Implement custom prompt and message formatting for FAQ chatbot in main.py to enhance response accuracy and professionalism.

main.py

@@ -72,6 +72,23 @@ _ = vector_store.add_documents(documents=docs)
 
 prompt = hub.pull("rlm/rag-prompt")
 
+# Replace with custom prompt
+system_message = """You are a helpful and professional FAQ chatbot for the MLSC Coherence 25 Hackathon. Your role is to:
+1. Provide accurate and concise answers based on the provided context
+2. Be friendly but professional in tone
+3. If you don't know the answer, simply say "I don't have information about that"
+4. Keep responses brief and to the point
+5. Focus on providing factual information from the context
+6. Never mention "the provided context" or similar phrases in your responses
+7. Never explain why you don't know something - just state that you don't know
+8. Be direct and avoid unnecessary explanations"""
+
+human_message_template = """Context: {context}
+
+Question: {question}
+
+Please provide a clear and concise answer based on the context above."""
+
 class State(TypedDict):
     question: str
     context: List[Document]
@@ -83,7 +100,13 @@ def retrieve(state: State):
 
 def generate(state: State):
     docs_content = "\n\n".join(doc.page_content for doc in state["context"])
-    messages = prompt.invoke({"question": state["question"], "context": docs_content})
+    messages = [
+        SystemMessage(content=system_message),
+        HumanMessage(content=human_message_template.format(
+            context=docs_content,
+            question=state["question"]
+        ))
+    ]
     print(messages)
     response = llm.invoke(messages)
     return {"answer": response.content}