update prompt

agent.py

@@ -80,6 +80,14 @@ def web_search(keywords: str, max_results:int = 5) -> str:
 #     return ''
 
 
+system_prompt = """
+You are a helpful assistant tasked with answering questions using a set of tools. 
+Now, I will ask you a question. Report your thoughts, and finish your answer with the following template: 
+FINAL ANSWER: [YOUR FINAL ANSWER]. 
+YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+Your answer should only start with "FINAL ANSWER: ", then follows with the answer. 
+"""
+
 class AgentState(TypedDict):
     messages: List[Union[HumanMessage, AIMessage, SystemMessage]]
 
@@ -126,6 +134,11 @@ def answer_message(state: AgentState) -> AgentState:
     Do not include any thought process before answering the question, and only response exactly what was being asked of you.
     If you are not able to provide an answer, use tools or state the limitation that you're facing instead. 
 
+    YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. 
+    If you are asked for a number, don't use comma to write your number, and don't use units such as $ or percent sign unless specified otherwise. 
+    If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. 
+    If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+
     Example question: How many hours are there in a day?
     Response: 24    
     """)]
@@ -165,51 +178,6 @@ def answer_message(state: AgentState) -> AgentState:
     messages.append(final_response)
     return {"messages": messages}
 
-def thought_process(state: AgentState) -> AgentState:
-    messages = state["messages"]
-    prompt = [SystemMessage(f"""
-    You are a GAIA question answering assistant.
-    Your task is to list down the steps and chain of thoughts to answer the question provided carefully.
-    Think carefully before answering the question. 
-    The steps provided should simplify the thinking of the another person and help them to derive the answer.
-    Include steps that require the use of specific tools or state the limitation that you're facing.  
-    """)]
-    messages = prompt + messages
-
-    # First pass: let model decide whether to call web_search
-    ai_msg = model_with_tools.invoke(messages)
-    messages.append(ai_msg)
-
-    # If the model didn't request any tools, its content is already the answer
-    tool_calls = getattr(ai_msg, "tool_calls", None) or []
-    if not tool_calls:
-        print(f"Final response: {ai_msg}")
-        return {"messages": messages}
-
-    # Execute requested tools and append their text output into the conversation
-    for tool_call in tool_calls:
-        name = tool_call["name"]
-        args = tool_call["args"]
-        tool = tools_by_name[name]
-        tool_result = tool.invoke(args)  # this is a plain string from web_search
-        messages.append(HumanMessage(content=f"Tool result ({name}):\n{tool_result}"))
-
-    # Second pass: force a plain-text final answer (no tool calls expected)
-    final_instruction = HumanMessage(
-        content=(
-            "Using the tool results above, provide the list of steps answer now. "
-            "Do not call any tools. Respond with only the chain of thought."
-        )
-    )
-    messages.append(final_instruction)
-
-    final_response = model.invoke(messages)
-    print(f"Final response: {final_response}")
-
-    # Return messages including the final AIMessage so BasicAgent reads .content
-    messages.append(final_response)
-    return {"thoughts": messages}
-
 
 def build_graph():
     agent_graph = StateGraph(AgentState)
@@ -217,12 +185,10 @@ def build_graph():
     # Add nodes
     agent_graph.add_node("read_message", read_message)
     agent_graph.add_node("answer_message", answer_message)
-    agent_graph.add_node("thought_process", thought_process)
 
     # Add edges
     agent_graph.add_edge(START, "read_message")
     agent_graph.add_edge("read_message", "answer_message")
-    agent_graph.add_conditional_edges("answer_message", restart_required, {True:"thought_process", False:END})
 
     # Final edge
     agent_graph.add_edge("answer_message", END)