wangtao
/
autogen
mirror of https://github.com/microsoft/autogen.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Rename Responsive -> Conversable (#1202) 

* responsive -> conversable

* preview

* rename

* register reply

* rename and version

* bump version to 2.1.0

* notebook

* bug fix
This commit is contained in:
Chi Wang 2023-09-11 17:07:35 -07:00 committed by GitHub
parent 599731cb22
commit 4886cb5689
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
15 changed files with 69 additions and 69 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
flaml/autogen/agentchat/__init__.py
View File

@ -1,12 +1,12 @@
from .agent import Agent
from .responsive_agent import ResponsiveAgent
from .conversable_agent import ConversableAgent
from .assistant_agent import AssistantAgent
from .user_proxy_agent import UserProxyAgent
from .groupchat import GroupChat, GroupChatManager
__all__ = [
"Agent",
"ResponsiveAgent",
"ConversableAgent",
"AssistantAgent",
"UserProxyAgent",
"GroupChat",

8
flaml/autogen/agentchat/assistant_agent.py
View File

@ -1,11 +1,11 @@
from .responsive_agent import ResponsiveAgent
from .conversable_agent import ConversableAgent
from typing import Callable, Dict, Optional, Union
class AssistantAgent(ResponsiveAgent):
class AssistantAgent(ConversableAgent):
"""(In preview) Assistant agent, designed to solve a task with LLM.
AssistantAgent is a subclass of ResponsiveAgent configured with a default system message.
AssistantAgent is a subclass of ConversableAgent configured with a default system message.
The default system message is designed to solve a task with LLM,
including suggesting python code blocks and debugging.
`human_input_mode` is default to "NEVER"
@ -52,7 +52,7 @@ Reply "TERMINATE" in the end when everything is done.
default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case).
The limit only plays a role when human_input_mode is not "ALWAYS".
**kwargs (dict): Please refer to other kwargs in
[ResponsiveAgent](responsive_agent#__init__).
[ConversableAgent](conversable_agent#__init__).
"""
super().__init__(
name,

2
flaml/autogen/agentchat/contrib/math_user_proxy_agent.py
View File

@ -165,7 +165,7 @@ class MathUserProxyAgent(UserProxyAgent):
default_auto_reply=default_auto_reply,
**kwargs,
)
self.register_auto_reply([Agent, None], MathUserProxyAgent._generate_math_reply, 1)
self.register_reply([Agent, None], MathUserProxyAgent._generate_math_reply, 1)
# fixed var
self._max_invalid_q_per_step = max_invalid_q_per_step

2
flaml/autogen/agentchat/contrib/retrieve_assistant_agent.py
View File

@ -16,7 +16,7 @@ class RetrieveAssistantAgent(AssistantAgent):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.register_auto_reply(Agent, RetrieveAssistantAgent._generate_retrieve_assistant_reply)
self.register_reply(Agent, RetrieveAssistantAgent._generate_retrieve_assistant_reply)
def _generate_retrieve_assistant_reply(
self,

2
flaml/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
View File

@ -148,7 +148,7 @@ class RetrieveUserProxyAgent(UserProxyAgent):
self._ipython = get_ipython()
self._doc_idx = -1 # the index of the current used doc
self._results = {} # the results of the current query
self.register_auto_reply(Agent, RetrieveUserProxyAgent._generate_retrieve_user_reply)
self.register_reply(Agent, RetrieveUserProxyAgent._generate_retrieve_user_reply)
@staticmethod
def get_max_tokens(model="gpt-3.5-turbo"):

26
flaml/autogen/agentchat/responsive_agent.py → flaml/autogen/agentchat/conversable_agent.py
View File

@ -21,11 +21,11 @@ except ImportError:
return x
class ResponsiveAgent(Agent):
"""(Experimental) A class for generic responsive agents which can be configured as assistant or user proxy.
class ConversableAgent(Agent):
"""(In preview) A class for generic conversable agents which can be configured as assistant or user proxy.
After receiving each message, the agent will send a reply to the sender unless the msg is a termination msg.
For example, AssistantAgent and UserProxyAgent are subclasses of ResponsiveAgent,
For example, AssistantAgent and UserProxyAgent are subclasses of this class,
configured with different default settings.
To modify auto reply, override `generate_reply` method.
@ -119,12 +119,12 @@ class ResponsiveAgent(Agent):
self._default_auto_reply = default_auto_reply
self._reply_func_list = []
self.reply_at_receive = defaultdict(bool)
self.register_auto_reply([Agent, None], ResponsiveAgent.generate_oai_reply)
self.register_auto_reply([Agent, None], ResponsiveAgent.generate_code_execution_reply)
self.register_auto_reply([Agent, None], ResponsiveAgent.generate_function_call_reply)
self.register_auto_reply([Agent, None], ResponsiveAgent.check_termination_and_human_reply)
self.register_reply([Agent, None], ConversableAgent.generate_oai_reply)
self.register_reply([Agent, None], ConversableAgent.generate_code_execution_reply)
self.register_reply([Agent, None], ConversableAgent.generate_function_call_reply)
self.register_reply([Agent, None], ConversableAgent.check_termination_and_human_reply)
def register_auto_reply(
def register_reply(
self,
trigger: Union[Type[Agent], str, Agent, Callable[[Agent], bool], List],
reply_func: Callable,
@ -151,7 +151,7 @@ class ResponsiveAgent(Agent):
The function takes a recipient agent, a list of messages, a sender agent and a config as input and returns a reply message.
```python
def reply_func(
recipient: ResponsiveAgent,
recipient: ConversableAgent,
messages: Optional[List[Dict]] = None,
sender: Optional[Agent] = None,
config: Optional[Any] = None,
@ -499,7 +499,7 @@ class ResponsiveAgent(Agent):
def initiate_chat(
self,
recipient: "ResponsiveAgent",
recipient: "ConversableAgent",
clear_history: Optional[bool] = True,
silent: Optional[bool] = False,
**context,
@ -522,7 +522,7 @@ class ResponsiveAgent(Agent):
async def a_initiate_chat(
self,
recipient: "ResponsiveAgent",
recipient: "ConversableAgent",
clear_history: Optional[bool] = True,
silent: Optional[bool] = False,
**context,
@ -610,8 +610,8 @@ class ResponsiveAgent(Agent):
return False, None
if messages is None:
messages = self._oai_messages[sender]
last_n_messages = min(len(messages), code_execution_config.pop("last_n_messages", 1))
for i in range(last_n_messages):
last_n_messages = code_execution_config.pop("last_n_messages", 1)
for i in range(min(len(messages), last_n_messages)):
message = messages[-(i + 1)]
code_blocks = extract_code(message["content"])
if len(code_blocks) == 1 and code_blocks[0][0] == UNKNOWN:

8
flaml/autogen/agentchat/groupchat.py
View File

@ -2,7 +2,7 @@ from dataclasses import dataclass
import sys
from typing import Dict, List, Optional, Union
from .agent import Agent
from .responsive_agent import ResponsiveAgent
from .conversable_agent import ConversableAgent
@dataclass
@ -39,7 +39,7 @@ class GroupChat:
Read the following conversation.
Then select the next role from {self.agent_names} to play. Only return the role."""
def select_speaker(self, last_speaker: Agent, selector: ResponsiveAgent):
def select_speaker(self, last_speaker: Agent, selector: ConversableAgent):
"""Select the next speaker."""
selector.update_system_message(self.select_speaker_msg())
final, name = selector.generate_oai_reply(
@ -63,7 +63,7 @@ Then select the next role from {self.agent_names} to play. Only return the role.
return "\n".join([f"{agent.name}: {agent.system_message}" for agent in self.agents])
class GroupChatManager(ResponsiveAgent):
class GroupChatManager(ConversableAgent):
"""(In preview) A chat manager agent that can manage a group chat of multiple agents."""
def __init__(
@ -84,7 +84,7 @@ class GroupChatManager(ResponsiveAgent):
system_message=system_message,
**kwargs,
)
self.register_auto_reply(Agent, GroupChatManager.run_chat, config=groupchat, reset_config=GroupChat.reset)
self.register_reply(Agent, GroupChatManager.run_chat, config=groupchat, reset_config=GroupChat.reset)
# self._random = random.Random(seed)
def run_chat(

8
flaml/autogen/agentchat/user_proxy_agent.py
View File

@ -1,14 +1,14 @@
from .responsive_agent import ResponsiveAgent
from .conversable_agent import ConversableAgent
from typing import Callable, Dict, Optional, Union
class UserProxyAgent(ResponsiveAgent):
class UserProxyAgent(ConversableAgent):
"""(In preview) A proxy agent for the user, that can execute code and provide feedback to the other agents.
UserProxyAgent is a subclass of ResponsiveAgent configured with `human_input_mode` to ALWAYS
UserProxyAgent is a subclass of ConversableAgent configured with `human_input_mode` to ALWAYS
and `llm_config` to False. By default, the agent will prompt for human input every time a message is received.
Code execution is enabled by default. LLM-based auto reply is disabled by default.
To modify auto reply, register a method with (`register_auto_reply`)[responsive_agent#register_auto_reply].
To modify auto reply, register a method with (`register_reply`)[conversable_agent#register_reply].
To modify the way to get human input, override `get_human_input` method.
To modify the way to execute code blocks, single code block, or function call, override `execute_code_blocks`,
`run_code`, and `execute_function` methods respectively.

2
flaml/version.py
View File

@ -1 +1 @@
__version__ = "2.0.3"
__version__ = "2.1.0"

10
notebook/autogen_agentchat_chess.ipynb
View File

@ -35,7 +35,7 @@
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"# %pip install flaml[autogen]~=2.0.0\n",
"# %pip install flaml[autogen]~=2.1.0\n",
"%pip install chess -U"
]
},
@ -158,7 +158,7 @@
" llm_config={\"temperature\": 0.0, \"config_list\": config_list_gpt4},\n",
" max_consecutive_auto_reply=10,\n",
" )\n",
" self.register_auto_reply(autogen.ResponsiveAgent, BoardAgent._generate_board_reply)\n",
" self.register_reply(autogen.ConversableAgent, BoardAgent._generate_board_reply)\n",
" self.board = board\n",
" self.correct_move_messages = defaultdict(list)\n",
"\n",
@ -226,8 +226,8 @@
" max_consecutive_auto_reply=max_turns,\n",
" **kwargs,\n",
" )\n",
" self.register_auto_reply(BoardAgent, ChessPlayerAgent._generate_reply_for_board, config=board_agent.board)\n",
" self.register_auto_reply(ChessPlayerAgent, ChessPlayerAgent._generate_reply_for_player, config=board_agent)\n",
" self.register_reply(BoardAgent, ChessPlayerAgent._generate_reply_for_board, config=board_agent.board)\n",
" self.register_reply(ChessPlayerAgent, ChessPlayerAgent._generate_reply_for_player, config=board_agent)\n",
" self.update_max_consecutive_auto_reply(board_agent.max_consecutive_auto_reply(), board_agent)\n",
"\n",
" def _generate_reply_for_board(\n",
@ -262,7 +262,7 @@
" return True, None\n",
" # converse with the board until a legal move is made or max allowed retries.\n",
" # change silent to False to see that conversation.\n",
" self.initiate_chat(board_agent, clear_history=False, message=message, silent=True)\n",
" self.initiate_chat(board_agent, clear_history=False, message=message, silent=self.human_input_mode == \"NEVER\")\n",
" # last message sent by the board agent\n",
" last_message = self._oai_messages[board_agent][-1]\n",
" if last_message[\"role\"] == \"assistant\":\n",

4
notebook/autogen_agentchat_stream.ipynb
View File

@ -45,7 +45,7 @@
},
"outputs": [],
"source": [
"# %pip install flaml[autogen]~=2.0.0"
"# %pip install flaml[autogen]~=2.1.0"
]
},
{
@ -258,7 +258,7 @@
" )\n",
" return False, None\n",
"\n",
"user_proxy.register_auto_reply(autogen.AssistantAgent, add_data_reply, 1, config={\"news_stream\": data})"
"user_proxy.register_reply(autogen.AssistantAgent, add_data_reply, 1, config={\"news_stream\": data})"
]
},
{

2
test/autogen/agentchat/test_async.py
View File

@ -98,7 +98,7 @@ async def test_stream():
)
return False, None
user_proxy.register_auto_reply(autogen.AssistantAgent, add_data_reply, 1, config={"news_stream": data})
user_proxy.register_reply(autogen.AssistantAgent, add_data_reply, 1, config={"news_stream": data})
await user_proxy.a_initiate_chat(
assistant,

46
test/autogen/agentchat/test_responsive_agent.py → test/autogen/agentchat/test_conversable_agent.py
View File

@ -1,48 +1,48 @@
import pytest
from flaml.autogen.agentchat import ResponsiveAgent
from flaml.autogen.agentchat import ConversableAgent
def test_trigger():
agent = ResponsiveAgent("a0", max_consecutive_auto_reply=0, llm_config=False, human_input_mode="NEVER")
agent1 = ResponsiveAgent("a1", max_consecutive_auto_reply=0, human_input_mode="NEVER")
agent.register_auto_reply(agent1, lambda recipient, messages, sender, config: (True, "hello"))
agent = ConversableAgent("a0", max_consecutive_auto_reply=0, llm_config=False, human_input_mode="NEVER")
agent1 = ConversableAgent("a1", max_consecutive_auto_reply=0, human_input_mode="NEVER")
agent.register_reply(agent1, lambda recipient, messages, sender, config: (True, "hello"))
agent1.initiate_chat(agent, message="hi")
assert agent1.last_message(agent)["content"] == "hello"
agent.register_auto_reply("a1", lambda recipient, messages, sender, config: (True, "hello a1"))
agent.register_reply("a1", lambda recipient, messages, sender, config: (True, "hello a1"))
agent1.initiate_chat(agent, message="hi")
assert agent1.last_message(agent)["content"] == "hello a1"
agent.register_auto_reply(
ResponsiveAgent, lambda recipient, messages, sender, config: (True, "hello responsive agent")
agent.register_reply(
ConversableAgent, lambda recipient, messages, sender, config: (True, "hello conversable agent")
)
agent1.initiate_chat(agent, message="hi")
assert agent1.last_message(agent)["content"] == "hello responsive agent"
agent.register_auto_reply(
assert agent1.last_message(agent)["content"] == "hello conversable agent"
agent.register_reply(
lambda sender: sender.name.startswith("a"), lambda recipient, messages, sender, config: (True, "hello a")
)
agent1.initiate_chat(agent, message="hi")
assert agent1.last_message(agent)["content"] == "hello a"
agent.register_auto_reply(
agent.register_reply(
lambda sender: sender.name.startswith("b"), lambda recipient, messages, sender, config: (True, "hello b")
)
agent1.initiate_chat(agent, message="hi")
assert agent1.last_message(agent)["content"] == "hello a"
agent.register_auto_reply(
agent.register_reply(
["agent2", agent1], lambda recipient, messages, sender, config: (True, "hello agent2 or agent1")
)
agent1.initiate_chat(agent, message="hi")
assert agent1.last_message(agent)["content"] == "hello agent2 or agent1"
agent.register_auto_reply(
agent.register_reply(
["agent2", "agent3"], lambda recipient, messages, sender, config: (True, "hello agent2 or agent3")
)
agent1.initiate_chat(agent, message="hi")
assert agent1.last_message(agent)["content"] == "hello agent2 or agent1"
pytest.raises(ValueError, agent.register_auto_reply, 1, lambda recipient, messages, sender, config: (True, "hi"))
pytest.raises(ValueError, agent.register_reply, 1, lambda recipient, messages, sender, config: (True, "hi"))
pytest.raises(ValueError, agent._match_trigger, 1, agent1)
def test_context():
agent = ResponsiveAgent("a0", max_consecutive_auto_reply=0, llm_config=False, human_input_mode="NEVER")
agent1 = ResponsiveAgent("a1", max_consecutive_auto_reply=0, human_input_mode="NEVER")
agent = ConversableAgent("a0", max_consecutive_auto_reply=0, llm_config=False, human_input_mode="NEVER")
agent1 = ConversableAgent("a1", max_consecutive_auto_reply=0, human_input_mode="NEVER")
agent1.send(
{
"content": "hello {name}",
@ -77,8 +77,8 @@ def test_context():
def test_max_consecutive_auto_reply():
agent = ResponsiveAgent("a0", max_consecutive_auto_reply=2, llm_config=False, human_input_mode="NEVER")
agent1 = ResponsiveAgent("a1", max_consecutive_auto_reply=0, human_input_mode="NEVER")
agent = ConversableAgent("a0", max_consecutive_auto_reply=2, llm_config=False, human_input_mode="NEVER")
agent1 = ConversableAgent("a1", max_consecutive_auto_reply=0, human_input_mode="NEVER")
assert agent.max_consecutive_auto_reply() == agent.max_consecutive_auto_reply(agent1) == 2
agent.update_max_consecutive_auto_reply(1)
assert agent.max_consecutive_auto_reply() == agent.max_consecutive_auto_reply(agent1) == 1
@ -105,9 +105,9 @@ def test_max_consecutive_auto_reply():
assert agent1.reply_at_receive[agent] is False and agent.reply_at_receive[agent1] is True
def test_responsive_agent():
dummy_agent_1 = ResponsiveAgent(name="dummy_agent_1", human_input_mode="ALWAYS")
dummy_agent_2 = ResponsiveAgent(name="dummy_agent_2", human_input_mode="TERMINATE")
def test_conversable_agent():
dummy_agent_1 = ConversableAgent(name="dummy_agent_1", human_input_mode="ALWAYS")
dummy_agent_2 = ConversableAgent(name="dummy_agent_2", human_input_mode="TERMINATE")
# monkeypatch.setattr(sys, "stdin", StringIO("exit"))
dummy_agent_1.receive("hello", dummy_agent_2) # receive a str
@ -159,7 +159,7 @@ def test_generate_reply():
given_num = 10
return num_to_be_added + given_num
dummy_agent_2 = ResponsiveAgent(name="user_proxy", human_input_mode="TERMINATE", function_map={"add_num": add_num})
dummy_agent_2 = ConversableAgent(name="user_proxy", human_input_mode="TERMINATE", function_map={"add_num": add_num})
messsages = [{"function_call": {"name": "add_num", "arguments": '{ "num_to_be_added": 5 }'}, "role": "assistant"}]
# when sender is None, messages is provided
@ -168,7 +168,7 @@ def test_generate_reply():
), "generate_reply not working when sender is None"
# when sender is provided, messages is None
dummy_agent_1 = ResponsiveAgent(name="dummy_agent_1", human_input_mode="ALWAYS")
dummy_agent_1 = ConversableAgent(name="dummy_agent_1", human_input_mode="ALWAYS")
dummy_agent_2._oai_messages[dummy_agent_1] = messsages
assert (
dummy_agent_2.generate_reply(messages=None, sender=dummy_agent_1)["content"] == "15"
@ -179,4 +179,4 @@ if __name__ == "__main__":
test_trigger()
# test_context()
# test_max_consecutive_auto_reply()
# test_responsive_agent(pytest.monkeypatch)
# test_conversable_agent(pytest.monkeypatch)

12
test/autogen/agentchat/test_groupchat.py
View File

@ -2,14 +2,14 @@ from flaml import autogen
def test_chat_manager():
agent1 = autogen.ResponsiveAgent(
agent1 = autogen.ConversableAgent(
"alice",
max_consecutive_auto_reply=2,
human_input_mode="NEVER",
llm_config=False,
default_auto_reply="This is alice sepaking.",
)
agent2 = autogen.ResponsiveAgent(
agent2 = autogen.ConversableAgent(
"bob",
max_consecutive_auto_reply=2,
human_input_mode="NEVER",
@ -33,14 +33,14 @@ def test_chat_manager():
def test_plugin():
# Give another Agent class ability to manage group chat
agent1 = autogen.ResponsiveAgent(
agent1 = autogen.ConversableAgent(
"alice",
max_consecutive_auto_reply=2,
human_input_mode="NEVER",
llm_config=False,
default_auto_reply="This is alice sepaking.",
)
agent2 = autogen.ResponsiveAgent(
agent2 = autogen.ConversableAgent(
"bob",
max_consecutive_auto_reply=2,
human_input_mode="NEVER",
@ -48,8 +48,8 @@ def test_plugin():
default_auto_reply="This is bob speaking.",
)
groupchat = autogen.GroupChat(agents=[agent1, agent2], messages=[], max_round=2)
group_chat_manager = autogen.ResponsiveAgent(name="deputy_manager", llm_config=False)
group_chat_manager.register_auto_reply(
group_chat_manager = autogen.ConversableAgent(name="deputy_manager", llm_config=False)
group_chat_manager.register_reply(
autogen.Agent,
reply_func=autogen.GroupChatManager.run_chat,
config=groupchat,

2
website/docs/Use-Cases/Autogen.md
View File

@ -1,3 +1,3 @@
# AutoGen: Enabling Next-Gen GPT-X Applications
# AutoGen for Large Language Models
Please refer to https://microsoft.github.io/autogen/.