agent.py

"""LangGraph Agent"""
import os
from dotenv import load_dotenv
from langchain_groq import ChatGroq
from langchain_core.tools import tool
from langgraph.prebuilt import ToolNode
from langgraph.prebuilt import tools_condition
from langgraph.graph import START, StateGraph, MessagesState
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_community.tools import DuckDuckGoSearchResults

load_dotenv()

@tool
def web_search(query: str) -> str:
    """
    Search DuckDuckGo for a query.

    Args:
        query: The search query.

    Returns:
        A dict with key ``"web_results"`` containing a
        markdown-formatted string of the retrieved documents.
    """
    # Instantiate the DuckDuckGo search utility
    search = DuckDuckGoSearchResults(max_results=3)

    # Perform the search
    search_result = search.invoke(query)

    return {"web_results": search_result}

tools = [
    web_search,
]

llm = ChatGroq(model="meta-llama/llama-4-maverick-17b-128e-instruct", temperature=0)
llm_with_tools = llm.bind_tools(tools)

llm_with_tools = llm.bind_tools(tools)

# Node
def start_preprocess(state: MessagesState):
    # System message
    system_prompt = "You are a general AI assistant. I will ask you a question. Your 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."
    sys_msg = SystemMessage(content=system_prompt)
    return {"messages": [sys_msg] + state["messages"]}

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

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

# Compile graph
graph = builder.compile()

if __name__ == "__main__":
    question = "NAMO age ?"
    # Run the graph
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({"messages": messages})
    for m in messages["messages"]:
        m.pretty_print()