src/usecases/teachableagent.py

import os
from autogen import AssistantAgent, UserProxyAgent
import streamlit as st
from autogen import ConversableAgent, UserProxyAgent
from autogen.agentchat.contrib.capabilities.teachability import Teachability


class TeachableAgent:
    def __init__(self,llm_config,problem):
        self.llm_config = llm_config
        self.problem = problem

    
    def start_chat(self):
        llm_config= st.session_state['llm_config']
        problem = self.problem
        # Start by instantiating any agent that inherits from ConversableAgent.
        teachable_agent = ConversableAgent(
            name="teachable_agent",  # The name is flexible, but should not contain spaces to work in group chat.
            llm_config=llm_config
        )

        # Instantiate the Teachability capability. Its parameters are all optional.
        teachability = Teachability(
            verbosity=0,  # 0 for basic info, 1 to add memory operations, 2 for analyzer messages, 3 for memo lists.
            reset_db=False,
            path_to_db_dir="./teachability_db",
            recall_threshold=1.5,  # Higher numbers allow more (but less relevant) memos to be recalled.
        )

        # Now add the Teachability capability to the agent.
        teachability.add_to_agent(teachable_agent)

        # Instantiate a UserProxyAgent to represent the user. But in this notebook, all user input will be simulated.
        user = UserProxyAgent(
            name="user",
            human_input_mode="NEVER",
            is_termination_msg=lambda x: True if "TERMINATE" in x.get("content") else False,
            max_consecutive_auto_reply=0,
            code_execution_config={
                "use_docker": False
            },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
        )
        #clear_history = False - Teach
        response = user.initiate_chat(teachable_agent, message=problem, clear_history=st.session_state["Chat_Purpose"])
        return response