174 lines
6.7 KiB
Python
174 lines
6.7 KiB
Python
"""Example of how to add MemGPT into an AutoGen groupchat and chat with docs.
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See https://memgpt.readme.io/docs/autogen#part-4-attaching-documents-to-memgpt-autogen-agents
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Based on the official AutoGen example here: https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb
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Begin by doing:
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pip install "pyautogen[teachable]"
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pip install pymemgpt
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or
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pip install -e . (inside the MemGPT home directory)
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"""
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import os
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import autogen
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from memgpt.autogen.memgpt_agent import create_memgpt_autogen_agent_from_config
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from memgpt.constants import DEFAULT_PRESET, LLM_MAX_TOKENS
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LLM_BACKEND = "openai"
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# LLM_BACKEND = "azure"
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# LLM_BACKEND = "local"
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if LLM_BACKEND == "openai":
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# For demo purposes let's use gpt-4
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model = "gpt-4"
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openai_api_key = os.getenv("OPENAI_API_KEY")
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assert openai_api_key, "You must set OPENAI_API_KEY or set LLM_BACKEND to 'local' to run this example"
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# This config is for AutoGen agents that are not powered by MemGPT
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config_list = [
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{
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"model": model,
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"api_key": os.getenv("OPENAI_API_KEY"),
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}
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]
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# This config is for AutoGen agents that powered by MemGPT
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config_list_memgpt = [
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{
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"model": model,
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"context_window": LLM_MAX_TOKENS[model],
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"preset": DEFAULT_PRESET,
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"model_wrapper": None,
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# OpenAI specific
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"model_endpoint_type": "openai",
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"model_endpoint": "https://api.openai.com/v1",
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"openai_key": openai_api_key,
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},
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]
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elif LLM_BACKEND == "azure":
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# Make sure that you have access to this deployment/model on your Azure account!
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# If you don't have access to the model, the code will fail
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model = "gpt-4"
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azure_openai_api_key = os.getenv("AZURE_OPENAI_KEY")
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azure_openai_version = os.getenv("AZURE_OPENAI_VERSION")
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azure_openai_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
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assert (
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azure_openai_api_key is not None and azure_openai_version is not None and azure_openai_endpoint is not None
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), "Set all the required OpenAI Azure variables (see: https://memgpt.readme.io/docs/endpoints#azure-openai)"
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# This config is for AutoGen agents that are not powered by MemGPT
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config_list = [
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{
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"model": model,
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"api_type": "azure",
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"api_key": azure_openai_api_key,
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"api_version": azure_openai_version,
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# NOTE: on versions of pyautogen < 0.2.0, use "api_base"
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# "api_base": azure_openai_endpoint,
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"base_url": azure_openai_endpoint,
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}
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]
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# This config is for AutoGen agents that powered by MemGPT
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config_list_memgpt = [
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{
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"model": model,
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"context_window": LLM_MAX_TOKENS[model],
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"preset": DEFAULT_PRESET,
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"model_wrapper": None,
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# Azure specific
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"model_endpoint_type": "azure",
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"azure_key": azure_openai_api_key,
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"azure_endpoint": azure_openai_endpoint,
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"azure_version": azure_openai_version,
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},
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]
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elif LLM_BACKEND == "local":
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# Example using LM Studio on a local machine
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# You will have to change the parameters based on your setup
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# Non-MemGPT agents will still use local LLMs, but they will use the ChatCompletions endpoint
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config_list = [
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{
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"model": "NULL", # not needed
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# NOTE: on versions of pyautogen < 0.2.0 use "api_base", and also uncomment "api_type"
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# "api_base": "http://localhost:1234/v1",
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# "api_type": "open_ai",
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"base_url": "http://localhost:1234/v1", # ex. "http://127.0.0.1:5001/v1" if you are using webui, "http://localhost:1234/v1/" if you are using LM Studio
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"api_key": "NULL", # not needed
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},
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]
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# MemGPT-powered agents will also use local LLMs, but they need additional setup (also they use the Completions endpoint)
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config_list_memgpt = [
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{
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"preset": DEFAULT_PRESET,
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"model": None, # only required for Ollama, see: https://memgpt.readme.io/docs/ollama
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"context_window": 8192, # the context window of your model (for Mistral 7B-based models, it's likely 8192)
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"model_wrapper": "chatml", # chatml is the default wrapper
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"model_endpoint_type": "lmstudio", # can use webui, ollama, llamacpp, etc.
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"model_endpoint": "http://localhost:1234", # the IP address of your LLM backend
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},
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]
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else:
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raise ValueError(LLM_BACKEND)
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# Set to True if you want to print MemGPT's inner workings.
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DEBUG = False
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interface_kwargs = {
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"debug": DEBUG,
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"show_inner_thoughts": True,
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"show_function_outputs": True, # let's set this to True so that we can see the search function in action
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}
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llm_config = {"config_list": config_list, "seed": 42}
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llm_config_memgpt = {"config_list": config_list_memgpt, "seed": 42}
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# The user agent
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user_proxy = autogen.UserProxyAgent(
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name="User_proxy",
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system_message="A human admin.",
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code_execution_config={"last_n_messages": 2, "work_dir": "groupchat"},
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human_input_mode="TERMINATE", # needed?
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default_auto_reply="...", # Set a default auto-reply message here (non-empty auto-reply is required for LM Studio)
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)
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# In our example, we swap this AutoGen agent with a MemGPT agent
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# This MemGPT agent will have all the benefits of MemGPT, ie persistent memory, etc.
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memgpt_agent = create_memgpt_autogen_agent_from_config(
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"MemGPT_agent",
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llm_config=llm_config_memgpt,
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system_message=f"You are an AI research assistant.\n" f"You are participating in a group chat with a user ({user_proxy.name}).",
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interface_kwargs=interface_kwargs,
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default_auto_reply="...", # Set a default auto-reply message here (non-empty auto-reply is required for LM Studio)
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skip_verify=False, # NOTE: you should set this to True if you expect your MemGPT AutoGen agent to call a function other than send_message on the first turn
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)
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# NOTE: you need to follow steps to load document first: see https://memgpt.readme.io/docs/autogen#part-4-attaching-documents-to-memgpt-autogen-agents
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memgpt_agent.load_and_attach(
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name="memgpt_research_paper",
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type="directory",
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input_dir=None,
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input_files=["memgpt_research_paper.pdf"],
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# force=True,
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)
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# Initialize the group chat between the agents
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groupchat = autogen.GroupChat(agents=[user_proxy, memgpt_agent], messages=[], max_round=3, speaker_selection_method="round_robin")
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manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)
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# Begin the group chat with a message from the user
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user_proxy.initiate_chat(
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manager,
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message="Tell me what virtual context in MemGPT is. Search your archival memory.",
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)
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