Fimeg/letta-server
1
0
Fork 0
Code Issues 4 Pull Requests Actions 3 Packages Projects Releases Wiki Activity

add examples

Browse Source
This commit is contained in:
Qingzheng Gao
2023-10-27 13:52:50 +08:00
parent 215dad3f12
commit b2fcbd0241
3 changed files with 125 additions and 24 deletions
Download Patch File Download Diff File Expand all files Collapse all files

66
memgpt/autogen/examples/agent_autoreply.py Normal file
View File

@@ -0,0 +1,66 @@
"""Example of how to add MemGPT into an AutoGen groupchat
Based on the official AutoGen example here: https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb
Begin by doing:
pip install "pyautogen[teachable]"
pip install pymemgpt
or
pip install -e . (inside the MemGPT home directory)
"""
import os
import autogen
from memgpt.autogen.memgpt_agent import create_memgpt_autogen_agent_from_config
config_list = [
{
"model": "gpt-4",
"api_key": os.getenv("OPENAI_API_KEY"),
},
]
# If USE_MEMGPT is False, then this example will be the same as the official AutoGen repo
# (https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb)
# If USE_MEMGPT is True, then we swap out the "coder" agent with a MemGPT agent
USE_MEMGPT = False
llm_config = {"config_list": config_list, "seed": 42}
# The user agent
user_proxy = autogen.UserProxyAgent(
name="User_proxy",
system_message="A human admin.",
code_execution_config={"last_n_messages": 2, "work_dir": "groupchat"},
human_input_mode="TERMINATE", # needed?
default_auto_reply="You are going to figure all out by your own. "
"Work by yourself, the user won't reply until you output `TERMINATE` to end the conversation.",
)
if not USE_MEMGPT:
# In the AutoGen example, we create an AssistantAgent to play the role of the coder
coder = autogen.AssistantAgent(
name="Coder",
llm_config=llm_config,
system_message=f"I am a 10x engineer, trained in Python. I was the first engineer at Uber "
f"(which I make sure to tell everyone I work with).",
human_input_mode="TERMINATE",
)
else:
# In our example, we swap this AutoGen agent with a MemGPT agent
# This MemGPT agent will have all the benefits of MemGPT, ie persistent memory, etc.
coder = create_memgpt_autogen_agent_from_config(
"MemGPT_coder",
llm_config=llm_config,
system_message=f"I am a 10x engineer, trained in Python. I was the first engineer at Uber "
f"(which I make sure to tell everyone I work with).",
human_input_mode="TERMINATE",
)
# Begin the group chat with a message from the user
user_proxy.initiate_chat(
coder,
message="I want to design an app to make me one million dollars in one month. "
"Tell me all the details, then try out every steps.",
)

36
memgpt/autogen/examples/agent_groupchat.py
View File

@@ -11,7 +11,7 @@ Begin by doing:
import os
import autogen
from memgpt.autogen.memgpt_agent import create_autogen_memgpt_agent
from memgpt.autogen.memgpt_agent import create_autogen_memgpt_agent, create_memgpt_autogen_agent_from_config
config_list = [
{
@@ -20,10 +20,13 @@ config_list = [
},
]
# If USE_MEMGPT is False, then this example will be the same as the official AutoGen repo (https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb)
# If USE_MEMGPT is False, then this example will be the same as the official AutoGen repo
# (https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb)
# If USE_MEMGPT is True, then we swap out the "coder" agent with a MemGPT agent
USE_MEMGPT = True
USE_AUTOGEN_WORKFLOW = False
llm_config = {"config_list": config_list, "seed": 42}
# The user agent
@@ -51,13 +54,25 @@ if not USE_MEMGPT:
else:
# In our example, we swap this AutoGen agent with a MemGPT agent
# This MemGPT agent will have all the benefits of MemGPT, ie persistent memory, etc.
coder = create_autogen_memgpt_agent(
"MemGPT_coder",
persona_description="I am a 10x engineer, trained in Python. I was the first engineer at Uber (which I make sure to tell everyone I work with).",
user_description=f"You are participating in a group chat with a user ({user_proxy.name}) and a product manager ({pm.name}).",
# extra options
# interface_kwargs={"debug": True},
)
if not USE_AUTOGEN_WORKFLOW:
coder = create_autogen_memgpt_agent(
"MemGPT_coder",
persona_description="I am a 10x engineer, trained in Python. I was the first engineer at Uber "
"(which I make sure to tell everyone I work with).",
user_description=f"You are participating in a group chat with a user ({user_proxy.name}) "
f"and a product manager ({pm.name}).",
# extra options
# interface_kwargs={"debug": True},
)
else:
coder = create_memgpt_autogen_agent_from_config(
"MemGPT_coder",
llm_config=llm_config,
system_message=f"I am a 10x engineer, trained in Python. I was the first engineer at Uber "
f"(which I make sure to tell everyone I work with).\n"
f"You are participating in a group chat with a user ({user_proxy.name}) "
f"and a product manager ({pm.name}).",
)
# Initialize the group chat between the user and two LLM agents (PM and coder)
groupchat = autogen.GroupChat(agents=[user_proxy, pm, coder], messages=[], max_round=12)
@@ -66,5 +81,6 @@ manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)
# Begin the group chat with a message from the user
user_proxy.initiate_chat(
manager,
message="I want to design an app to make me one million dollars in one month. Yes, your heard that right.",
message="I want to design an app to make me one million dollars in one month. "
"Yes, your heard that right.",
)

47
memgpt/autogen/memgpt_agent.py
View File

@@ -27,13 +27,13 @@ def create_memgpt_autogen_agent_from_config(
"""Construct AutoGen config workflow in a clean way."""
model = constants.DEFAULT_MEMGPT_MODEL if llm_config is None else llm_config["config_list"][0]["model"]
persona_desc = personas.DEFAULT if system_message is "" else system_message
if human_input_mode is "ALWAYS":
persona_desc = personas.DEFAULT if system_message == "" else system_message
if human_input_mode == "ALWAYS":
user_desc = humans.DEFAULT
elif human_input_mode is "TERMINATE":
user_desc = "Output `TERMINATE` to end the conversation."
elif human_input_mode == "TERMINATE":
user_desc = "Work by yourself, the user won't reply until you output `TERMINATE` to end the conversation."
else:
user_desc = ""
user_desc = "Work by yourself, the user won't reply. Elaborate as much as possible."
if function_map is not None or code_execution_config is not None:
raise NotImplementedError
@@ -47,20 +47,37 @@ def create_memgpt_autogen_agent_from_config(
is_termination_msg=is_termination_msg,
)
if human_input_mode is not "ALWAYS":
user_proxy = UserProxyAgent(
name="user_proxy",
system_message=humans.DEFAULT,
human_input_mode="NEVER",
default_auto_reply=default_auto_reply,
if human_input_mode != "ALWAYS":
coop_agent1 = create_autogen_memgpt_agent(
name,
preset=presets.DEFAULT,
model=model,
persona_description=persona_desc,
user_description=user_desc,
is_termination_msg=is_termination_msg,
)
if default_auto_reply != "":
coop_agent2 = UserProxyAgent(
name,
human_input_mode="NEVER",
default_auto_reply=default_auto_reply,
)
else:
coop_agent2 = create_autogen_memgpt_agent(
name,
preset=presets.DEFAULT,
model=model,
persona_description=persona_desc,
user_description=user_desc,
is_termination_msg=is_termination_msg,
)
groupchat = GroupChat(
agents=[user_proxy, autogen_memgpt_agent],
agents=[autogen_memgpt_agent, coop_agent1, coop_agent2],
messages=[],
max_round=12 if max_consecutive_auto_reply is None else max_consecutive_auto_reply
)
manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)
manager = GroupChatManager(name=name, groupchat=groupchat, llm_config=llm_config)
return manager
else:
@@ -136,7 +153,7 @@ class MemGPTAgent(ConversableAgent):
self.messages_processed_up_to_idx = 0
self._is_termination_msg = (
is_termination_msg if is_termination_msg is not None else (lambda x: x.get("content") == "TERMINATE")
is_termination_msg if is_termination_msg is not None else (lambda x: x == "TERMINATE")
)
def format_other_agent_message(self, msg):
@@ -188,6 +205,8 @@ class MemGPTAgent(ConversableAgent):
# Extend the MemGPT message list with multiple 'user' messages, then push the last one with agent.step()
self.agent.messages.extend(new_messages[:-1])
user_message = new_messages[-1]
elif len(new_messages) < 1:
user_message = ""
else:
user_message = new_messages[0]