class_docstr: Teachability uses a vector database to give an agent the ability to remember user teachings,
  where the user is any caller (human or not) sending messages to the teachable agent.
  Teachability is designed to be composable with other agent capabilities.
  To make any conversable agent teachable, instantiate both the agent and the Teachability class,
  then pass the agent to teachability.add_to_agent(agent).
  Note that teachable agents in a group chat must be given unique path_to_db_dir values.
class_docstr_tok: ["Teachability","uses","a","vector","database","to","give","an","agent","the","ability","to","remember","user","teachings",",","where","the","user","is","any","caller","(","human","or","not",")","sending","messages","to","the","teachable","agent",".","Teachability","is","designed","to","be","composable","with","other","agent","capabilities",".","To","make","any","conversable","agent","teachable",",","instantiate","both","the","agent","and","the","Teachability","class",",","then","pass","the","agent","to","teachability.add_to_agent","(","agent",")",".","Note","that","teachable","agents","in","a","group","chat","must","be","given","unique","path_to_db_dir","values","."]
docstr: Decides whether to store something from one user comment in the DB.
docstr_tok: ["Decides","whether","to","store","something","from","one","user","comment","in","the","DB","."]
code: def _consider_memo_storage(self, comment):
      """Decides whether to store something from one user comment in the DB."""
      memo_added = False
  
      # Check for a problem-solution pair.
      response = self._analyze(
          comment,
          "Does any part of the TEXT ask the agent to perform a task or solve a problem? Answer with just one word, yes or no.",
      )
      if "yes" in response.lower():
          # Can we extract advice?
          advice = self._analyze(
              comment,
              "Briefly copy any advice from the TEXT that may be useful for a similar but different task in the future. But if no advice is present, just respond with 'none'.",
          )
          if "none" not in advice.lower():
              # Yes. Extract the task.
              task = self._analyze(
                  comment,
                  "Briefly copy just the task from the TEXT, then stop. Don't solve it, and don't include any advice.",
              )
              # Generalize the task.
              general_task = self._analyze(
                  task,
                  "Summarize very briefly, in general terms, the type of task described in the TEXT. Leave out details that might not appear in a similar problem.",
              )
              # Add the task-advice (problem-solution) pair to the vector DB.
              if self.verbosity >= 1:
                  print(
                      colored(
                          "\nREMEMBER THIS TASK-ADVICE PAIR",
                          "light_yellow",
                      )
                  )
              self.memo_store.add_input_output_pair(
                  general_task, advice
              )
              memo_added = True
  
      # Check for information to be learned.
      response = self._analyze(
          comment,
          "Does the TEXT contain information that could be committed to memory? Answer with just one word, yes or no.",
      )
      if "yes" in response.lower():
          # Yes. What question would this information answer?
          question = self._analyze(
              comment,
              "Imagine that the user forgot this information in the TEXT. How would they ask you for this information? Include no other text in your response.",
          )
          # Extract the information.
          answer = self._analyze(
              comment,
              "Copy the information from the TEXT that should be committed to memory. Add no explanation.",
          )
          # Add the question-answer pair to the vector DB.
          if self.verbosity >= 1:
              print(
                  colored(
                      "\nREMEMBER THIS QUESTION-ANSWER PAIR",
                      "light_yellow",
                  )
              )
          self.memo_store.add_input_output_pair(question, answer)
          memo_added = True
  
      # Were any memos added?
      if memo_added:
          # Yes. Save them to disk.
          self.memo_store._save_memos()
  
imports: import os
  from autogen.agentchat.assistant_agent import ConversableAgent
  from autogen.agentchat.contrib.capabilities.agent_capability import AgentCapability
  from autogen.agentchat.contrib.text_analyzer_agent import TextAnalyzerAgent
  from typing import Dict, Optional, Union, List, Tuple, Any
  import chromadb
  from chromadb.config import Settings
  import pickle
label_desc: Use image node_id 5 for calling the Teachability obj's underlying member method code with example usage: obj._consider_memo_storage(comment) without return types