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_groq import ChatGroq
from langchain_community.tools import DuckDuckGoSearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain_core.output_parsers import StrOutputParser

load_dotenv()

@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a query and return maximum 2 results.
    
    Args:
        query: The search query."""
    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 web_search(query: str) -> str:
    """Search DuckDuckGo for a query and return maximum 3 results.
    
    Args:
        query: The search query."""
    search_docs = DuckDuckGoSearchResults(max_results=3).invoke(query)
    return {"web_results": search_docs}

system_prompt =  "You are a helpful assistant"
sys_msg = SystemMessage(content=system_prompt)

tools = [
    wiki_search,
    web_search,
]

llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
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")
graph = builder.compile()


def get_answer(query):
  messages = [HumanMessage(content=query)]
  messages = graph.invoke({"messages": messages})
  return result["messages"][-1]["content"]

if __name__ == "__main__":
    question = "In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?"
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({"messages": messages})
    for m in messages["messages"]:
        m.pretty_print()