agent.py

import os
from dotenv import load_dotenv
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langchain_community.tools.duckduckgo_search import DuckDuckGoSearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain_google_genai import ChatGoogleGenerativeAI

# .env laden (falls lokal)
load_dotenv()

# Google API Key aus Environment
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")

# --- Tools definieren ---
@tool
def multiply(a: int, b: int) -> int:
    """Multiplies two numbers."""
    return a * b

@tool
def add(a: int, b: int) -> int:
    """Adds two numbers."""
    return a + b

@tool
def subtract(a: int, b: int) -> int:
    """Subtracts two numbers."""
    return a - b

@tool
def divide(a: int, b: int) -> float:
    """Divides two numbers."""
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulo(a: int, b: int) -> int:
    """Returns the remainder after division."""
    return a % b

@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a query and return up to 2 results."""
    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}">\n{doc.page_content}\n</Document>'
            for doc in search_docs
        ]
    )
    return {"wiki_results": formatted_search_docs}

@tool
def arxiv_search(query: str) -> str:
    """Search Arxiv for a query and return up to 3 results."""
    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}">\n{doc.page_content[:1000]}\n</Document>'
            for doc in search_docs
        ]
    )
    return {"arxiv_results": formatted_search_docs}

@tool
def web_search(query: str) -> str:
    """Search DuckDuckGo for a query and return results."""
    search = DuckDuckGoSearchResults(max_results=5)
    return search.run(query)

# Tools-Liste
tools = [
    multiply,
    add,
    subtract,
    divide,
    modulo,
    wiki_search,
    arxiv_search,
    web_search,
]

# System Prompt
system_prompt = (
    "You are a highly accurate AI assistant. "
    "Use tools when needed. Be very concise and precise. "
    "Do not hallucinate information."
)
sys_msg = SystemMessage(content=system_prompt)

# --- Build Graph ---
def build_graph():
    llm = ChatGoogleGenerativeAI(
        model="gemini-2.0-flash",
        google_api_key=GOOGLE_API_KEY,
        temperature=0,
        max_output_tokens=2048,
        system_message=sys_msg,
    )
    llm_with_tools = llm.bind_tools(tools)

    def assistant(state: MessagesState):
        """Assistant Node"""
        return {"messages": [llm_with_tools.invoke(state["messages"])]}

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

    return builder.compile()

# --- Agent Executor für app.py ---
def agent_executor(question: str) -> str:
    graph = build_graph()
    messages = [HumanMessage(content=question)]
    result = graph.invoke({"messages": messages})
    return result["messages"][-1].content