Add role to reflection with llm

autogen/agentchat/conversable_agent.py

@@ -937,6 +937,7 @@ def my_summary_method(
  One example key is "summary_prompt", and value is a string of text used to prompt a LLM-based agent (the sender or receiver agent) to reflect
  on the conversation and extract a summary when summary_method is "reflection_with_llm".
  The default summary_prompt is DEFAULT_SUMMARY_PROMPT, i.e., "Summarize takeaway from the conversation. Do not add any introductory phrases. If the intended request is NOT properly addressed, please point it out."
+ Another available key is "summary_role", which is the role of the message sent to the agent in charge of summarizing. Default is "system".
  message (str, dict or Callable): the initial message to be sent to the recipient. Needs to be provided. Otherwise, input() will be called to get the initial message.
  - If a string or a dict is provided, it will be used as the initial message. `generate_init_message` is called to generate the initial message for the agent based on this string and the context.
  If dict, it may contain the following reserved fields (either content or tool_calls need to be provided).
@@ -1168,8 +1169,13 @@ def _reflection_with_llm_as_summary(sender, recipient, summary_args):
  raise ValueError("The summary_prompt must be a string.")
  msg_list = recipient.chat_messages_for_summary(sender)
  agent = sender if recipient is None else recipient
+ role = summary_args.get("summary_role", None)
+ if role and not isinstance(role, str):
+ raise ValueError("The summary_role in summary_arg must be a string.")
  try:
- summary = sender._reflection_with_llm(prompt, msg_list, llm_agent=agent, cache=summary_args.get("cache"))
+ summary = sender._reflection_with_llm(
+ prompt, msg_list, llm_agent=agent, cache=summary_args.get("cache"), role=role
+ )
  except BadRequestError as e:
  warnings.warn(
  f"Cannot extract summary using reflection_with_llm: {e}. Using an empty str as summary.", UserWarning
@@ -1178,7 +1184,12 @@ def _reflection_with_llm_as_summary(sender, recipient, summary_args):
  return summary
 
  def _reflection_with_llm(
- self, prompt, messages, llm_agent: Optional[Agent] = None, cache: Optional[AbstractCache] = None
+ self,
+ prompt,
+ messages,
+ llm_agent: Optional[Agent] = None,
+ cache: Optional[AbstractCache] = None,
+ role: Union[str, None] = None,
  ) -> str:
  """Get a chat summary using reflection with an llm client based on the conversation history.
 
@@ -1187,10 +1198,14 @@ def _reflection_with_llm(
  messages (list): The messages generated as part of a chat conversation.
  llm_agent: the agent with an llm client.
  cache (AbstractCache or None): the cache client to be used for this conversation.
+ role (str): the role of the message, usually "system" or "user". Default is "system".
  """
+ if not role:
+ role = "system"
+
  system_msg = [
  {
- "role": "system",
+ "role": role,
  "content": prompt,
  }
  ]

test/agentchat/test_groupchat.py

@@ -5,9 +5,10 @@
 import json
 import logging
 from typing import Any, Dict, List, Optional
-from unittest import mock
+from unittest import TestCase, mock
 
 import pytest
+from test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST
 
 import autogen
 from autogen import Agent, AssistantAgent, GroupChat, GroupChatManager
@@ -1446,6 +1447,46 @@ def test_speaker_selection_agent_name_match():
  assert result == {}
 
 
+def test_role_for_reflection_summary():
+ llm_config = {"config_list": [{"model": "mock", "api_key": "mock"}]}
+ agent1 = autogen.ConversableAgent(
+ "alice",
+ max_consecutive_auto_reply=10,
+ human_input_mode="NEVER",
+ llm_config=False,
+ default_auto_reply="This is alice speaking.",
+ )
+ agent2 = autogen.ConversableAgent(
+ "bob",
+ max_consecutive_auto_reply=10,
+ human_input_mode="NEVER",
+ llm_config=False,
+ default_auto_reply="This is bob speaking.",
+ )
+ groupchat = autogen.GroupChat(
+ agents=[agent1, agent2], messages=[], max_round=3, speaker_selection_method="round_robin"
+ )
+ group_chat_manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)
+
+ role_name = "user"
+ with mock.patch.object(
+ autogen.ConversableAgent, "_generate_oai_reply_from_client"
+ ) as mock_generate_oai_reply_from_client:
+ mock_generate_oai_reply_from_client.return_value = "Mocked summary"
+
+ agent1.initiate_chat(
+ group_chat_manager,
+ max_turns=2,
+ message="hello",
+ summary_method="reflection_with_llm",
+ summary_args={"summary_role": role_name},
+ )
+
+ mock_generate_oai_reply_from_client.assert_called_once()
+ args, kwargs = mock_generate_oai_reply_from_client.call_args
+ assert kwargs["messages"][-1]["role"] == role_name
+
+
 def test_speaker_selection_auto_process_result():
  """
  Tests the return result of the 2-agent chat used for speaker selection for the auto method.
@@ -1984,12 +2025,16 @@ def test_manager_resume_messages():
  # test_role_for_select_speaker_messages()
  # test_select_speaker_message_and_prompt_templates()
  # test_speaker_selection_agent_name_match()
+ # test_role_for_reflection_summary()
+ # test_speaker_selection_auto_process_result()
+ # test_speaker_selection_validate_speaker_name()
+ # test_select_speaker_auto_messages()
  # test_speaker_selection_auto_process_result()
  # test_speaker_selection_validate_speaker_name()
  # test_select_speaker_auto_messages()
- test_manager_messages_to_string()
- test_manager_messages_from_string()
- test_manager_resume_functions()
- test_manager_resume_returns()
- test_manager_resume_messages()
- # pass
+ # test_manager_messages_to_string()
+ # test_manager_messages_from_string()
+ # test_manager_resume_functions()
+ # test_manager_resume_returns()
+ # test_manager_resume_messages()
+ pass