context_analysis_agent/agent.py

from langchain_groq import ChatGroq
from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.checkpoint.memory import MemorySaver
from .utils.state import State 
from .utils.nodes import introduction_node, extract_business_details
from utils.models_loader import llm

business_state = State()

class IntroductionChatbot:
    def __init__(self):
        self.memory = MemorySaver()
        self.llm = llm
        self.workflow = self._initialize_workflow()
        self.interact_agent = self.workflow.compile(checkpointer=self.memory)
        self.messages = []

    def _initialize_workflow(self):
        workflow = StateGraph(MessagesState)
        workflow.add_node("chatbot", lambda state: introduction_node(state, self.llm))
        workflow.add_edge(START, "chatbot")
        workflow.add_edge("chatbot", END)
        return workflow

    def chat(self, user_input: str):
        self.messages.append({"role": "user", "content": user_input})
        config = {"configurable": {"thread_id": "1"}}
        response = self.interact_agent.invoke({"messages": [user_input]}, config)['messages'][-1].content
        self.messages.append({"role": "assistant", "content": response})
        business_state.interactions.append({'user': user_input, 'agent_response': response})
        return response

    def is_complete(self, latest_response: str) -> bool:
        return "Thanks for providing all your required business details" in latest_response

    def extract_details(self):
        response = extract_business_details(business_state.interactions)
        print('Extracted details:', response)
        return response