fix

DSL/Ruuter.private/rag-search/POST/inference/production.yml

@@ -85,10 +85,15 @@ call_orchestrate_endpoint:
     headers:
       Content-Type: "application/json"
   result: orchestrate_result
+  next: assign_response
+
+assign_response:
+  assign:
+    response: "${orchestrate_result.response.body}"
   next: return_orchestrate_response
 
 return_orchestrate_response:
-  return: ${orchestrate_result.response.body}
+  return: ${response}
   next: end
 
 assign_disconnected_response:

DSL/Ruuter.private/rag-search/POST/inference/test.yml

@@ -24,8 +24,9 @@ extract_request_data:
 get_connection:
   call: http.post
   args:
-    url: "[#RAG_SEARCH_RESQL]/get-production-connection"
-    body: {}
+    url: "[#RAG_SEARCH_RESQL]/get-llm-connection"
+    body: 
+      connection_id: ${connectionId}
   result: connection_result
   next: validate_connection
 
@@ -57,18 +58,23 @@ validate_budget_status:
 call_orchestrate_endpoint:
   call: http.post
   args:
-    url: "[#RAG_SEARCH_LLM_ORCHESTRATOR]"
+    url: "[#RAG_SEARCH_LLM_ORCHESTRATOR]/test"
     body:
       connectionId: ${connectionId}
       message: ${message}
       environment: "test"
     headers:
       Content-Type: "application/json"
   result: orchestrate_result
+  next: assign_response
+
+assign_response:
+  assign:
+    response: "${orchestrate_result.response.body}"
   next: return_orchestrate_response
 
 return_orchestrate_response:
-  return: ${orchestrate_result.response.body}
+  return: ${response}
   next: end
 
 assign_disconnected_response:

DSL/Ruuter.private/rag-search/POST/llm-connections/add.yml

@@ -140,10 +140,19 @@ add_llm_connection:
       access_key: ${access_key}
       embedding_model_api_key: ${embedding_model_api_key}
   result: connection_result
+  next: assign_connection_response
+
+assign_connection_response:
+  assign:
+    response: {
+      id: "${connection_result.response.body[0].id}",
+      status: 201,
+      operationSuccess: true
+    }
   next: return_success
 
 return_success:
-  return: "LLM connection added successfully"
+  return: ${response}
   status: 200
   next: end
 

GUI/src/services/inference.ts

@@ -16,8 +16,8 @@ export interface InferenceResponse {
 }
 
 export async function viewInferenceResult(request: InferenceRequest): Promise<InferenceResponse> {
-  const { data } = await apiDev.post(inferenceEndpoints.VIEW_INFERENCE_RESULT(), {
-    llmConnectionId: request.llmConnectionId,
+  const { data } = await apiDev.post(inferenceEndpoints.VIEW_TEST_INFERENCE_RESULT(), {
+    connectionId: request.llmConnectionId,
     message: request.message,
   });
   return data;

GUI/src/utils/endpoints.ts

@@ -25,5 +25,5 @@ export const llmConnectionsEndpoints = {
 }
 
 export const inferenceEndpoints = {
-  VIEW_INFERENCE_RESULT: (): string => `/rag-search/inference/results/view`,
+  VIEW_TEST_INFERENCE_RESULT: (): string => `/rag-search/inference/test`,
 }

docker-compose.yml

@@ -142,7 +142,7 @@ services:
       - NODE_ENV=development
       - REACT_APP_RUUTER_API_URL=http://localhost:8086
       - REACT_APP_RUUTER_PRIVATE_API_URL=http://localhost:8088  
-      - REACT_APP_CUSTOMER_SERVICE_LOGIN=http://localhost:3004
+      - REACT_APP_CUSTOMER_SERVICE_LOGIN=http://localhost:3004/et/dev-auth
       - REACT_APP_CSP=upgrade-insecure-requests; default-src 'self'; font-src 'self' data:; img-src 'self' data:; script-src 'self' 'unsafe-eval' 'unsafe-inline'; style-src 'self' 'unsafe-inline'; object-src 'none'; connect-src 'self' http://localhost:8086 http://localhost:8088 http://localhost:3004 http://localhost:3005 ws://localhost;
       - DEBUG_ENABLED=true
       - CHOKIDAR_USEPOLLING=true
@@ -419,7 +419,7 @@ services:
       - ./vault/config:/vault/config          # contains vault.hcl
       - ./vault/logs:/vault/logs
     expose:
-      - "8200"
+    - "8200"
     networks:
       - bykstack
     restart: unless-stopped

run_vector_indexer.py

@@ -0,0 +1,179 @@
+#!/usr/bin/env python3
+"""
+Entry point script for Vector Indexer - Contextual Retrieval Pipeline
+
+This script can be run directly or called by cron jobs for automated processing.
+
+Usage:
+    python run_vector_indexer.py [--config CONFIG_PATH] [--health-check] [--dry-run]
+
+Examples:
+    # Run with default config
+    python run_vector_indexer.py
+
+    # Run with custom config
+    python run_vector_indexer.py --config /path/to/config.yaml
+
+    # Health check only
+    python run_vector_indexer.py --health-check
+
+    # Dry run (validate without processing)
+    python run_vector_indexer.py --dry-run
+"""
+
+import argparse
+import asyncio
+import sys
+from pathlib import Path
+
+# Add src to Python path
+sys.path.insert(0, str(Path(__file__).parent / "src"))
+
+from src.vector_indexer.main_indexer import VectorIndexer
+
+
+async def main():
+    """Main entry point with command line argument parsing."""
+
+    parser = argparse.ArgumentParser(
+        description="Vector Indexer - Contextual Retrieval Pipeline",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+
+    parser.add_argument(
+        "--config",
+        type=str,
+        default="src/vector_indexer/config/vector_indexer_config.yaml",
+        help="Path to configuration file (default: src/vector_indexer/config/vector_indexer_config.yaml)",
+    )
+
+    parser.add_argument(
+        "--health-check", action="store_true", help="Run health check only and exit"
+    )
+
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Validate configuration and connectivity without processing documents",
+    )
+
+    parser.add_argument(
+        "--verbose", "-v", action="store_true", help="Enable verbose logging"
+    )
+
+    parser.add_argument(
+        "--quiet", "-q", action="store_true", help="Suppress non-error output"
+    )
+
+    args = parser.parse_args()
+
+    # Configure logging level based on arguments
+    log_level = "INFO"
+    if args.verbose:
+        log_level = "DEBUG"
+    elif args.quiet:
+        log_level = "ERROR"
+
+    try:
+        # Initialize vector indexer with specified config
+        indexer = VectorIndexer(config_path=args.config)
+
+        if args.health_check:
+            # Health check only
+            print("🔍 Running health check...")
+            health_ok = await indexer.run_health_check()
+
+            if health_ok:
+                print("✅ Health check passed!")
+                return 0
+            else:
+                print("❌ Health check failed!")
+                return 1
+
+        elif args.dry_run:
+            # Dry run - validate without processing
+            print("🧪 Running dry run validation...")
+
+            health_ok = await indexer.run_health_check()
+            if not health_ok:
+                print("❌ Validation failed!")
+                return 1
+
+            # Discover documents but don't process
+            documents = indexer.document_loader.discover_all_documents()
+            print(f"📄 Found {len(documents)} documents ready for processing")
+            print("✅ Dry run validation passed!")
+            return 0
+
+        else:
+            # Full processing run
+            print("🚀 Starting Vector Indexer processing...")
+
+            # Health check first
+            health_ok = await indexer.run_health_check()
+            if not health_ok:
+                print("❌ Pre-processing health check failed!")
+                return 1
+
+            # Process all documents
+            stats = await indexer.process_all_documents()
+
+            # Return appropriate exit code
+            if stats.documents_failed > 0:
+                print(f"⚠️  Processing completed with {stats.documents_failed} failures")
+                return 2  # Partial success
+            else:
+                print("✅ Processing completed successfully!")
+                return 0
+
+    except KeyboardInterrupt:
+        print("\n⏹️  Processing interrupted by user")
+        return 130
+    except FileNotFoundError as e:
+        print(f"❌ Configuration file not found: {e}")
+        return 1
+    except Exception as e:
+        print(f"💥 Fatal error: {e}")
+        return 1
+
+
+def cron_entry_point():
+    """
+    Entry point specifically designed for cron jobs.
+
+    This function:
+    - Uses minimal output suitable for cron logs
+    - Returns appropriate exit codes for monitoring
+    - Handles errors gracefully for automated systems
+    """
+    import logging
+
+    # Configure minimal logging for cron
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s - Vector Indexer - %(levelname)s - %(message)s",
+    )
+
+    try:
+        # Run with default configuration
+        result = asyncio.run(main())
+
+        if result == 0:
+            logging.info("Vector indexer completed successfully")
+        elif result == 2:
+            logging.warning("Vector indexer completed with some failures")
+        else:
+            logging.error("Vector indexer failed")
+
+        return result
+
+    except Exception as e:
+        logging.error(f"Vector indexer fatal error: {e}")
+        return 1
+
+
+if __name__ == "__main__":
+    # Run the async main function
+    exit_code = asyncio.run(main())
+    sys.exit(exit_code)