"""LangGraph Agent""" 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.tavily_search import TavilySearchResults from langchain_community.document_loaders import WikipediaLoader from langchain_core.messages import HumanMessage from langchain_core.tools import tool from langchain_mistralai import ChatMistralAI from langchain_groq import ChatGroq load_dotenv() def web_search(query: str) -> str: """Search Tavily for a query and return maximum 3 results. Args: query: The search query.""" search_docs = TavilySearchResults(max_results=3).invoke(input=query) formatted_search_docs = "\n\n---\n\n".join( [ f'\n{doc["content"]}\n' for doc in search_docs ]) return {"web_results": formatted_search_docs} 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'\n{doc.page_content}\n' for doc in search_docs ]) return {"wiki_results": formatted_search_docs} @tool def multiply(a: int, b: int) -> int: """Multiply two numbers. Args: a: first int b: second int """ return a * b @tool def add(a: int, b: int) -> int: """Add two numbers. Args: a: first int b: second int """ return a + b @tool def subtract(a: int, b: int) -> int: """Subtract two numbers. Args: a: first int b: second int """ return a - b @tool def divide(a: int, b: int) -> int: """Divide two numbers. Args: a: first int b: second int """ if b == 0: raise ValueError("Cannot divide by zero.") return a / b @tool def modulus(a: int, b: int) -> int: """Get the modulus of two numbers. Args: a: first int b: second int """ return a % b tools = [ multiply, add, subtract, divide, modulus, wiki_search, web_search, ] def build_graph(): '''llm = ChatMistralAI( model="magistral-small-2506", temperature=0, max_retries=2, )''' llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it llm_with_tools = llm.bind_tools(tools) # Node 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") # Compile graph return builder.compile() if __name__=="__main__": #system_prompt = "You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. 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." system_prompt = "You are a general AI assistant. I will ask you a question. Give your answer with the following template: only show your final answer. 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." question = "when was the Eiffel tower built?" question = f"{system_prompt} \ The question: \ {question} \ " graph = build_graph() messages = [HumanMessage(content=question)] messages = graph.invoke({"messages": messages}) for m in messages["messages"]: m.pretty_print()