Files
letta-server/memgpt/persistence_manager.py

169 lines
6.7 KiB
Python

from abc import ABC, abstractmethod
import pickle
from memgpt.config import AgentConfig
from memgpt.memory import (
DummyRecallMemory,
BaseRecallMemory,
EmbeddingArchivalMemory,
)
from memgpt.utils import get_local_time, printd
from memgpt.data_types import Message
from memgpt.config import MemGPTConfig
from datetime import datetime
def parse_formatted_time(formatted_time):
# parse times returned by memgpt.utils.get_formatted_time()
return datetime.strptime(formatted_time, "%Y-%m-%d %I:%M:%S %p %Z%z")
class PersistenceManager(ABC):
@abstractmethod
def trim_messages(self, num):
pass
@abstractmethod
def prepend_to_messages(self, added_messages):
pass
@abstractmethod
def append_to_messages(self, added_messages):
pass
@abstractmethod
def swap_system_message(self, new_system_message):
pass
@abstractmethod
def update_memory(self, new_memory):
pass
class LocalStateManager(PersistenceManager):
"""In-memory state manager has nothing to manage, all agents are held in-memory"""
recall_memory_cls = BaseRecallMemory
archival_memory_cls = EmbeddingArchivalMemory
def __init__(self, agent_config: AgentConfig):
# Memory held in-state useful for debugging stateful versions
self.memory = None
self.messages = [] # current in-context messages
# self.all_messages = [] # all messages seen in current session (needed if lazily synchronizing state with DB)
self.archival_memory = EmbeddingArchivalMemory(agent_config)
self.recall_memory = BaseRecallMemory(agent_config)
self.agent_config = agent_config
self.config = MemGPTConfig.load()
@classmethod
def load(cls, agent_config: AgentConfig):
""" Load a LocalStateManager from a file. """ ""
# TODO: remove this function
return cls(agent_config)
# try:
# with open(filename, "rb") as f:
# data = pickle.load(f)
# except ModuleNotFoundError as e:
# # Patch for stripped openai package
# # ModuleNotFoundError: No module named 'openai.openai_object'
# with open(filename, "rb") as f:
# unpickler = OpenAIBackcompatUnpickler(f)
# data = unpickler.load()
# # print(f"Unpickled data:\n{data.keys()}")
# from memgpt.openai_backcompat.openai_object import OpenAIObject
# def convert_openai_objects_to_dict(obj):
# if isinstance(obj, OpenAIObject):
# # Convert to dict or handle as needed
# # print(f"detected OpenAIObject on {obj}")
# return obj.to_dict_recursive()
# elif isinstance(obj, dict):
# return {k: convert_openai_objects_to_dict(v) for k, v in obj.items()}
# elif isinstance(obj, list):
# return [convert_openai_objects_to_dict(v) for v in obj]
# else:
# return obj
# data = convert_openai_objects_to_dict(data)
# # print(f"Converted data:\n{data.keys()}")
# manager = cls(agent_config)
# manager.archival_memory = EmbeddingArchivalMemory(agent_config)
# manager.recall_memory = BaseRecallMemory(agent_config)
# return manager
def save(self):
"""Ensure storage connectors save data"""
self.archival_memory.save()
self.recall_memory.save()
def init(self, agent):
"""Connect persistent state manager to agent"""
printd(f"Initializing {self.__class__.__name__} with agent object")
# self.all_messages = [{"timestamp": get_local_time(), "message": msg} for msg in agent.messages.copy()]
self.messages = [{"timestamp": get_local_time(), "message": msg} for msg in agent.messages.copy()]
self.memory = agent.memory
# printd(f"{self.__class__.__name__}.all_messages.len = {len(self.all_messages)}")
printd(f"{self.__class__.__name__}.messages.len = {len(self.messages)}")
# Persistence manager also handles DB-related state
# self.recall_memory = self.recall_memory_cls(message_database=self.all_messages)
def json_to_message(self, message_json) -> Message:
"""Convert agent message JSON into Message object"""
timestamp = message_json["timestamp"]
message = message_json["message"]
return Message(
user_id=self.config.anon_clientid,
agent_id=self.agent_config.name,
role=message["role"],
text=message["content"],
model=self.agent_config.model,
created_at=parse_formatted_time(timestamp),
function_name=message["function_name"] if "function_name" in message else None,
function_args=message["function_args"] if "function_args" in message else None,
function_response=message["function_response"] if "function_response" in message else None,
id=message["id"] if "id" in message else None,
)
def trim_messages(self, num):
# printd(f"InMemoryStateManager.trim_messages")
self.messages = [self.messages[0]] + self.messages[num:]
def prepend_to_messages(self, added_messages):
# first tag with timestamps
added_messages = [{"timestamp": get_local_time(), "message": msg} for msg in added_messages]
printd(f"{self.__class__.__name__}.prepend_to_message")
self.messages = [self.messages[0]] + added_messages + self.messages[1:]
# add to recall memory
self.recall_memory.insert_many([self.json_to_message(m) for m in added_messages])
def append_to_messages(self, added_messages):
# first tag with timestamps
added_messages = [{"timestamp": get_local_time(), "message": msg} for msg in added_messages]
printd(f"{self.__class__.__name__}.append_to_messages")
self.messages = self.messages + added_messages
# add to recall memory
self.recall_memory.insert_many([self.json_to_message(m) for m in added_messages])
def swap_system_message(self, new_system_message):
# first tag with timestamps
new_system_message = {"timestamp": get_local_time(), "message": new_system_message}
printd(f"{self.__class__.__name__}.swap_system_message")
self.messages[0] = new_system_message
# add to recall memory
self.recall_memory.insert(self.json_to_message(new_system_message))
def update_memory(self, new_memory):
printd(f"{self.__class__.__name__}.update_memory")
self.memory = new_memory