Update tools.py

tools.py

@@ -3,6 +3,7 @@
 from smolagents import tool
 from retriever import retrieve_relevant_context
 
+
 def clean_answer_with_prompt(agent_output: str) -> str:
     """
     Extracts and cleans the final answer from the agent output.
@@ -12,6 +13,18 @@ def clean_answer_with_prompt(agent_output: str) -> str:
         return agent_output.split("FINAL ANSWER:")[-1].strip()
     return agent_output.strip()
 
+def build_prompt(question: str, context: str) -> str:
+    """
+    Combine the system instruction, context, and question to build the LLM prompt.
+    """
+    system_instruction = (
+        "You are an intelligent assistant helping answer complex real-world questions. "
+        "Use the provided context to reason and provide a concise factual answer. "
+        "Only answer what is asked. Do not include 'FINAL ANSWER:' or extra explanation.\n\n"
+    )
+    return f"{system_instruction}Context:\n{context}\n\nQuestion: {question}\nAnswer:"
+
+
 @tool
 def guest_info_tool(name: str) -> str:
     """
@@ -38,18 +51,4 @@ def greeting_tool(name: str) -> str:
     """
     return f"Welcome to the gala, {name}! We're honored to have you with us."
 
-def build_prompt(question: str, context: str = "") -> str:
-    """
-    Constructs a prompt for the agent using a question and optional context.
-
-    Args:
-        question (str): The user query to be answered.
-        context (str): Optional supporting context or retrieved documents.
 
-    Returns:
-        str: A formatted prompt for the LLM.
-    """
-    if context:
-        return f"Here is some context: {context}\n\nQuestion: {question}\n\nAnswer:"
-    else:
-        return f"Question: {question}\n\nAnswer:"