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