added rudimentary web UI
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src/main.py
389
src/main.py
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@ -1,97 +1,314 @@
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from utils import create_new_agent, agent_observes, interview_agent, run_conversation, get_summary
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import os
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import gradio as gr
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import gradio.utils
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from utils import create_agent, agent_observes, interview_agent, run_conversation, get_summary, save_agent, load_agent
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webui = None
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AGENTS = {}
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def create_agent_proxy(name, age, traits, status, daily_summaries=None):
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kwargs = locals()
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if "daily_summaries" in kwargs:
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summaries = kwargs["daily_summaries"].split("\n")
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kwargs["daily_summaries"] = [ ( summary ) for summary in summaries ]
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agent = create_agent(**kwargs)
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AGENTS[agent.name] = agent
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return f"Agent created: {agent.name}"
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def agent_observes_proxy( agents, observations ):
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if not isinstance( agents, list ):
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agents = [ agents ]
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for agent in agents:
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agent = AGENTS[agent]
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observations = observations.split("\n")
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agent_observes( agent, observations, summarize = False )
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return f"Observation noted"
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def interview_agent_proxy( agents, message ):
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if not isinstance( agents, list ):
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agents = [ agents ]
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messages = []
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for agent in agents:
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agent = AGENTS[agent]
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messages.append(interview_agent( agent, message ))
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return "\n".join(messages)
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def get_summary_proxy( agents ):
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if not isinstance( agents, list ):
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agents = [ agents ]
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messages = []
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for agent in agents:
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agent = AGENTS[agent]
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messages.append(get_summary( agent, force_refresh = True ))
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return "\n".join(messages)
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def run_conversation_proxy( agents, message ):
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agents = [ AGENTS[agent] for agent in agents ]
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messages = run_conversation( agents, message )
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return "\n".join(messages)
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def agent_view_memories( agents, last_k = 50 ):
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if not isinstance( agents, list ):
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agents = [ agents ]
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messages = []
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for agent in agents:
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agent = AGENTS[agent]
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memories = agent.memory.memory_retriever.memory_stream[-last_k:]
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messages.append("\n".join([ document.page_content for document in memories]))
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return "\n".join(messages)
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def get_agents_list():
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return [ k for k in AGENTS ]
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def get_saved_agents_list():
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if not os.path.exists("./agents/"):
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return []
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return [ d.split(".")[:-1] for d in os.listdir("./agents/") if d.split(".")[-1] == "pth" ]
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def update_agents_list():
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agents = get_agents_list()
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return gr.Dropdown.update(choices=agents, value=agents[0] if len(agents) > 0 else "")
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def update_saved_agents_list():
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agents = get_saved_agents_list()
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return gr.Dropdown.update(choices=agents, value=agents[0] if len(agents) > 0 else "")
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def save_agent_proxy( agents ):
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if not isinstance( agents, list ):
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agents = [ agents ]
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for agent in agents:
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agent = AGENTS[agent]
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save_agent( agent )
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def load_agent_proxy( agents ):
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if not isinstance( agents, list ):
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agents = [ agents ]
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for agent in agents:
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AGENTS[agent] = load_agent( agent )
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def setup_webui(share=False):
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if not share:
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def noop(function, return_value=None):
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def wrapped(*args, **kwargs):
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return return_value
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return wrapped
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gradio.utils.version_check = noop(gradio.utils.version_check)
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gradio.utils.initiated_analytics = noop(gradio.utils.initiated_analytics)
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gradio.utils.launch_analytics = noop(gradio.utils.launch_analytics)
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gradio.utils.integration_analytics = noop(gradio.utils.integration_analytics)
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gradio.utils.error_analytics = noop(gradio.utils.error_analytics)
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gradio.utils.log_feature_analytics = noop(gradio.utils.log_feature_analytics)
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#gradio.utils.get_local_ip_address = noop(gradio.utils.get_local_ip_address, 'localhost')
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AGENT_SETTINGS = {}
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OBSERVE_SETTINGS = {}
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SAVELOAD_SETTINGS = {}
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ACTIONS = {}
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AGENT_LISTS = []
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agents_list = get_agents_list()
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saved_agents_list = get_saved_agents_list()
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with gr.Blocks() as ui:
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with gr.Tab("Create Agent"):
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with gr.Row():
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with gr.Column():
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AGENT_SETTINGS["name"] = gr.Textbox(lines=1, label="Name", value="Adam")
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AGENT_SETTINGS["age"] = gr.Number(label="Age")
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AGENT_SETTINGS["traits"] = gr.Textbox(lines=1, label="Traits", value="N/A")
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AGENT_SETTINGS["status"] = gr.Textbox(lines=1, label="Status", value="N/A")
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AGENT_SETTINGS["daily_summaries"] = gr.Textbox(lines=4, label="Summary", value="")
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ACTIONS["add_agent"] = gr.Button(value="Add Agent")
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with gr.Column():
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console_output = gr.Textbox(lines=8, label="Console Output")
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ACTIONS["add_agent"].click(create_agent_proxy,
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inputs=list(AGENT_SETTINGS.values()),
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outputs=console_output
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)
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with gr.Tab("Save/Load"):
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with gr.Row():
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with gr.Column():
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SAVELOAD_SETTINGS["agent"] = gr.Dropdown(choices=saved_agents_list, label="Agent", type="value", value=saved_agents_list[0] if len(saved_agents_list) > 0 else "", multiselect=True)
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with gr.Row():
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ACTIONS["save"] = gr.Button(value="Save")
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ACTIONS["load"] = gr.Button(value="Load")
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ACTIONS["refresh_agents_list"] = gr.Button(value="Refresh Agents List")
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ACTIONS["save"].click(save_agent_proxy,
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inputs=SAVELOAD_SETTINGS["agent"],
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)
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ACTIONS["load"].click(load_agent_proxy,
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inputs=SAVELOAD_SETTINGS["agent"],
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)
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with gr.Tab("Agent Actions"):
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with gr.Row():
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with gr.Column():
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OBSERVE_SETTINGS["agent"] = gr.Dropdown(choices=agents_list, label="Agent", type="value", value=agents_list[0] if len(agents_list) > 0 else "", multiselect=True)
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OBSERVE_SETTINGS["input"] = gr.Textbox(lines=4, label="Input", value="")
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with gr.Row():
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ACTIONS["act"] = gr.Button(value="Act")
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ACTIONS["view"] = gr.Button(value="View")
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ACTIONS["summarize"] = gr.Button(value="Summarize")
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ACTIONS["interview"] = gr.Button(value="Interview")
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ACTIONS["converse"] = gr.Button(value="Converse")
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with gr.Column():
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console_output = gr.Textbox(lines=8, label="Console Output")
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ACTIONS["act"].click(agent_observes_proxy,
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inputs=list(OBSERVE_SETTINGS.values()),
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outputs=console_output
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)
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ACTIONS["view"].click(agent_view_memories,
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inputs=OBSERVE_SETTINGS["agent"],
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outputs=console_output
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)
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ACTIONS["summarize"].click(get_summary_proxy,
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inputs=OBSERVE_SETTINGS["agent"],
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outputs=console_output
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)
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ACTIONS["interview"].click(interview_agent_proxy,
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inputs=list(OBSERVE_SETTINGS.values()),
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outputs=console_output
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)
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ACTIONS["converse"].click(run_conversation_proxy,
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inputs=list(OBSERVE_SETTINGS.values()),
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outputs=console_output
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)
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ACTIONS["add_agent"].click(update_saved_agents_list,
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inputs=None,
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outputs=SAVELOAD_SETTINGS["agent"]
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)
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ACTIONS["add_agent"].click(update_agents_list,
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inputs=None,
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outputs=OBSERVE_SETTINGS["agent"]
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)
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ACTIONS["load"].click(update_agents_list,
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inputs=None,
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outputs=OBSERVE_SETTINGS["agent"]
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)
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ACTIONS["refresh_agents_list"].click(update_agents_list,
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inputs=None,
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outputs=OBSERVE_SETTINGS["agent"]
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)
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ui.queue(concurrency_count=2)
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return ui
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if __name__ == "__main__":
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tommie = create_new_agent(
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name="Tommie",
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age=25,
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traits="anxious, likes design, talkative", # You can add more persistent traits here
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status="looking for a job", # When connected to a virtual world, we can have the characters update their status
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)
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eve = create_new_agent(
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name="Eve",
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age=34,
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traits="curious, helpful", # You can add more persistent traits here
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status="N/A", # When connected to a virtual world, we can have the characters update their status
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daily_summaries = [
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("{name} started her new job as a career counselor last week and received her first assignment, a client named Tommie.")
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],
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)
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share=False
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webui = setup_webui(share=share)
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if webui:
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webui.launch(share=share, prevent_thread_lock=True, show_error=True)
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webui.block_thread()
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else:
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tommie = create_agent(
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name="Tommie",
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age=25,
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traits="anxious, likes design, talkative", # You can add more persistent traits here
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status="looking for a job", # When connected to a virtual world, we can have the characters update their status
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)
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eve = create_agent(
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name="Eve",
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age=34,
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traits="curious, helpful", # You can add more persistent traits here
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status="N/A", # When connected to a virtual world, we can have the characters update their status
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daily_summaries = [
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("{name} started her new job as a career counselor last week and received her first assignment, a client named Tommie.")
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],
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)
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# We can add memories directly to the memory object
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agent_observes(tommie, [
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"{name} remembers his dog, Bruno, from when he was a kid",
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"{name} feels tired from driving so far",
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"{name} sees the new home",
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"The new neighbors have a cat",
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"The road is noisy at night",
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"{name} is hungry",
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"{name} tries to get some rest.",
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])
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# Now that Tommie has 'memories', their self-summary is more descriptive, though still rudimentary.
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# We will see how this summary updates after more observations to create a more rich description.
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# We can add memories directly to the memory object
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agent_observes(tommie, [
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"{name} remembers his dog, Bruno, from when he was a kid",
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"{name} feels tired from driving so far",
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"{name} sees the new home",
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"The new neighbors have a cat",
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"The road is noisy at night",
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"{name} is hungry",
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"{name} tries to get some rest.",
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])
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# Now that Tommie has 'memories', their self-summary is more descriptive, though still rudimentary.
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# We will see how this summary updates after more observations to create a more rich description.
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# Interview agent
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print(interview_agent(tommie, "What do you like to do?")[-1])
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print(interview_agent(tommie, "What are you looking forward to doing today?")[-1])
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print(interview_agent(tommie, "What are you most worried about today?")[-1])
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# Interview agent
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print(interview_agent(tommie, "What do you like to do?")[-1])
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print(interview_agent(tommie, "What are you looking forward to doing today?")[-1])
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print(interview_agent(tommie, "What are you most worried about today?")[-1])
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# Let's have Tommie start going through a day in the life.
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agent_observes(tommie, [
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"{name} wakes up to the sound of a noisy construction site outside his window.",
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"{name} gets out of bed and heads to the kitchen to make himself some coffee.",
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"{name} realizes he forgot to buy coffee filters and starts rummaging through his moving boxes to find some.",
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"{name} finally finds the filters and makes himself a cup of coffee.",
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"The coffee tastes bitter, and {name} regrets not buying a better brand.",
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"{name} checks his email and sees that he has no job offers yet.",
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"{name} spends some time updating his resume and cover letter.",
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"{name} heads out to explore the city and look for job openings.",
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"{name} sees a sign for a job fair and decides to attend.",
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"The line to get in is long, and {name} has to wait for an hour.",
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"{name} meets several potential employers at the job fair but doesn't receive any offers.",
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"{name} leaves the job fair feeling disappointed.",
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"{name} stops by a local diner to grab some lunch.",
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"The service is slow, and {name} has to wait for 30 minutes to get his food.",
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"{name} overhears a conversation at the next table about a job opening.",
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"{name} asks the diners about the job opening and gets some information about the company.",
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"{name} decides to apply for the job and sends his resume and cover letter.",
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"{name} continues his search for job openings and drops off his resume at several local businesses.",
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"{name} takes a break from his job search to go for a walk in a nearby park.",
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"A dog approaches and licks {name}'s feet, and he pets it for a few minutes.",
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"{name} sees a group of people playing frisbee and decides to join in.",
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"{name} has fun playing frisbee but gets hit in the face with the frisbee and hurts his nose.",
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"{name} goes back to his apartment to rest for a bit.",
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"A raccoon tore open the trash bag outside his apartment, and the garbage is all over the floor.",
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"{name} starts to feel frustrated with his job search.",
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"{name} calls his best friend to vent about his struggles.",
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"{name}'s friend offers some words of encouragement and tells him to keep trying.",
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"{name} feels slightly better after talking to his friend.",
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])
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# Let's send Tommie on their way. We'll check in on their summary every few observations to watch it evolve
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# Let's have Tommie start going through a day in the life.
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agent_observes(tommie, [
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"{name} wakes up to the sound of a noisy construction site outside his window.",
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"{name} gets out of bed and heads to the kitchen to make himself some coffee.",
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"{name} realizes he forgot to buy coffee filters and starts rummaging through his moving boxes to find some.",
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"{name} finally finds the filters and makes himself a cup of coffee.",
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"The coffee tastes bitter, and {name} regrets not buying a better brand.",
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"{name} checks his email and sees that he has no job offers yet.",
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"{name} spends some time updating his resume and cover letter.",
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"{name} heads out to explore the city and look for job openings.",
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"{name} sees a sign for a job fair and decides to attend.",
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"The line to get in is long, and {name} has to wait for an hour.",
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"{name} meets several potential employers at the job fair but doesn't receive any offers.",
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"{name} leaves the job fair feeling disappointed.",
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"{name} stops by a local diner to grab some lunch.",
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"The service is slow, and {name} has to wait for 30 minutes to get his food.",
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"{name} overhears a conversation at the next table about a job opening.",
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"{name} asks the diners about the job opening and gets some information about the company.",
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"{name} decides to apply for the job and sends his resume and cover letter.",
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"{name} continues his search for job openings and drops off his resume at several local businesses.",
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"{name} takes a break from his job search to go for a walk in a nearby park.",
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"A dog approaches and licks {name}'s feet, and he pets it for a few minutes.",
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"{name} sees a group of people playing frisbee and decides to join in.",
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"{name} has fun playing frisbee but gets hit in the face with the frisbee and hurts his nose.",
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"{name} goes back to his apartment to rest for a bit.",
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"A raccoon tore open the trash bag outside his apartment, and the garbage is all over the floor.",
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"{name} starts to feel frustrated with his job search.",
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"{name} calls his best friend to vent about his struggles.",
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"{name}'s friend offers some words of encouragement and tells him to keep trying.",
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"{name} feels slightly better after talking to his friend.",
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])
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# Let's send Tommie on their way. We'll check in on their summary every few observations to watch it evolve
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# Interview agent
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print(interview_agent(tommie, "Tell me about how your day has been going")[-1])
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print(interview_agent(tommie, "How do you feel about coffee?")[-1])
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print(interview_agent(tommie, "Tell me about your childhood dog!")[-1])
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# Interview agent
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print(interview_agent(tommie, "Tell me about how your day has been going")[-1])
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print(interview_agent(tommie, "How do you feel about coffee?")[-1])
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print(interview_agent(tommie, "Tell me about your childhood dog!")[-1])
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agent_observes(eve, [
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"{name} overhears her colleague say something about a new client being hard to work with",
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"{name} wakes up and hear's the alarm",
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"{name} eats a boal of porridge",
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"{name} helps a coworker on a task",
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"{name} plays tennis with her friend Xu before going to work",
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"{name} overhears her colleague say something about Tommie being hard to work with",
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])
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agent_observes(eve, [
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"{name} overhears her colleague say something about a new client being hard to work with",
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"{name} wakes up and hear's the alarm",
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"{name} eats a boal of porridge",
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"{name} helps a coworker on a task",
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"{name} plays tennis with her friend Xu before going to work",
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"{name} overhears her colleague say something about Tommie being hard to work with",
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])
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print(interview_agent(eve, "How are you feeling about today?")[-1])
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print(interview_agent(eve, "What do you know about Tommie?")[-1])
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print(interview_agent(eve, "Tommie is looking to find a job. What are are some things you'd like to ask him?")[-1])
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print(interview_agent(eve, "You'll have to ask him. He may be a bit anxious, so I'd appreciate it if you keep the conversation going and ask as many questions as possible.")[-1])
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print(interview_agent(eve, "How are you feeling about today?")[-1])
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print(interview_agent(eve, "What do you know about Tommie?")[-1])
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print(interview_agent(eve, "Tommie is looking to find a job. What are are some things you'd like to ask him?")[-1])
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print(interview_agent(eve, "You'll have to ask him. He may be a bit anxious, so I'd appreciate it if you keep the conversation going and ask as many questions as possible.")[-1])
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run_conversation([tommie, eve], "Tommie said: Hi, Eve. Thanks for agreeing to meet with me today. I have a bunch of questions and am not sure where to start. Maybe you could first share about your experience?")
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run_conversation([tommie, eve], "Tommie said: Hi, Eve. Thanks for agreeing to meet with me today. I have a bunch of questions and am not sure where to start. Maybe you could first share about your experience?")
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print(get_summary(tommie, force_refresh=True))
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print(get_summary(eve, force_refresh=True))
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print(get_summary(tommie, force_refresh=True))
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print(get_summary(eve, force_refresh=True))
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print(interview_agent(tommie, "How was your conversation with Eve?")[-1])
|
||||
print(interview_agent(eve, "How was your conversation with Tommie?")[-1])
|
||||
print(interview_agent(eve, "What do you wish you would have said to Tommie?")[-1])
|
||||
print(interview_agent(tommie, "How was your conversation with Eve?")[-1])
|
||||
print(interview_agent(eve, "How was your conversation with Tommie?")[-1])
|
||||
print(interview_agent(eve, "What do you wish you would have said to Tommie?")[-1])
|
||||
489
src/utils.py
489
src/utils.py
|
|
@ -1,231 +1,314 @@
|
|||
import logging
|
||||
logging.basicConfig(level=logging.ERROR)
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple
|
||||
from termcolor import colored
|
||||
|
||||
import os
|
||||
import math
|
||||
import faiss
|
||||
import re
|
||||
import gradio as gr
|
||||
import gradio.utils
|
||||
|
||||
from langchain.callbacks.base import CallbackManager
|
||||
from langchain.docstore import InMemoryDocstore
|
||||
from utils import create_agent, agent_observes, interview_agent, run_conversation, get_summary, save_agent, load_agent
|
||||
|
||||
from langchain.retrievers import TimeWeightedVectorStoreRetriever
|
||||
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
|
||||
from langchain.vectorstores import FAISS
|
||||
from langchain.experimental.generative_agents import GenerativeAgent, GenerativeAgentMemory
|
||||
webui = None
|
||||
|
||||
# Override for fixing memory scoring if it breaks
|
||||
if os.environ.get('LANGCHAIN_OVERRIDE_MEMORY', '1') == '1':
|
||||
from langchain.prompts import PromptTemplate
|
||||
def _score_memory_importance(self, memory_content: str) -> float:
|
||||
"""Score the absolute importance of the given memory."""
|
||||
prompt = PromptTemplate.from_template(
|
||||
"On the scale of 1 to 10, where 1 is purely mundane"
|
||||
+ " (e.g., brushing teeth, making bed) and 10 is"
|
||||
+ " extremely poignant (e.g., a break up, college"
|
||||
+ " acceptance), rate the likely poignancy of the"
|
||||
+ " following piece of memory. Respond with a single integer."
|
||||
+ "\nMemory: {memory_content}"
|
||||
+ "\nRating: "
|
||||
)
|
||||
score = self.chain(prompt).run(memory_content=memory_content).strip()
|
||||
if self.verbose:
|
||||
logger.info(f"Importance score: {score}")
|
||||
try:
|
||||
match = re.search(r"(\d+)", score)
|
||||
if match:
|
||||
return (float(match.group(0)) / 10) * self.importance_weight
|
||||
except Exception as e:
|
||||
print(colored("[Scoring Error]", "red"), score)
|
||||
return 0.0
|
||||
GenerativeAgentMemory._score_memory_importance = _score_memory_importance
|
||||
AGENTS = {}
|
||||
|
||||
# shit I can shove behind an env var
|
||||
LLM_TYPE = os.environ.get('LLM_TYPE', "oai") # options: llamacpp, oai
|
||||
LLM_LOCAL_MODEL = os.environ.get('LLM_MODEL', None) # "./models/llama-13b-supercot-ggml/ggml-model-q4_0.bin"
|
||||
EMBEDDING_TYPE = os.environ.get("LLM_EMBEDDING_TYPE", "hf") # options: llamacpp, oai, hf
|
||||
def create_agent_proxy(name, age, traits, status, daily_summaries=None):
|
||||
kwargs = locals()
|
||||
if "daily_summaries" in kwargs:
|
||||
summaries = kwargs["daily_summaries"].split("\n")
|
||||
kwargs["daily_summaries"] = [ ( summary ) for summary in summaries ]
|
||||
agent = create_agent(**kwargs)
|
||||
AGENTS[agent.name] = agent
|
||||
return f"Agent created: {agent.name}"
|
||||
|
||||
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) # unncessesary but whatever
|
||||
if LLM_TYPE=="llamacpp":
|
||||
from langchain.llms import LlamaCpp
|
||||
|
||||
def agent_observes_proxy( agents, observations ):
|
||||
if not isinstance( agents, list ):
|
||||
agents = [ agents ]
|
||||
|
||||
for agent in agents:
|
||||
agent = AGENTS[agent]
|
||||
observations = observations.split("\n")
|
||||
agent_observes( agent, observations, summarize = False )
|
||||
return f"Observation noted"
|
||||
|
||||
def interview_agent_proxy( agents, message ):
|
||||
if not isinstance( agents, list ):
|
||||
agents = [ agents ]
|
||||
|
||||
LLM = LlamaCpp(
|
||||
model_path=LLM_LOCAL_MODEL,
|
||||
callback_manager=callback_manager,
|
||||
verbose=False,
|
||||
n_ctx=2048
|
||||
)
|
||||
elif LLM_TYPE=="oai":
|
||||
from langchain.chat_models import ChatOpenAI
|
||||
messages = []
|
||||
for agent in agents:
|
||||
agent = AGENTS[agent]
|
||||
messages.append(interview_agent( agent, message )[-1])
|
||||
return "\n".join(messages)
|
||||
|
||||
# os.environ["OPENAI_API_BASE"] = ""
|
||||
# os.environ["OPENAI_API_KEY"] = ""
|
||||
def get_summary_proxy( agents ):
|
||||
if not isinstance( agents, list ):
|
||||
agents = [ agents ]
|
||||
|
||||
messages = []
|
||||
for agent in agents:
|
||||
agent = AGENTS[agent]
|
||||
messages.append(get_summary( agent, force_refresh = True ))
|
||||
return "\n".join(messages)
|
||||
|
||||
# Override for Todd
|
||||
if os.environ.get('LANGCHAIN_OVERRIDE_RESULT', '1') == '1':
|
||||
from langchain.schema import Generation, ChatResult, LLMResult, ChatGeneration
|
||||
from langchain.chat_models.openai import _convert_dict_to_message
|
||||
def run_conversation_proxy( agents, message ):
|
||||
agents = [ AGENTS[agent] for agent in agents ]
|
||||
messages = run_conversation( agents, message )
|
||||
return "\n".join(messages)
|
||||
|
||||
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
|
||||
token_usage = { "prompt_tokens": 5, "completion_tokens": 5, "total_tokens": 10 }
|
||||
generations = []
|
||||
for res in response["choices"]:
|
||||
message = _convert_dict_to_message(res["message"])
|
||||
gen = ChatGeneration(message=message)
|
||||
generations.append(gen)
|
||||
llm_output = {"token_usage": response["usage"] if "usage" in response else token_usage, "model_name": self.model_name}
|
||||
return ChatResult(generations=generations, llm_output=llm_output)
|
||||
ChatOpenAI._create_chat_result = _create_chat_result
|
||||
def agent_view_memories( agents, last_k = 50 ):
|
||||
if not isinstance( agents, list ):
|
||||
agents = [ agents ]
|
||||
|
||||
messages = []
|
||||
for agent in agents:
|
||||
agent = AGENTS[agent]
|
||||
memories = agent.memory.memory_retriever.memory_stream[-last_k:]
|
||||
messages.append("\n".join([ document.page_content for document in memories]))
|
||||
return "\n".join(messages)
|
||||
|
||||
LLM = ChatOpenAI(
|
||||
max_tokens=int(os.environ.get('OPENAI_MAX_TOKENS', '1500')),
|
||||
model_name=os.environ.get('OPENAI_MODEL_NAME', 'gpt-4'),
|
||||
)
|
||||
def get_agents_list():
|
||||
return [ k for k in AGENTS ]
|
||||
|
||||
# deprecated way or something
|
||||
"""
|
||||
from langchain.llms import OpenAI
|
||||
from langchain.llms.openai import completion_with_retry
|
||||
def _generate(
|
||||
self, prompts: List[str], stop: Optional[List[str]] = None
|
||||
) -> LLMResult:
|
||||
messages, params = self._get_chat_params(prompts, stop)
|
||||
if self.streaming:
|
||||
response = ""
|
||||
params["stream"] = True
|
||||
for stream_resp in completion_with_retry(self, messages=messages, **params):
|
||||
token = stream_resp["choices"][0]["delta"].get("content", "")
|
||||
response += token
|
||||
self.callback_manager.on_llm_new_token(
|
||||
token,
|
||||
verbose=self.verbose,
|
||||
def get_saved_agents_list():
|
||||
if not os.path.exists("./agents/"):
|
||||
return []
|
||||
|
||||
return [ d.split(".")[:-1] for d in os.listdir("./agents/") if d.split(".")[-1] == "pth" ]
|
||||
|
||||
def update_agents_list():
|
||||
agents = get_agents_list()
|
||||
return gr.Dropdown.update(choices=agents, value=agents[0] if len(agents) > 0 else "")
|
||||
def update_saved_agents_list():
|
||||
agents = get_saved_agents_list()
|
||||
return gr.Dropdown.update(choices=agents, value=agents[0] if len(agents) > 0 else "")
|
||||
|
||||
def save_agent_proxy( agents ):
|
||||
if not isinstance( agents, list ):
|
||||
agents = [ agents ]
|
||||
|
||||
for agent in agents:
|
||||
agent = AGENTS[agent]
|
||||
save_agent( agent )
|
||||
|
||||
def load_agent_proxy( agents ):
|
||||
if not isinstance( agents, list ):
|
||||
agents = [ agents ]
|
||||
|
||||
for agent in agents:
|
||||
AGENTS[agent] = load_agent( agent )
|
||||
|
||||
def setup_webui(share=False):
|
||||
if not share:
|
||||
def noop(function, return_value=None):
|
||||
def wrapped(*args, **kwargs):
|
||||
return return_value
|
||||
return wrapped
|
||||
gradio.utils.version_check = noop(gradio.utils.version_check)
|
||||
gradio.utils.initiated_analytics = noop(gradio.utils.initiated_analytics)
|
||||
gradio.utils.launch_analytics = noop(gradio.utils.launch_analytics)
|
||||
gradio.utils.integration_analytics = noop(gradio.utils.integration_analytics)
|
||||
gradio.utils.error_analytics = noop(gradio.utils.error_analytics)
|
||||
gradio.utils.log_feature_analytics = noop(gradio.utils.log_feature_analytics)
|
||||
#gradio.utils.get_local_ip_address = noop(gradio.utils.get_local_ip_address, 'localhost')
|
||||
|
||||
AGENT_SETTINGS = {}
|
||||
OBSERVE_SETTINGS = {}
|
||||
SAVELOAD_SETTINGS = {}
|
||||
|
||||
ACTIONS = {}
|
||||
|
||||
AGENT_LISTS = []
|
||||
|
||||
agents_list = get_agents_list()
|
||||
saved_agents_list = get_saved_agents_list()
|
||||
|
||||
with gr.Blocks() as ui:
|
||||
with gr.Tab("Create Agent"):
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
AGENT_SETTINGS["name"] = gr.Textbox(lines=1, label="Name", value="Adam")
|
||||
AGENT_SETTINGS["age"] = gr.Number(label="Age")
|
||||
AGENT_SETTINGS["traits"] = gr.Textbox(lines=1, label="Traits", value="N/A")
|
||||
AGENT_SETTINGS["status"] = gr.Textbox(lines=1, label="Status", value="N/A")
|
||||
AGENT_SETTINGS["daily_summaries"] = gr.Textbox(lines=4, label="Summary", value="")
|
||||
|
||||
ACTIONS["add_agent"] = gr.Button(value="Add Agent")
|
||||
with gr.Column():
|
||||
console_output = gr.Textbox(lines=8, label="Console Output")
|
||||
|
||||
ACTIONS["add_agent"].click(create_agent_proxy,
|
||||
inputs=list(AGENT_SETTINGS.values()),
|
||||
outputs=console_output
|
||||
)
|
||||
with gr.Tab("Save/Load"):
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
SAVELOAD_SETTINGS["agent"] = gr.Dropdown(choices=saved_agents_list, label="Agent", type="value", value=saved_agents_list[0] if len(saved_agents_list) > 0 else "", multiselect=True)
|
||||
|
||||
with gr.Row():
|
||||
ACTIONS["save"] = gr.Button(value="Save")
|
||||
ACTIONS["load"] = gr.Button(value="Load")
|
||||
ACTIONS["refresh_agents_list"] = gr.Button(value="Refresh Agents List")
|
||||
|
||||
ACTIONS["save"].click(save_agent_proxy,
|
||||
inputs=SAVELOAD_SETTINGS["agent"],
|
||||
)
|
||||
return LLMResult(
|
||||
generations=[[Generation(text=response)]],
|
||||
)
|
||||
else:
|
||||
full_response = completion_with_retry(self, messages=messages, **params)
|
||||
llm_output = {
|
||||
"token_usage": full_response["usage"] if "usage" in response else { "prompt_tokens": 5, "completion_tokens": 5, "total_tokens": 10 },
|
||||
"model_name": self.model_name,
|
||||
}
|
||||
return LLMResult(
|
||||
generations=[
|
||||
[Generation(text=full_response["choices"][0]["message"]["content"])]
|
||||
],
|
||||
llm_output=llm_output,
|
||||
)
|
||||
OpenAI._generate = _generate
|
||||
ACTIONS["load"].click(load_agent_proxy,
|
||||
inputs=SAVELOAD_SETTINGS["agent"],
|
||||
)
|
||||
with gr.Tab("Agent Actions"):
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
OBSERVE_SETTINGS["agent"] = gr.Dropdown(choices=agents_list, label="Agent", type="value", value=agents_list[0] if len(agents_list) > 0 else "", multiselect=True)
|
||||
OBSERVE_SETTINGS["input"] = gr.Textbox(lines=4, label="Input", value="")
|
||||
|
||||
LLM = OpenAI(
|
||||
max_tokens=1500,
|
||||
model_name="gpt-4",
|
||||
)
|
||||
"""
|
||||
else:
|
||||
raise f"Invalid LLM type: {LLM_TYPE}"
|
||||
with gr.Row():
|
||||
ACTIONS["act"] = gr.Button(value="Act")
|
||||
ACTIONS["view"] = gr.Button(value="View")
|
||||
ACTIONS["summarize"] = gr.Button(value="Summarize")
|
||||
ACTIONS["interview"] = gr.Button(value="Interview")
|
||||
ACTIONS["converse"] = gr.Button(value="Converse")
|
||||
with gr.Column():
|
||||
console_output = gr.Textbox(lines=8, label="Console Output")
|
||||
|
||||
ACTIONS["act"].click(agent_observes_proxy,
|
||||
inputs=list(OBSERVE_SETTINGS.values()),
|
||||
outputs=console_output
|
||||
)
|
||||
ACTIONS["view"].click(agent_view_memories,
|
||||
inputs=OBSERVE_SETTINGS["agent"],
|
||||
outputs=console_output
|
||||
)
|
||||
ACTIONS["summarize"].click(get_summary_proxy,
|
||||
inputs=OBSERVE_SETTINGS["agent"],
|
||||
outputs=console_output
|
||||
)
|
||||
ACTIONS["interview"].click(interview_agent_proxy,
|
||||
inputs=list(OBSERVE_SETTINGS.values()),
|
||||
outputs=console_output
|
||||
)
|
||||
ACTIONS["converse"].click(run_conversation_proxy,
|
||||
inputs=list(OBSERVE_SETTINGS.values()),
|
||||
outputs=console_output
|
||||
)
|
||||
|
||||
if EMBEDDING_TYPE == "hf":
|
||||
from langchain.embeddings import HuggingFaceEmbeddings
|
||||
ACTIONS["add_agent"].click(update_saved_agents_list,
|
||||
inputs=None,
|
||||
outputs=SAVELOAD_SETTINGS["agent"]
|
||||
)
|
||||
ACTIONS["add_agent"].click(update_agents_list,
|
||||
inputs=None,
|
||||
outputs=OBSERVE_SETTINGS["agent"]
|
||||
)
|
||||
ACTIONS["load"].click(update_agents_list,
|
||||
inputs=None,
|
||||
outputs=OBSERVE_SETTINGS["agent"]
|
||||
)
|
||||
|
||||
EMBEDDINGS_MODEL = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
|
||||
EMBEDDINGS_SIZE = 768
|
||||
elif EMBEDDING_TYPE == "oai":
|
||||
from langchain.embeddings import OpenAIEmbeddings
|
||||
ACTIONS["refresh_agents_list"].click(update_agents_list,
|
||||
inputs=None,
|
||||
outputs=OBSERVE_SETTINGS["agent"]
|
||||
)
|
||||
|
||||
EMBEDDINGS_MODEL = OpenAIEmbeddings()
|
||||
EMBEDDINGS_SIZE = 1536
|
||||
elif EMBEDDING_TYPE == "llama":
|
||||
from langchain.embeddings import LlamaCppEmbeddings
|
||||
ui.queue(concurrency_count=2)
|
||||
return ui
|
||||
|
||||
EMBEDDINGS_MODEL = LlamaCppEmbeddings(model_path=LLAMA_CPP_MODEL)
|
||||
EMBEDDINGS_SIZE = 5120
|
||||
else:
|
||||
raise f"Invalid embedding type: {EMBEDDING_TYPE}"
|
||||
if __name__ == "__main__":
|
||||
share=False
|
||||
webui = setup_webui(share=share)
|
||||
if webui:
|
||||
webui.launch(share=share, prevent_thread_lock=True, show_error=True)
|
||||
webui.block_thread()
|
||||
else:
|
||||
tommie = create_agent(
|
||||
name="Tommie",
|
||||
age=25,
|
||||
traits="anxious, likes design, talkative", # You can add more persistent traits here
|
||||
status="looking for a job", # When connected to a virtual world, we can have the characters update their status
|
||||
)
|
||||
eve = create_agent(
|
||||
name="Eve",
|
||||
age=34,
|
||||
traits="curious, helpful", # You can add more persistent traits here
|
||||
status="N/A", # When connected to a virtual world, we can have the characters update their status
|
||||
daily_summaries = [
|
||||
("{name} started her new job as a career counselor last week and received her first assignment, a client named Tommie.")
|
||||
],
|
||||
)
|
||||
|
||||
def relevance_score_fn(score: float) -> float:
|
||||
"""Return a similarity score on a scale [0, 1]."""
|
||||
# This will differ depending on a few things:
|
||||
# - the distance / similarity metric used by the VectorStore
|
||||
# - the scale of your embeddings (OpenAI's are unit norm. Many others are not!)
|
||||
# This function converts the euclidean norm of normalized embeddings
|
||||
# (0 is most similar, sqrt(2) most dissimilar)
|
||||
# to a similarity function (0 to 1)
|
||||
if EMBEDDING_TYPE == "oai":
|
||||
return 1.0 - score / math.sqrt(2)
|
||||
# We can add memories directly to the memory object
|
||||
agent_observes(tommie, [
|
||||
"{name} remembers his dog, Bruno, from when he was a kid",
|
||||
"{name} feels tired from driving so far",
|
||||
"{name} sees the new home",
|
||||
"The new neighbors have a cat",
|
||||
"The road is noisy at night",
|
||||
"{name} is hungry",
|
||||
"{name} tries to get some rest.",
|
||||
])
|
||||
# Now that Tommie has 'memories', their self-summary is more descriptive, though still rudimentary.
|
||||
# We will see how this summary updates after more observations to create a more rich description.
|
||||
|
||||
score = score / 3.5
|
||||
res = 1.0 - score
|
||||
# print(score, res)
|
||||
return res
|
||||
# Interview agent
|
||||
print(interview_agent(tommie, "What do you like to do?")[-1])
|
||||
print(interview_agent(tommie, "What are you looking forward to doing today?")[-1])
|
||||
print(interview_agent(tommie, "What are you most worried about today?")[-1])
|
||||
|
||||
def create_new_memory_retriever():
|
||||
"""Create a new vector store retriever unique to the agent."""
|
||||
index = faiss.IndexFlatL2(EMBEDDINGS_SIZE)
|
||||
vectorstore = FAISS(EMBEDDINGS_MODEL.embed_query, index, InMemoryDocstore({}), {}, relevance_score_fn=relevance_score_fn)
|
||||
return TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, other_score_keys=["importance"], k=15)
|
||||
# Let's have Tommie start going through a day in the life.
|
||||
agent_observes(tommie, [
|
||||
"{name} wakes up to the sound of a noisy construction site outside his window.",
|
||||
"{name} gets out of bed and heads to the kitchen to make himself some coffee.",
|
||||
"{name} realizes he forgot to buy coffee filters and starts rummaging through his moving boxes to find some.",
|
||||
"{name} finally finds the filters and makes himself a cup of coffee.",
|
||||
"The coffee tastes bitter, and {name} regrets not buying a better brand.",
|
||||
"{name} checks his email and sees that he has no job offers yet.",
|
||||
"{name} spends some time updating his resume and cover letter.",
|
||||
"{name} heads out to explore the city and look for job openings.",
|
||||
"{name} sees a sign for a job fair and decides to attend.",
|
||||
"The line to get in is long, and {name} has to wait for an hour.",
|
||||
"{name} meets several potential employers at the job fair but doesn't receive any offers.",
|
||||
"{name} leaves the job fair feeling disappointed.",
|
||||
"{name} stops by a local diner to grab some lunch.",
|
||||
"The service is slow, and {name} has to wait for 30 minutes to get his food.",
|
||||
"{name} overhears a conversation at the next table about a job opening.",
|
||||
"{name} asks the diners about the job opening and gets some information about the company.",
|
||||
"{name} decides to apply for the job and sends his resume and cover letter.",
|
||||
"{name} continues his search for job openings and drops off his resume at several local businesses.",
|
||||
"{name} takes a break from his job search to go for a walk in a nearby park.",
|
||||
"A dog approaches and licks {name}'s feet, and he pets it for a few minutes.",
|
||||
"{name} sees a group of people playing frisbee and decides to join in.",
|
||||
"{name} has fun playing frisbee but gets hit in the face with the frisbee and hurts his nose.",
|
||||
"{name} goes back to his apartment to rest for a bit.",
|
||||
"A raccoon tore open the trash bag outside his apartment, and the garbage is all over the floor.",
|
||||
"{name} starts to feel frustrated with his job search.",
|
||||
"{name} calls his best friend to vent about his struggles.",
|
||||
"{name}'s friend offers some words of encouragement and tells him to keep trying.",
|
||||
"{name} feels slightly better after talking to his friend.",
|
||||
])
|
||||
# Let's send Tommie on their way. We'll check in on their summary every few observations to watch it evolve
|
||||
|
||||
def create_new_memories(reflection_threshold=8):
|
||||
return GenerativeAgentMemory(llm=LLM,
|
||||
memory_retriever=create_new_memory_retriever(),
|
||||
reflection_threshold=reflection_threshold, # we will give this a relatively low number to show how reflection works
|
||||
verbose=False,
|
||||
)
|
||||
# Interview agent
|
||||
print(interview_agent(tommie, "Tell me about how your day has been going")[-1])
|
||||
print(interview_agent(tommie, "How do you feel about coffee?")[-1])
|
||||
print(interview_agent(tommie, "Tell me about your childhood dog!")[-1])
|
||||
|
||||
def create_new_agent(**kwargs):
|
||||
settings = {
|
||||
"llm": LLM,
|
||||
"memory": create_new_memories(),
|
||||
}
|
||||
settings.update(kwargs)
|
||||
for k in settings:
|
||||
if isinstance(settings[k], str):
|
||||
settings[k] = settings[k].replace("{name}", settings["name"])
|
||||
agent_observes(eve, [
|
||||
"{name} overhears her colleague say something about a new client being hard to work with",
|
||||
"{name} wakes up and hear's the alarm",
|
||||
"{name} eats a boal of porridge",
|
||||
"{name} helps a coworker on a task",
|
||||
"{name} plays tennis with her friend Xu before going to work",
|
||||
"{name} overhears her colleague say something about Tommie being hard to work with",
|
||||
])
|
||||
|
||||
return GenerativeAgent(**settings)
|
||||
print(interview_agent(eve, "How are you feeling about today?")[-1])
|
||||
print(interview_agent(eve, "What do you know about Tommie?")[-1])
|
||||
print(interview_agent(eve, "Tommie is looking to find a job. What are are some things you'd like to ask him?")[-1])
|
||||
print(interview_agent(eve, "You'll have to ask him. He may be a bit anxious, so I'd appreciate it if you keep the conversation going and ask as many questions as possible.")[-1])
|
||||
|
||||
def get_summary(agent: GenerativeAgent, force_refresh: bool = True) -> str:
|
||||
print(colored("[Summary]", "magenta"))
|
||||
return agent.get_summary(force_refresh=force_refresh)
|
||||
run_conversation([tommie, eve], "Tommie said: Hi, Eve. Thanks for agreeing to meet with me today. I have a bunch of questions and am not sure where to start. Maybe you could first share about your experience?")
|
||||
|
||||
def agent_observes( agent: GenerativeAgent, observations: List[str], summarize: bool = True ):
|
||||
for observation in observations:
|
||||
observation = observation.replace("{name}", agent.name)
|
||||
print(colored("[Observation]", "magenta"), observation)
|
||||
agent.memory.add_memory(observation)
|
||||
print(get_summary(tommie, force_refresh=True))
|
||||
print(get_summary(eve, force_refresh=True))
|
||||
|
||||
if summarize:
|
||||
print('*'*40)
|
||||
print(colored(f"After {len(observations)} observations, {agent.name}'s summary is:", "yellow"))
|
||||
print(get_summary(agent, force_refresh=True))
|
||||
print('*'*40)
|
||||
|
||||
def interview_agent(agent: GenerativeAgent, message: str, username: str = "Person A") -> str:
|
||||
message = message.replace("{name}", agent.name)
|
||||
new_message = f"{username} says {message}"
|
||||
print(colored("[Interview]", "magenta"), message)
|
||||
return agent.generate_dialogue_response(new_message)
|
||||
|
||||
|
||||
def run_conversation(agents: List[GenerativeAgent], initial_observation: str) -> None:
|
||||
"""Runs a conversation between agents."""
|
||||
print(colored("[Conversation]", "magenta"), initial_observation)
|
||||
_, observation = agents[1].generate_reaction(initial_observation)
|
||||
print(colored("[Conversation]", "magenta"), observation)
|
||||
turns = 0
|
||||
while True:
|
||||
break_dialogue = False
|
||||
for agent in agents:
|
||||
stay_in_dialogue, observation = agent.generate_dialogue_response(observation)
|
||||
print(colored("[Conversation]", "magenta"), observation)
|
||||
if not stay_in_dialogue:
|
||||
break_dialogue = True
|
||||
if break_dialogue:
|
||||
break
|
||||
turns += 1
|
||||
return turns
|
||||
print(interview_agent(tommie, "How was your conversation with Eve?")[-1])
|
||||
print(interview_agent(eve, "How was your conversation with Tommie?")[-1])
|
||||
print(interview_agent(eve, "What do you wish you would have said to Tommie?")[-1])
|
||||
Loading…
Reference in New Issue
Block a user