Chore: Bux fixes on accessing collection

app.py

@@ -195,6 +195,11 @@ class RAGService:
     async def retrieve_relevant_chunks(query: str, top_k: int = Config.TOP_K) -> List[str]:
         """Retrieve relevant document chunks from Qdrant"""
         try:
+            # Check if embedding service is initialized
+            if embedding_service is None:
+                print("Error: Embedding service is not initialized")
+                return []
+            
             # Get query embedding - all-MiniLM works well without special prefixes
             query_embedding = await embedding_service.get_query_embedding(query)
             
@@ -255,10 +260,16 @@ async def health_check():
         qdrant_status = f"error: {str(e)}"
     
     # Test embedding service
-    embedding_health = embedding_service.health_check()
+    if embedding_service is None:
+        embedding_health = {"status": "not_initialized", "error": "EmbeddingService is None"}
+    else:
+        try:
+            embedding_health = embedding_service.health_check()
+        except Exception as e:
+            embedding_health = {"status": "error", "error": str(e)}
     
     return {
-        "status": "healthy",
+        "status": "healthy" if embedding_service is not None else "unhealthy",
         "groq": "connected" if groq_client else "not configured",
         "qdrant": qdrant_status,
         "embedding_service": embedding_health,
@@ -402,11 +413,14 @@ async def stream_chat_completion(messages: List[Dict], request: ChatCompletionRe
         }
         yield f"data: {json.dumps(error_chunk)}\n\n"
 
-# Additional endpoints for managing the vector database
 @app.post("/v1/embeddings/add")
 async def add_document(content: str, metadata: Optional[Dict] = None):
     """Add a document to the vector database"""
     try:
+        # Check if embedding service is initialized
+        if embedding_service is None:
+            raise HTTPException(status_code=500, detail="Embedding service is not initialized")
+        
         # Generate embedding for document
         embedding = await embedding_service.get_document_embedding(content)
         
@@ -436,6 +450,10 @@ async def add_document(content: str, metadata: Optional[Dict] = None):
 async def batch_add_documents(documents: List[Dict[str, Any]]):
     """Add multiple documents to the vector database"""
     try:
+        # Check if embedding service is initialized
+        if embedding_service is None:
+            raise HTTPException(status_code=500, detail="Embedding service is not initialized")
+        
         # Extract texts and metadata
         texts = [doc.get("content", "") for doc in documents]
         metadatas = [doc.get("metadata", {}) for doc in documents]
@@ -475,6 +493,10 @@ async def batch_add_documents(documents: List[Dict[str, Any]]):
 async def create_collection():
     """Create a new collection in Qdrant with the correct vector size"""
     try:
+        # Check if embedding service is initialized
+        if embedding_service is None:
+            raise HTTPException(status_code=500, detail="Embedding service is not initialized")
+        
         from qdrant_client.models import VectorParams, Distance
         
         await qdrant_client.create_collection(