agent.py

import os
import pandas as pd
from langchain_core.messages import HumanMessage, AIMessage
from langgraph.graph import StateGraph, END, MessagesState
from langchain_huggingface import HuggingFaceEndpoint
from tools import TOOLS

QA_PATH = "metadata.jsonl"
qa_pairs = pd.read_json(QA_PATH, lines=True)
qa_dict = {
    row["Question"].strip(): row["Final answer"].strip()
    for _, row in qa_pairs.iterrows()
}

def build_graph():
    llm = HuggingFaceEndpoint(
        endpoint_url="https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3",
        task="text-generation",
        max_new_tokens=512,
        temperature=0.1,
        top_k=50,
        top_p=0.95,
        huggingfacehub_api_token=os.environ["HF_TOKEN"]
    )

    def retriever_node(state: MessagesState):
        query = state["messages"][-1].content.strip()
        if query in qa_dict:
            print("✅ Exact match found in retriever.")
            answer = qa_dict[query]
            return {"messages": [AIMessage(content=answer)]}
        print("🔍 No match. Passing to LLM.")
        return 

    def assistant_node(state: MessagesState):
        query = state["messages"][-1].content.strip()

        system_prompt = (
            "You are a helpful assistant. To answer the user's question, you can use tools. "
            "To use a tool, respond with a single line: 'tool:tool_name:input'. "
            "For example: 'tool:wiki_search:Apple Inc.' "
            "If you have the final answer, provide it directly without any prefixes. "
            "Never justify or explain your final answer."
        )
        
        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": query}
        ]
        
        response = llm.invoke(messages).strip()

        for tag in ("", "Answer:", "assistant:"):
            if response.lower().startswith(tag.lower()):
                response = response[len(tag):].strip()
        
        return {"messages": [AIMessage(content=response)]}

    def tool_node(state: MessagesState):
        last_message = state["messages"][-1].content.strip()
        try:
            _, tool_name, tool_input = last_message.split(":", 2)
            tool_name = tool_name.strip()
            tool_input = tool_input.strip()
            tool_fn = TOOLS.get(tool_name)

            if not tool_fn:
                print(f"❌ Unknown tool: {tool_name}")
                return {"messages": [AIMessage(content="Unknown")]}
            
            print(f"🔧 Using tool: {tool_name} with input: {tool_input}")
            tool_result = tool_fn(tool_input)
            return {"messages": [AIMessage(content=str(tool_result))]}

        except Exception as e:
            print(f"⚠️ Tool error: {e}")
            return {"messages": [AIMessage(content="Unknown")]}

    def route_after_retriever(state: MessagesState):
        if isinstance(state["messages"][-1], AIMessage):
            return END
        return "assistant"

    def route_after_assistant(state: MessagesState):
        last_message = state["messages"][-1].content.strip().lower()
        if last_message.startswith("tool:"):
            return "tool"
        return END

    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever_node)
    builder.add_node("assistant", assistant_node)
    builder.add_node("tool", tool_node)

    builder.set_entry_point("retriever")

    builder.add_conditional_edges(
        "retriever",
        route_after_retriever,
        {"assistant": "assistant", END: END}
    )
    builder.add_conditional_edges(
        "assistant",
        route_after_assistant,
        {"tool": "tool", END: END}
    )
    builder.add_edge("tool", "assistant")

    return builder.compile()

class BasicAgent:
    def __init__(self):
        print("✅ BasicAgent initialized with retriever + LLM + tools")
        self.graph = build_graph()

    def __call__(self, question: str) -> str:
        print(f"📥 Question: {question[:100]}")
        config = {"recursion_limit": 50}
        try:
            result = self.graph.invoke(
                {"messages": [HumanMessage(content=question)]},
                config=config
            )
            answer = result["messages"][-1].content.strip()
            print(f"📤 Answer: {answer}")
            return answer
        except Exception as e:
            print(f"Error during graph invocation: {e}")
            return f"AGENT ERROR: {e}"