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Diffstat (limited to 'autogpt/memory/message_history.py')
| -rw-r--r-- | autogpt/memory/message_history.py | 235 |
1 files changed, 0 insertions, 235 deletions
diff --git a/autogpt/memory/message_history.py b/autogpt/memory/message_history.py deleted file mode 100644 index 30dbbb809..000000000 --- a/autogpt/memory/message_history.py +++ /dev/null @@ -1,235 +0,0 @@ -from __future__ import annotations - -import copy -import json -from dataclasses import dataclass -from typing import TYPE_CHECKING, Optional - -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 -from autogpt.llm.providers.openai import OPEN_AI_CHAT_MODELS -from autogpt.llm.utils import ( - count_message_tokens, - count_string_tokens, - create_chat_completion, -) -from autogpt.logs import PROMPT_SUMMARY_FILE_NAME, SUMMARY_FILE_NAME, logger - - -@dataclass -class MessageHistory(ChatSequence): - max_summary_tlength: int = 500 - agent: Optional[Agent] = None - summary: str = "I was created" - last_trimmed_index: int = 0 - - SUMMARIZATION_PROMPT = '''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: -""" -{summary} -""" - -Latest Development: -""" -{new_events} -""" -''' - - 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, 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, - max_summary_length: Optional[int] = None, - ) -> Message: - """ - This function takes a list of Message objects and updates the running summary - to include the events they describe. The updated summary is returned - in a Message formatted in the 1st person past tense. - - Args: - new_events: A list of Messages containing the latest events to be added to the summary. - - Returns: - Message: a Message containing the updated running summary. - - Example: - ```py - 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() - if not max_summary_length: - max_summary_length = self.max_summary_tlength - - # 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) - - summ_model = OPEN_AI_CHAT_MODELS[config.fast_llm] - - # Determine token lengths for use in batching - prompt_template_length = len( - MessageHistory.SUMMARIZATION_PROMPT.format(summary="", new_events="") - ) - max_input_tokens = summ_model.max_tokens - max_summary_length - summary_tlength = count_string_tokens(self.summary, summ_model.name) - batch = [] - batch_tlength = 0 - - # TODO: Put a cap on length of total new events and drop some previous events to - # save API cost. Need to think thru more how to do it without losing the context. - for event in new_events: - event_tlength = count_message_tokens(event, summ_model.name) - - if ( - batch_tlength + event_tlength - > max_input_tokens - prompt_template_length - summary_tlength - ): - # The batch is full. Summarize it and start a new one. - self.summarize_batch(batch, config, max_summary_length) - summary_tlength = count_string_tokens(self.summary, summ_model.name) - 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, max_summary_length) - - return self.summary_message() - - def summarize_batch( - self, new_events_batch: list[Message], config: Config, max_output_length: int - ): - prompt = MessageHistory.SUMMARIZATION_PROMPT.format( - summary=self.summary, new_events=new_events_batch - ) - - prompt = ChatSequence.for_model(config.fast_llm, [Message("user", prompt)]) - if self.agent: - self.agent.log_cycle_handler.log_cycle( - self.agent.ai_config.ai_name, - self.agent.created_at, - self.agent.cycle_count, - prompt.raw(), - PROMPT_SUMMARY_FILE_NAME, - ) - - self.summary = create_chat_completion( - prompt, config, max_tokens=max_output_length - ).content - - if self.agent: - self.agent.log_cycle_handler.log_cycle( - self.agent.ai_config.ai_name, - self.agent.created_at, - self.agent.cycle_count, - self.summary, - SUMMARY_FILE_NAME, - ) |