agent.py

import os
from dotenv import load_dotenv

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.tools import tool
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader

from langgraph.graph import StateGraph, START, MessagesState
from langgraph.prebuilt import ToolNode, tools_condition

load_dotenv()

SYSTEM_PROMPT = """You are a general AI assistant. I will ask you a question. Report your thoughts, and output only your final answer, no prefixes, suffixes, or extra text. 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."""


@tool
def add(a: float, b: float) -> float:
    """Add two numbers together.

    Args:
        a: First number
        b: Second number
    """
    return a + b


@tool
def subtract(a: float, b: float) -> float:
    """Subtract b from a.

    Args:
        a: Number to subtract from
        b: Number to subtract
    """
    return a - b


@tool
def multiply(a: float, b: float) -> float:
    """Multiply two numbers together.

    Args:
        a: First number
        b: Second number
    """
    return a * b


@tool
def divide(a: float, b: float) -> float:
    """Divide a by b.

    Args:
        a: Dividend
        b: Divisor
    """
    if b == 0:
        return "Error: Division by zero"
    return a / b


@tool
def modulo(a: float, b: float) -> float:
    """Return the remainder of a divided by b.

    Args:
        a: Dividend
        b: Divisor
    """
    if b == 0:
        return "Error: Division by zero"
    return a % b


@tool
def power(a: float, b: float) -> float:
    """Raise a to the power of b.

    Args:
        a: Base number
        b: Exponent
    """
    return a**b


@tool
def square_root(a: float) -> float:
    """Calculate the square root of a number.

    Args:
        a: Number to calculate square root of
    """
    if a < 0:
        return "Error: Cannot calculate square root of negative number"
    return a**0.5


@tool
def web_search(query: str) -> str:
    """Search the web for current information and facts.

    Args:
        query: Search query string
    """
    try:
        search_tool = TavilySearchResults(max_results=3)
        results = search_tool.invoke(query)

        if not results:
            return "No search results found."

        formatted_results = []
        for i, result in enumerate(results, 1):
            title = result.get("title", "No title")
            content = result.get("content", "No content")
            url = result.get("url", "No URL")
            formatted_results.append(f"{i}. {title}\n{content}\nSource: {url}")

        return "\n\n ==== \n\n".join(formatted_results)
    except Exception as e:
        return f"Error performing search: {str(e)}"


@tool
def wikipedia_search(query: str) -> str:
    """Search Wikipedia for factual information.

    Args:
        query: Wikipedia search query
    """
    try:
        loader = WikipediaLoader(query=query, load_max_docs=2)
        docs = loader.load()

        if not docs:
            return "No Wikipedia results found."

        formatted_docs = []
        for i, doc in enumerate(docs, 1):
            title = doc.metadata.get("title", "No title")
            content = doc.page_content
            formatted_docs.append(f"{i}. {title}\n{content}")

        return "\n\n ==== \n\n".join(formatted_docs)
    except Exception as e:
        return f"Error searching Wikipedia: {str(e)}"


tools = [
    add,
    subtract,
    multiply,
    divide,
    modulo,
    power,
    square_root,
    web_search,
    wikipedia_search,
]


def get_llm():
    """Initialize the llm"""
    return ChatGoogleGenerativeAI(
        model="gemini-2.5-flash", temperature=0, api_key=os.getenv("GEMINI_API_KEY")
    )


def call_model(state: MessagesState):
    """Call the LLM with the current state.
    
    Args:
        state: Current state containing messages
    """
    llm = get_llm()
    llm_with_tools = llm.bind_tools(tools)
    
    messages = state["messages"]
    
    if not messages or not isinstance(messages[0], SystemMessage):
        messages = [SystemMessage(content=SYSTEM_PROMPT)] + messages
    
    response = llm_with_tools.invoke(messages)
    
    return {"messages": [response]}


def build_graph():
    """Build and return the LangGraph workflow."""
    workflow = StateGraph(MessagesState)

    workflow.add_node("agent", call_model)
    workflow.add_node("tools", ToolNode(tools))

    workflow.add_edge(START, "agent")
    workflow.add_conditional_edges("agent", tools_condition)
    workflow.add_edge("tools", "agent")

    return workflow.compile()


if __name__ == "__main__":
    graph = build_graph()
    test_message = [HumanMessage(content="What is 15 + 27?")]
    result = graph.invoke({"messages": test_message})
    print(f"Test result: {result['messages'][-1].content}")