Update app.py

app.py

@@ -67,7 +67,7 @@ with st.sidebar:
 # Store LLM generated responses
 if "messages" not in st.session_state:
     st.session_state.chain = init_chain()
-    st.session_state.messages = [{"role": "assistant", "content": "How may I help you today?"}]
+    st.session_state.messages = [{"role": "assistant", "content": "Hello! I am your Conversational Agent for the Citizens Charter of Camarines Sur Polytechnic Colleges (CSPC). How may I assist you today?"}]
     st.session_state.query_counter = 0  # Track the number of user queries
     st.session_state.conversation_history = ""  # Keep track of history for the LLM
 
@@ -79,6 +79,12 @@ def generate_response(prompt_input):
         
         # Format the input for the chain with the retrieved context
         formatted_input = (
+            f"You are a Conversational Agent for the Citizens Charter of Camarines Sur Polytechnic Colleges (CSPC). "
+            f"Your purpose is to provide accurate and helpful information about CSPC's policies, procedures, and services as outlined in the Citizens Charter. "
+            f"When responding to user queries:\n"
+            f"1. Always prioritize information from the provided context (Citizens Charter or other CSPC resources).\n"
+            f"2. Be concise, clear, and professional in your responses.\n"
+            f"3. If the user's question is outside the scope of the Citizens Charter, politely inform them and suggest relevant resources or departments they can contact.\n\n"
             f"Context:\n"
             f"{' '.join([doc.page_content for doc in relevant_context])}\n\n"
             f"Conversation:\n{st.session_state.conversation_history}user: {prompt_input}\n"