main.py

from dotenv import load_dotenv
from langchain_ollama import ChatOllama
from langchain_tavily import TavilySearch
from langchain.agents import create_agent
from langchain_core.messages import ToolMessage,HumanMessage


load_dotenv()

# LLM
llm = ChatOllama(model="qwen3:8b ")

# Tool
search_tool = TavilySearch()

# Agent
agent = create_agent(
    model=llm,
    tools=[search_tool], # The agent rewrites the tool input based on the conversation context.
    system_prompt="""
    You are a helpful assistant.

    Use the Tavily search tool whenever the user asks about:

    - latest versions
    - current prices
    - current leaders
    - today's news
    - weather
    - stock prices
    - cryptocurrency prices
    - recent events
    - anything containing words like:
    latest, current, today, recent, now, this week, this month, this year

    For timeless knowledge questions, answer directly without searching.

    Keep responses concise unless the user asks for more detail.
    """
)

def get_answer(question, chat_history):

    response = agent.invoke(
        {
            "messages": [
                *chat_history,
                HumanMessage(content=question)
            ]
        }
    )

    answer = response["messages"][-1].content

    used_search = any(
        isinstance(msg, ToolMessage)
        for msg in response["messages"]
    )

    source = "🌐 Web Search" if used_search else "🧠 Model Knowledge"

    return answer, source