class_docstr: Provides memory storage and retrieval for a teachable agent, using a vector database.
  Each DB entry (called a memo) is a pair of strings: an input text and an output text.
  The input text might be a question, or a task to perform.
  The output text might be an answer to the question, or advice on how to perform the task.
  Vector embeddings are currently supplied by Chroma's default Sentence Transformers.
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docstr: Adds a few arbitrary examples to the vector DB, just to make retrieval less trivial.
docstr_tok: ["Adds","a","few","arbitrary","examples","to","the","vector","DB",",","just","to","make","retrieval","less","trivial","."]
code: def prepopulate(self):
      """Adds a few arbitrary examples to the vector DB, just to make retrieval less trivial."""
      if self.verbosity >= 1:
          print(colored("\nPREPOPULATING MEMORY", "light_green"))
      examples = []
      examples.append(
          {
              "text": "When I say papers I mean research papers, which are typically pdfs.",
              "label": "yes",
          }
      )
      examples.append(
          {
              "text": "Please verify that each paper you listed actually uses langchain.",
              "label": "no",
          }
      )
      examples.append(
          {
              "text": "Tell gpt the output should still be latex code.",
              "label": "no",
          }
      )
      examples.append(
          {
              "text": "Hint: convert pdfs to text and then answer questions based on them.",
              "label": "yes",
          }
      )
      examples.append(
          {
              "text": "To create a good PPT, include enough content to make it interesting.",
              "label": "yes",
          }
      )
      examples.append(
          {
              "text": "No, for this case the columns should be aspects and the rows should be frameworks.",
              "label": "no",
          }
      )
      examples.append(
          {
              "text": "When writing code, remember to include any libraries that are used.",
              "label": "yes",
          }
      )
      examples.append(
          {
              "text": "Please summarize the papers by Eric Horvitz on bounded rationality.",
              "label": "no",
          }
      )
      examples.append(
          {
              "text": "Compare the h-index of Daniel Weld and Oren Etzioni.",
              "label": "no",
          }
      )
      examples.append(
          {
              "text": "Double check to be sure that the columns in a table correspond to what was asked for.",
              "label": "yes",
          }
      )
      for example in examples:
          self.add_input_output_pair(example["text"], example["label"])
      self._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 8 for calling the MemoStore obj's underlying member method code with example usage: obj.prepopulate() without return types