from langgraph.graph import StateGraph
from langchain_core.tools import Tool
from langchain_community.chat_models import ChatOpenAI
from langchain_community.tools import TavilySearchResults



def build_graph():
    graph = StateGraph(dict)

    # Setup tools
    search = TavilySearchResults()


    def search_step(state):
        question = state.get("question")
        if not question:
            raise ValueError("Missing 'question' in state")
        result = search.run(question)
        state["search_result"] = result
        return state

    def llm_step(state):
        llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
        question = state.get("question")
        search_info = state.get("search_result", "")
        prompt = f"""
You are solving a GAIA benchmark question.
Here’s the question:
{question}

Here’s retrieved web info:
{search_info}

ONLY return the final exact answer (no explanation, no prefix).
"""
        response = llm.invoke(prompt)
        state["llm_output"] = response.content.strip()
        return state

    def formatter_step(state):
        llm_output = state.get("llm_output")
        if not llm_output:
            raise ValueError("Missing 'llm_output' in state")
        cleaned = llm_output.strip()
        state["final_answer"] = cleaned
        return state

    # Add nodes
    graph.add_node("search", search_step)
    graph.add_node("llm", llm_step)
    graph.add_node("formatter", formatter_step)

    # Define flow
    graph.set_entry_point("search")
    graph.add_edge("search", "llm")
    graph.add_edge("llm", "formatter")
    graph.set_finish_point("formatter")

    return graph