-metadata' | 'embed-document';
+export type AiBatchJobType =
+  | 'ocr'
+  | 'extract-metadata'
+  | 'embed-document'
+  | 'sandbox-rag'
+  | 'sandbox-extract';
 
 export interface AiBatchJobData {
   jobType: AiBatchJobType;
@@ -27,36 +40,62 @@ export interface AiBatchJobData {
 @Processor(QUEUE_AI_BATCH, { concurrency: 1 })
 export class AiBatchProcessor extends WorkerHost {
   private readonly logger = new Logger(AiBatchProcessor.name);
+  private readonly abortControllers = new Map<string, AbortController>();
 
   constructor(
     @InjectRepository(Attachment)
     private readonly attachmentRepo: Repository<Attachment>,
-    private readonly embeddingService: EmbeddingService
+    private readonly embeddingService: EmbeddingService,
+    private readonly ragService: AiRagService,
+    private readonly ocrService: OcrService,
+    private readonly ollamaService: OllamaService,
+    @InjectRedis() private readonly redis: Redis
   ) {
     super();
   }
 
   /** Dispatch งาน batch ตาม jobType */
   async process(job: Job<AiBatchJobData>): Promise<void> {
-    await this.setAiProcessingStatus(job.data.documentPublicId, 'PROCESSING');
+    const isSandbox =
+      job.data.jobType === 'sandbox-rag' ||
+      job.data.jobType === 'sandbox-extract';
+    if (!isSandbox) {
+      await this.setAiProcessingStatus(job.data.documentPublicId, 'PROCESSING');
+    }
     try {
       switch (job.data.jobType) {
         case 'ocr':
           this.logger.log(`OCR batch job processing — jobId=${String(job.id)}`);
-          // OCR logic handled by OcrService in ai-realtime processor
-          await this.setAiProcessingStatus(job.data.documentPublicId, 'DONE');
+          if (!isSandbox) {
+            await this.setAiProcessingStatus(job.data.documentPublicId, 'DONE');
+          }
           return;
         case 'extract-metadata':
           this.logger.log(
             `Metadata extraction job processing — jobId=${String(job.id)}`
           );
-          // Metadata extraction handled in ai-realtime processor
-          await this.setAiProcessingStatus(job.data.documentPublicId, 'DONE');
+          if (!isSandbox) {
+            await this.setAiProcessingStatus(job.data.documentPublicId, 'DONE');
+          }
           return;
         case 'embed-document':
           this.logger.log(`Embedding job processing — jobId=${String(job.id)}`);
           await this.processEmbedDocument(job.data);
-          await this.setAiProcessingStatus(job.data.documentPublicId, 'DONE');
+          if (!isSandbox) {
+            await this.setAiProcessingStatus(job.data.documentPublicId, 'DONE');
+          }
+          return;
+        case 'sandbox-rag':
+          this.logger.log(
+            `Sandbox RAG job processing — jobId=${String(job.id)}`
+          );
+          await this.processSandboxRag(job.data);
+          return;
+        case 'sandbox-extract':
+          this.logger.log(
+            `Sandbox Extract job processing — jobId=${String(job.id)}`
+          );
+          await this.processSandboxExtract(job.data);
           return;
         default: {
           const unreachable: never = job.data.jobType;
@@ -70,7 +109,9 @@ export class AiBatchProcessor extends WorkerHost {
         `Batch job failed — jobType=${job.data.jobType}, documentPublicId=${job.data.documentPublicId}`,
         err instanceof Error ? err.stack : String(err)
       );
-      await this.setAiProcessingStatus(job.data.documentPublicId, 'FAILED');
+      if (!isSandbox) {
+        await this.setAiProcessingStatus(job.data.documentPublicId, 'FAILED');
+      }
       throw err;
     }
   }
@@ -80,27 +121,43 @@ export class AiBatchProcessor extends WorkerHost {
     const { documentPublicId, projectPublicId, payload } = data;
     const pdfPath = payload.pdfPath as string;
     const extractedText = payload.extractedText as string | undefined;
-
     if (!pdfPath) {
       throw new Error('pdfPath is required for embed-document job');
     }
-
     const result = await this.embeddingService.embedDocument(
       pdfPath,
       documentPublicId,
       projectPublicId,
       extractedText
     );
-
     if (!result.success) {
       throw new Error(`Embedding failed: ${result.error ?? 'Unknown error'}`);
     }
-
     this.logger.log(
       `Embedding completed for document ${documentPublicId} — ${result.chunksEmbedded} chunks embedded`
     );
   }
 
+  /** ประมวลผล sandbox RAG query */
+  private async processSandboxRag(data: AiBatchJobData): Promise<void> {
+    const { projectPublicId, idempotencyKey, payload } = data;
+    const query = payload.query as string;
+    const userPublicId = payload.userPublicId as string;
+    const controller = new AbortController();
+    this.abortControllers.set(idempotencyKey, controller);
+    try {
+      await this.ragService.processQuery(
+        idempotencyKey,
+        query,
+        projectPublicId,
+        userPublicId,
+        controller.signal
+      );
+    } finally {
+      this.abortControllers.delete(idempotencyKey);
+    }
+  }
+
   private async setAiProcessingStatus(
     documentPublicId: string,
     status: 'PENDING' | 'PROCESSING' | 'DONE' | 'FAILED'
@@ -110,4 +167,85 @@ export class AiBatchProcessor extends WorkerHost {
       { aiProcessingStatus: status }
     );
   }
+
+  /** ประมวลผล sandbox OCR + Metadata extraction โดยไม่บันทึกลง database */
+  private async processSandboxExtract(data: AiBatchJobData): Promise<void> {
+    const { idempotencyKey, payload } = data;
+    const pdfPath = payload.pdfPath as string;
+    if (!pdfPath) {
+      throw new Error('pdfPath is required for sandbox-extract job');
+    }
+    await this.redis.setex(
+      `ai:rag:result:${idempotencyKey}`,
+      3600,
+      JSON.stringify({
+        requestPublicId: idempotencyKey,
+        status: 'processing',
+      })
+    );
+    try {
+      const ocrResult = await this.ocrService.detectAndExtract({ pdfPath });
+      const prompt = `You are an expert document extraction system.
+Analyze the following OCR text extracted from a project document and extract the metadata fields.
+
+OCR TEXT:
+${ocrResult.text}
+
+Extract these fields:
+1. documentNumber: The official document number or code. If not found, return null.
+2. subject: The main subject, title, or topic of the document. If not found, return null.
+3. discipline: Must be exactly one of: "Civil", "Mechanical", "Electrical", "Architectural", or null if not specified.
+4. date: The issue date in YYYY-MM-DD format. If not found, return null.
+5. confidence: A float between 0.0 and 1.0 indicating your confidence in this extraction.
+
+Return ONLY a valid JSON object matching this schema. Do NOT include markdown code blocks, HTML, or any conversational text. Example:
+{
+  "documentNumber": "LCBP3-CIV-001",
+  "subject": "Foundation Inspection Report",
+  "discipline": "Civil",
+  "date": "2026-05-20",
+  "confidence": 0.95
+}`;
+      const response = await this.ollamaService.generate(prompt);
+      const cleanedResponse = response
+        .replace(/```json/g, '')
+        .replace(/```/g, '')
+        .trim();
+      let extractedMetadata: Record<string, unknown>;
+      try {
+        extractedMetadata = JSON.parse(cleanedResponse) as Record<
+          string,
+          unknown
+        >;
+      } catch {
+        throw new Error(
+          `Failed to parse LLM response as JSON: ${cleanedResponse}`
+        );
+      }
+      await this.redis.setex(
+        `ai:rag:result:${idempotencyKey}`,
+        3600,
+        JSON.stringify({
+          requestPublicId: idempotencyKey,
+          status: 'completed',
+          answer: JSON.stringify(extractedMetadata, null, 2),
+          completedAt: new Date().toISOString(),
+        })
+      );
+    } catch (err: unknown) {
+      const errMsg = err instanceof Error ? err.message : String(err);
+      this.logger.error(`Sandbox extract failed: ${errMsg}`);
+      await this.redis.setex(
+        `ai:rag:result:${idempotencyKey}`,
+        3600,
+        JSON.stringify({
+          requestPublicId: idempotencyKey,
+          status: 'failed',
+          errorMessage: errMsg,
+          completedAt: new Date().toISOString(),
+        })
+      );
+      throw err;
+    }
+  }
 }