feat: Implement optimized agentic RAG processor with vector embeddings and LLM analysis

- Add LLM analysis integration to optimized agentic RAG processor
- Fix strategy routing in job queue service to use configured processing strategy
- Update ProcessingResult interface to include LLM analysis results
- Integrate vector database operations with semantic chunking
- Add comprehensive CIM review generation with proper error handling
- Fix TypeScript errors and improve type safety
- Ensure complete pipeline from upload to final analysis output

The optimized agentic RAG processor now:
- Creates intelligent semantic chunks with metadata enrichment
- Generates vector embeddings for all chunks
- Stores chunks in pgvector database with optimized batching
- Runs LLM analysis to generate comprehensive CIM reviews
- Provides complete integration from upload to final output

Tested successfully with STAX CIM document processing.

backend/src/services/jobQueueService.ts

@@ -1,5 +1,6 @@
 import { EventEmitter } from 'events';
 import { logger } from '../utils/logger';
+import { config } from '../config/env';
 import { ProcessingOptions } from './documentProcessingService';
 import { unifiedDocumentProcessor } from './unifiedDocumentProcessor';
 
@@ -209,8 +210,8 @@ class JobQueueService extends EventEmitter {
     await this.updateJobStatus(job.id, 'processing');
 
     // Use unified processor for strategy-aware processing
-    const strategy = options?.strategy || 'chunking';
-    logger.info('Processing document job with strategy', { documentId, strategy, jobId: job.id });
+    const strategy = options?.strategy || config.processingStrategy;
+    logger.info('Processing document job with strategy', { documentId, strategy, jobId: job.id, configStrategy: config.processingStrategy });
 
     const result = await unifiedDocumentProcessor.processDocument(
       documentId,