code: def create_teachable_agent(reset_db=False):
      """Instantiates a teachable agent using the settings from the top of this file."""
      # Load LLM inference endpoints from an env variable or a file
      # See https://microsoft.github.io/autogen/docs/FAQ#set-your-api-endpoints
      # and OAI_CONFIG_LIST_sample
      config_list = config_list_from_json(
          env_or_file=OAI_CONFIG_LIST,
          filter_dict=filter_dict,
          file_location=KEY_LOC,
      )
  
      # Start by instantiating any agent that inherits from ConversableAgent.
      teachable_agent = ConversableAgent(
          name="teachable_agent",
          llm_config={
              "config_list": config_list,
              "timeout": 120,
              "cache_seed": None,
          },  # Disable caching.
      )
  
      # 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=reset_db,
          path_to_db_dir="./tmp/interactive/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)
  
      return teachable_agent
  
file_path: autogen/test/agentchat/contrib/chat_with_teachable_agent.py
imports: from autogen import UserProxyAgent, config_list_from_json
  from autogen.agentchat.contrib.capabilities.teachability import Teachability
  from autogen import ConversableAgent
  import os
  import sys
  from test_assistant_agent import OAI_CONFIG_LIST, KEY_LOC
label_desc: Use image node_id 1 for calling a global function with example usage: create_teachable_agent(reset_db) and returns: teachable_agent