from __future__ import annotations import copy import json from dataclasses import dataclass, field from typing import TYPE_CHECKING if TYPE_CHECKING: from autogpt.agent import Agent from autogpt.config import Config from autogpt.json_utils.utilities import extract_json_from_response from autogpt.llm.base import ChatSequence, Message, MessageRole, MessageType from autogpt.llm.providers.openai import OPEN_AI_CHAT_MODELS from autogpt.llm.utils import count_string_tokens, create_chat_completion from autogpt.log_cycle.log_cycle import PROMPT_SUMMARY_FILE_NAME, SUMMARY_FILE_NAME from autogpt.logs import logger @dataclass class MessageHistory: agent: Agent messages: list[Message] = field(default_factory=list) summary: str = "I was created" last_trimmed_index: int = 0 def __getitem__(self, i: int): return self.messages[i] def __iter__(self): return iter(self.messages) def __len__(self): return len(self.messages) def add( self, role: MessageRole, content: str, type: MessageType | None = None, ): return self.append(Message(role, content, type)) def append(self, message: Message): return self.messages.append(message) def trim_messages( self, current_message_chain: list[Message], config: Config ) -> tuple[Message, list[Message]]: """ Returns a list of trimmed messages: messages which are in the message history but not in current_message_chain. Args: current_message_chain (list[Message]): The messages currently in the context. config (Config): The config to use. Returns: Message: A message with the new running summary after adding the trimmed messages. list[Message]: A list of messages that are in full_message_history with an index higher than last_trimmed_index and absent from current_message_chain. """ # Select messages in full_message_history with an index higher than last_trimmed_index new_messages = [ msg for i, msg in enumerate(self) if i > self.last_trimmed_index ] # Remove messages that are already present in current_message_chain new_messages_not_in_chain = [ msg for msg in new_messages if msg not in current_message_chain ] if not new_messages_not_in_chain: return self.summary_message(), [] new_summary_message = self.update_running_summary( new_events=new_messages_not_in_chain, config=config ) # Find the index of the last message processed last_message = new_messages_not_in_chain[-1] self.last_trimmed_index = self.messages.index(last_message) return new_summary_message, new_messages_not_in_chain def per_cycle(self, config: Config, messages: list[Message] | None = None): """ Yields: Message: a message containing user input Message: a message from the AI containing a proposed action Message: the message containing the result of the AI's proposed action """ messages = messages or self.messages for i in range(0, len(messages) - 1): ai_message = messages[i] if ai_message.type != "ai_response": continue user_message = ( messages[i - 1] if i > 0 and messages[i - 1].role == "user" else None ) result_message = messages[i + 1] try: assert ( extract_json_from_response(ai_message.content) != {} ), "AI response is not a valid JSON object" assert result_message.type == "action_result" yield user_message, ai_message, result_message except AssertionError as err: logger.debug( f"Invalid item in message history: {err}; Messages: {messages[i-1:i+2]}" ) def summary_message(self) -> Message: return Message( "system", f"This reminds you of these events from your past: \n{self.summary}", ) def update_running_summary( self, new_events: list[Message], config: Config ) -> Message: """ This function takes a list of dictionaries representing new events and combines them with the current summary, focusing on key and potentially important information to remember. The updated summary is returned in a message formatted in the 1st person past tense. Args: new_events (List[Dict]): A list of dictionaries containing the latest events to be added to the summary. Returns: str: A message containing the updated summary of actions, formatted in the 1st person past tense. Example: new_events = [{"event": "entered the kitchen."}, {"event": "found a scrawled note with the number 7"}] update_running_summary(new_events) # Returns: "This reminds you of these events from your past: \nI entered the kitchen and found a scrawled note saying 7." """ if not new_events: return self.summary_message() # Create a copy of the new_events list to prevent modifying the original list new_events = copy.deepcopy(new_events) # Replace "assistant" with "you". This produces much better first person past tense results. for event in new_events: if event.role.lower() == "assistant": event.role = "you" # Remove "thoughts" dictionary from "content" try: content_dict = extract_json_from_response(event.content) if "thoughts" in content_dict: del content_dict["thoughts"] event.content = json.dumps(content_dict) except json.JSONDecodeError as e: logger.error(f"Error: Invalid JSON: {e}") if config.debug_mode: logger.error(f"{event.content}") elif event.role.lower() == "system": event.role = "your computer" # Delete all user messages elif event.role == "user": new_events.remove(event) # Summarize events and current summary in batch to a new running summary # Assume an upper bound length for the summary prompt template, i.e. Your task is to create a concise running summary...., in summarize_batch func # TODO make this default dynamic prompt_template_length = 100 max_tokens = OPEN_AI_CHAT_MODELS.get(config.fast_llm_model).max_tokens summary_tlength = count_string_tokens(str(self.summary), config.fast_llm_model) batch = [] batch_tlength = 0 # TODO Can put a cap on length of total new events and drop some previous events to save API cost, but need to think thru more how to do it without losing the context for event in new_events: event_tlength = count_string_tokens(str(event), config.fast_llm_model) if ( batch_tlength + event_tlength > max_tokens - prompt_template_length - summary_tlength ): # The batch is full. Summarize it and start a new one. self.summarize_batch(batch, config) summary_tlength = count_string_tokens( str(self.summary), config.fast_llm_model ) batch = [event] batch_tlength = event_tlength else: batch.append(event) batch_tlength += event_tlength if batch: # There's an unprocessed batch. Summarize it. self.summarize_batch(batch, config) return self.summary_message() def summarize_batch(self, new_events_batch, config): prompt = f'''Your task is to create a concise running summary of actions and information results in the provided text, focusing on key and potentially important information to remember. You will receive the current summary and your latest actions. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise. Summary So Far: """ {self.summary} """ Latest Development: """ {new_events_batch or "Nothing new happened."} """ ''' prompt = ChatSequence.for_model( config.fast_llm_model, [Message("user", prompt)] ) self.agent.log_cycle_handler.log_cycle( self.agent.ai_name, self.agent.created_at, self.agent.cycle_count, prompt.raw(), PROMPT_SUMMARY_FILE_NAME, ) self.summary = create_chat_completion(prompt, config).content self.agent.log_cycle_handler.log_cycle( self.agent.ai_name, self.agent.created_at, self.agent.cycle_count, self.summary, SUMMARY_FILE_NAME, )