.env.example

@@ -15,3 +15,7 @@ AI_SERVICE_LLM_MODEL=gpt-4o-mini
 # Ollama Configuration (for embedding model)
 AI_SERVICE_OLLAMA_BASE_URL=http://ollama:11434
 AI_SERVICE_OLLAMA_EMBEDDING_MODEL=nomic-embed-text
+
+# Frontend API Key (required for admin panel authentication)
+# Get this key from the backend logs after first startup, or from /admin/api-keys
+VITE_APP_API_KEY=your-frontend-api-key-here

ai-service-admin/Dockerfile

@@ -3,6 +3,12 @@ FROM docker.1ms.run/node:20-alpine AS builder
 
 WORKDIR /app
 
+ARG VITE_APP_API_KEY
+ARG VITE_APP_BASE_API=/api
+
+ENV VITE_APP_API_KEY=$VITE_APP_API_KEY
+ENV VITE_APP_BASE_API=$VITE_APP_BASE_API
+
 COPY package*.json ./
 
 RUN npm install && npm install @rollup/rollup-linux-x64-musl --save-optional

ai-service/app/services/retrieval/optimized_retriever.py

@@ -396,42 +396,32 @@ class OptimizedRetriever(BaseRetriever):
     ) -> list[dict[str, Any]]:
         """Search using specified vector dimension."""
         try:
-            qdrant = await client.get_client()
-            collection_name = client.get_collection_name(tenant_id)
-            
             logger.info(
-                f"[RAG-OPT] Searching collection={collection_name}, "
-                f"vector_name={vector_name}, limit={limit}, vector_dim={len(query_vector)}"
+                f"[RAG-OPT] Searching with vector_name={vector_name}, "
+                f"limit={limit}, vector_dim={len(query_vector)}"
             )
             
-            results = await qdrant.search(
-                collection_name=collection_name,
-                query_vector=(vector_name, query_vector),
+            results = await client.search(
+                tenant_id=tenant_id,
+                query_vector=query_vector,
                 limit=limit,
+                vector_name=vector_name,
             )
             
             logger.info(
-                f"[RAG-OPT] Search returned {len(results)} results from collection={collection_name}"
+                f"[RAG-OPT] Search returned {len(results)} results"
             )
             
             if len(results) > 0:
                 for i, r in enumerate(results[:3]):
                     logger.debug(
-                        f"[RAG-OPT] Result {i+1}: id={r.id}, score={r.score:.4f}"
+                        f"[RAG-OPT] Result {i+1}: id={r['id']}, score={r['score']:.4f}"
                     )
             
-            return [
-                {
-                    "id": str(result.id),
-                    "score": result.score,
-                    "payload": result.payload or {},
-                }
-                for result in results
-            ]
+            return results
         except Exception as e:
             logger.error(
-                f"[RAG-OPT] Search with {vector_name} failed: {e}, "
-                f"collection_name={client.get_collection_name(tenant_id)}",
+                f"[RAG-OPT] Search with {vector_name} failed: {e}",
                 exc_info=True
             )
             return []

docker-compose.yaml

@@ -35,6 +35,9 @@ services:
     build:
       context: ./ai-service-admin
       dockerfile: Dockerfile
+      args:
+        VITE_APP_API_KEY: ${VITE_APP_API_KEY:-}
+        VITE_APP_BASE_API: /api
     container_name: ai-service-admin
     restart: unless-stopped
     ports: