agent.py

"""LangGraph Agent"""
import os
from dotenv import load_dotenv

from langchain_openai import ChatOpenAI

from langgraph.graph import START, StateGraph
from langgraph.prebuilt import tools_condition, ToolNode
from langgraph.graph import START, StateGraph, MessagesState
from langchain_core.messages import SystemMessage, HumanMessage

from tools import level1_tools

load_dotenv()

# Build graph function
def build_agent_graph():
    """Build the graph"""
    # Load environment variables from .env file
    llm = ChatOpenAI(model="gpt-4o-mini")
    
    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(level1_tools)
    
    # System message
    system_prompt = SystemMessage(
    content="""You are a general AI assistant being evaluated in the GAIA Benchmark.
    I will ask you a question and you must reach your final answer by using a set of tools I provide to you. Please, when you are needed to pass file names to the tools, pass absolute paths.
    Your final answer should be a number OR as few words as possible OR a comma separated list of numbers and/or strings.
    Here are more detailed instructions you must follow to write your final answer:
    1) If you are asked for a number, you must write a number!. Don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise.
    2) 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.
    3) 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.
    If you follow all these instructions perfectly, you will win 1,000,000 dollars, otherwise, your mom will die.
    Let's start!
    """
    )
    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        #return {"messages": [llm_with_tools.invoke(state["messages"])]}
        return {"messages": [llm_with_tools.invoke([system_prompt] + state["messages"])]}

    
    builder = StateGraph(MessagesState)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(level1_tools))
    builder.add_edge(START, "assistant")
    builder.add_conditional_edges(
        "assistant",
        tools_condition,
    )
    builder.add_edge("tools", "assistant")

    # Compile graph
    return builder.compile()



class MyGAIAAgent:
    def __init__(self):
        print("MyAgent initialized.")
        self.graph = build_agent_graph()
    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        # Wrap the question in a HumanMessage from langchain_core
        
        messages = [HumanMessage(content=question)]
        messages = self.graph.invoke({"messages": messages})
        answer = messages['messages'][-1].content
        
        '''
        user_input = {"messages": [("user", question)]}
        answer1 = self.graph.invoke(user_input)["messages"][-1].content
        print (answer1)
        '''
        return answer
    
    def _clean_answer(self, answer: any) -> str:
        """
        Taken from `susmitsil`:
        https://huggingface.co/spaces/susmitsil/FinalAgenticAssessment/blob/main/main_agent.py
        Clean up the answer to remove common prefixes and formatting
        that models often add but that can cause exact match failures.
        Args:
            answer: The raw answer from the model
        Returns:
            The cleaned answer as a string
        """
        # Convert non-string types to strings
        if not isinstance(answer, str):
            # Handle numeric types (float, int)
            if isinstance(answer, float):
                # Format floating point numbers properly
                # Check if it's an integer value in float form (e.g., 12.0)
                if answer.is_integer():
                    formatted_answer = str(int(answer))
                else:
                    # For currency values that might need formatting
                    if abs(answer) >= 1000:
                        formatted_answer = f"${answer:,.2f}"
                    else:
                        formatted_answer = str(answer)
                return formatted_answer
            elif isinstance(answer, int):
                return str(answer)
            else:
                # For any other type
                return str(answer)

        # Now we know answer is a string, so we can safely use string methods
        # Normalize whitespace
        answer = answer.strip()

        # Remove common prefixes and formatting that models add
        prefixes_to_remove = [
            "The answer is ",
            "Answer: ",
            "Final answer: ",
            "The result is ",
            "To answer this question: ",
            "Based on the information provided, ",
            "According to the information: ",
        ]

        for prefix in prefixes_to_remove:
            if answer.startswith(prefix):
                answer = answer[len(prefix) :].strip()

        # Remove quotes if they wrap the entire answer
        if (answer.startswith('"') and answer.endswith('"')) or (
            answer.startswith("'") and answer.endswith("'")
        ):
            answer = answer[1:-1].strip()
        return answer



# test
if __name__ == "__main__":
    question1 = "How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)?"
    question2 = "Convert 10 miles to kilometers."       
    question3 = "Examine the video at https://www.youtube.com/watch?v=1htKBjuUWec. What does Teal'c say in response to the question ""Isn't that hot?"
    agent = MyGAIAAgent()
    print(agent(question1))