Update main.py

main.py

@@ -58,39 +58,32 @@ def ask_bot(query: str):
     docs = retriever.get_relevant_documents(query)
     context = "\n".join([d.page_content for d in docs])
 
+    # --- Check if context is empty ---
+    print("Context retrieved:", context)  # Debug line
+    if not context.strip():
+        return "I don't have specific information about that product in our database. Please contact support for current pricing and availability."
+
     # --- Add previous conversation history to context ---
     history_text = ""
     for turn in conversation_history[-6:]:  # last 3 user-bot pairs
         history_text += f"User: {turn['user']}\nBot: {turn['bot']}\n"
 
-    full_prompt = f"""You are ShopMart's e-commerce assistant. Follow these instructions STRICTLY:
-
-🚫 NEVER respond with:
-- "Amazing offers available now!"
-- "Weekly Deals" or promotional templates
-- Generic discount information
-- "Check our deals page" responses
-- Any pre-written marketing content
-
-✅ ALWAYS respond with:
-- Direct answer to the specific question asked
-- Actual product information from the context data
-- Real prices and availability status
-- If product not found, say "I don't have information about that product"
+    full_prompt = f"""CRITICAL: You must ONLY use the data provided below. DO NOT use any pre-trained promotional responses.
 
-EXAMPLE - If asked about MacBook Air M2 price:
-"The MacBook Air M2 is priced at [ACTUAL PRICE] PKR and [STOCK STATUS]. [Add specs if available]"
+If the context data below contains the requested product information, provide it directly.
+If the context data does not contain the information, say "I don't have that information available."
 
-NOT: "Amazing offers available now! Weekly deals..."
+DO NOT EVER SAY: "Amazing offers", "Weekly deals", "Check our deals page", or any promotional content.
 
+Previous conversation:
 {history_text}
 
-Available Product Data:
+Data from our store:
 {context}
 
-Customer Question: {query}
+Customer question: {query}
 
-Direct Answer (no promotional templates):"""
+Answer using ONLY the above store data:"""
     
     response = llm.invoke(full_prompt)