MemGPT/main.py

232 lines
10 KiB
Python

import asyncio
from absl import app, flags
import logging
import os
import sys
import pickle
from rich.console import Console
console = Console()
import interface # for printing to terminal
import memgpt.agent as agent
import memgpt.system as system
import memgpt.utils as utils
import memgpt.presets as presets
import memgpt.constants as constants
import memgpt.personas.personas as personas
import memgpt.humans.humans as humans
from memgpt.persistence_manager import InMemoryStateManager, InMemoryStateManagerWithPreloadedArchivalMemory, InMemoryStateManagerWithFaiss
FLAGS = flags.FLAGS
flags.DEFINE_string("persona", default=personas.DEFAULT, required=False, help="Specify persona")
flags.DEFINE_string("human", default=humans.DEFAULT, required=False, help="Specify human")
flags.DEFINE_string("model", default=constants.DEFAULT_MEMGPT_MODEL, required=False, help="Specify the LLM model")
flags.DEFINE_boolean("first", default=False, required=False, help="Use -first to send the first message in the sequence")
flags.DEFINE_boolean("debug", default=False, required=False, help="Use -debug to enable debugging output")
flags.DEFINE_string("archival_storage_faiss_path", default="", required=False, help="Specify archival storage with FAISS index to load (a folder with a .index and .json describing documents to be loaded)")
flags.DEFINE_string("archival_storage_files", default="", required=False, help="Specify files to pre-load into archival memory (glob pattern)")
flags.DEFINE_string("archival_storage_sqldb", default="", required=False, help="Specify SQL database to pre-load into archival memory")
def clear_line():
# print(f"os.name = {os.name}")
if os.name == 'nt': # for windows
console.print("\033[A\033[K", end="")
else: # for linux
# console.print("\033[2K\033[G", end="")
sys.stdout.write("\033[2K\033[G")
sys.stdout.flush()
async def main():
utils.DEBUG = FLAGS.debug
logging.getLogger().setLevel(logging.CRITICAL)
if FLAGS.debug:
logging.getLogger().setLevel(logging.DEBUG)
print("Running... [exit by typing '/exit']")
if FLAGS.archival_storage_faiss_path:
index, archival_database = utils.prepare_archival_index(FLAGS.archival_storage_faiss_path)
persistence_manager = InMemoryStateManagerWithFaiss(index, archival_database)
elif FLAGS.archival_storage_files:
archival_database = utils.prepare_archival_index_from_files(FLAGS.archival_storage_files)
print(f"Preloaded {len(archival_database)} chunks into archival memory.")
persistence_manager = InMemoryStateManagerWithPreloadedArchivalMemory(archival_database)
else:
persistence_manager = InMemoryStateManager()
memgpt_agent = presets.use_preset(presets.DEFAULT, FLAGS.model, personas.get_persona_text(FLAGS.persona), humans.get_human_text(FLAGS.human), interface, persistence_manager)
print_messages = interface.print_messages
await print_messages(memgpt_agent.messages)
counter = 0
user_input = None
skip_next_user_input = False
user_message = None
USER_GOES_FIRST = FLAGS.first
if FLAGS.archival_storage_sqldb:
if not os.path.exists(FLAGS.archival_storage_sqldb):
print(f"File {user_input} does not exist")
return
# Ingest data from file into archival storage
else:
print(f"Database found! Loading database into archival memory")
data_list = utils.read_database_as_list(FLAGS.archival_storage_sqldb)
user_message = f"Your archival memory has been loaded with a SQL database called {data_list[0]}, which contains schema {data_list[1]}. Remember to refer to this first while answering any user questions!"
for row in data_list:
await memgpt_agent.persistence_manager.archival_memory.insert(row)
print(f"Database loaded into archival memory.")
if not USER_GOES_FIRST:
console.input('[bold cyan]Hit enter to begin (will request first MemGPT message)[/bold cyan]')
clear_line()
print()
while True:
if not skip_next_user_input and (counter > 0 or USER_GOES_FIRST):
# Ask for user input
user_input = console.input("[bold cyan]Enter your message:[/bold cyan] ")
clear_line()
if user_input.startswith('!'):
print(f"Commands for CLI begin with '/' not '!'")
continue
if user_input == "":
# no empty messages allowed
print("Empty input received. Try again!")
continue
# Handle CLI commands
# Commands to not get passed as input to MemGPT
if user_input.startswith('/'):
if user_input.lower() == "/exit":
break
elif user_input.lower() == "/savechat":
filename = utils.get_local_time().replace(' ', '_').replace(':', '_')
filename = f"{filename}.pkl"
try:
if not os.path.exists("saved_chats"):
os.makedirs("saved_chats")
with open(os.path.join('saved_chats', filename), 'wb') as f:
pickle.dump(memgpt_agent.messages, f)
print(f"Saved messages to: {filename}")
except Exception as e:
print(f"Saving chat to {filename} failed with: {e}")
continue
elif user_input.lower() == "/save":
filename = utils.get_local_time().replace(' ', '_').replace(':', '_')
filename = f"{filename}.json"
filename = os.path.join('saved_state', filename)
try:
if not os.path.exists("saved_state"):
os.makedirs("saved_state")
memgpt_agent.save_to_json_file(filename)
print(f"Saved checkpoint to: {filename}")
except Exception as e:
print(f"Saving state to {filename} failed with: {e}")
continue
elif user_input.lower() == "/load" or user_input.lower().startswith("/load "):
command = user_input.strip().split()
filename = command[1] if len(command) > 1 else None
if filename is not None:
try:
memgpt_agent.load_from_json_file_inplace(filename)
print(f"Loaded checkpoint {filename}")
except Exception as e:
print(f"Loading {filename} failed with: {e}")
else:
print(f"/load error: no checkpoint specified")
continue
elif user_input.lower() == "/dump":
await print_messages(memgpt_agent.messages)
continue
elif user_input.lower() == "/dumpraw":
await interface.print_messages_raw(memgpt_agent.messages)
continue
elif user_input.lower() == "/dump1":
await print_messages(memgpt_agent.messages[-1])
continue
elif user_input.lower() == "/memory":
print(f"\nDumping memory contents:\n")
print(f"{str(memgpt_agent.memory)}")
print(f"{str(memgpt_agent.persistence_manager.archival_memory)}")
print(f"{str(memgpt_agent.persistence_manager.recall_memory)}")
continue
elif user_input.lower() == "/model":
if memgpt_agent.model == 'gpt-4':
memgpt_agent.model = 'gpt-3.5-turbo'
elif memgpt_agent.model == 'gpt-3.5-turbo':
memgpt_agent.model = 'gpt-4'
print(f"Updated model to:\n{str(memgpt_agent.model)}")
continue
elif user_input.lower() == "/pop" or user_input.lower().startswith("/pop "):
# Check if there's an additional argument that's an integer
command = user_input.strip().split()
amount = int(command[1]) if len(command) > 1 and command[1].isdigit() else 2
print(f"Popping last {amount} messages from stack")
memgpt_agent.messages = memgpt_agent.messages[:-amount]
continue
# No skip options
elif user_input.lower() == "/wipe":
memgpt_agent = agent.AgentAsync(interface)
user_message = None
elif user_input.lower() == "/heartbeat":
user_message = system.get_heartbeat()
elif user_input.lower() == "/memorywarning":
user_message = system.get_token_limit_warning()
else:
print(f"Unrecognized command: {user_input}")
continue
else:
# If message did not begin with command prefix, pass inputs to MemGPT
# Handle user message and append to messages
user_message = system.package_user_message(user_input)
skip_next_user_input = False
with console.status("[bold cyan]Thinking...") as status:
new_messages, heartbeat_request, function_failed, token_warning = await memgpt_agent.step(user_message, first_message=False)
# Skip user inputs if there's a memory warning, function execution failed, or the agent asked for control
if token_warning:
user_message = system.get_token_limit_warning()
skip_next_user_input = True
elif function_failed:
user_message = system.get_heartbeat(constants.FUNC_FAILED_HEARTBEAT_MESSAGE)
skip_next_user_input = True
elif heartbeat_request:
user_message = system.get_heartbeat(constants.REQ_HEARTBEAT_MESSAGE)
skip_next_user_input = True
counter += 1
print("Finished.")
if __name__ == '__main__':
def run(argv):
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
app.run(run)