Update app.py

app.py

@@ -54,7 +54,7 @@ def preprocess_query(query):
 # Function to create RAG chain with Groq
 def create_rag_chain(vector_store):
     prompt = ChatPromptTemplate.from_messages([
-        ("system", "You are an AI assistant helping with data protection and regulation compliance related queries. Use the following context to answer the user's question:\n\n{context}"),
+        ("system", "You are an AI assistant helping with data protection and regulation compliance related queries. Use the following passages of official regulation documents to provide practical advice on how to meet regulatory requirements in the context of the user question:\n\n{context}"),
         ("human", "{input}")
     ])
     document_chain = create_stuff_documents_chain(groq_client, prompt)
@@ -63,7 +63,7 @@ def create_rag_chain(vector_store):
 # Function for Gemini response with long context
 def gemini_response(query, full_pdf_content):
     prompt = ChatPromptTemplate.from_messages([
-        ("system", "You are an AI assistant helping with data protection and regulation compliance related queries.. Use the following full content of official regulation documents to answer the user's question:\n\n{context}"),
+        ("system", "You are an AI assistant helping with data protection and regulation compliance related queries. Use the following full content of official regulation documents to provide practical advice on how to meet regulatory requirements in the context of the user question:\n\n{context}"),
         ("human", "{input}")
     ])
     chain = prompt | gemini_client
@@ -78,7 +78,7 @@ def generate_final_response(query, response1, response2):
     2. Think of a comprehensive answer that combines the strengths of both responses.
     3. If the responses contradict each other, highlight this and if it might indicate a hallucination.
     [Output]
-    4. Provide practical advice on how to meet regulatory requirements in the context of the user question based on the information given.
+    4. Provide practical advice on how to meet regulatory requirements in the context of the user question based on Steps 1, 2, and 3.
 
     User Query: {query}