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Diffstat (limited to 'autogpt/llm/chat.py')
| -rw-r--r-- | autogpt/llm/chat.py | 203 |
1 files changed, 0 insertions, 203 deletions
diff --git a/autogpt/llm/chat.py b/autogpt/llm/chat.py deleted file mode 100644 index 4364cb1d8..000000000 --- a/autogpt/llm/chat.py +++ /dev/null @@ -1,203 +0,0 @@ -from __future__ import annotations - -import time -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from autogpt.agent.agent import Agent - -from autogpt.config import Config -from autogpt.llm.api_manager import ApiManager -from autogpt.llm.base import ChatSequence, Message -from autogpt.llm.providers.openai import ( - count_openai_functions_tokens, - get_openai_command_specs, -) -from autogpt.llm.utils import count_message_tokens, create_chat_completion -from autogpt.logs import CURRENT_CONTEXT_FILE_NAME, logger - - -# TODO: Change debug from hardcode to argument -def chat_with_ai( - config: Config, - agent: Agent, - system_prompt: str, - triggering_prompt: str, - token_limit: int, - model: str | None = None, -): - """ - Interact with the OpenAI API, sending the prompt, user input, - message history, and permanent memory. - - Args: - config (Config): The config to use. - agent (Agent): The agent to use. - system_prompt (str): The prompt explaining the rules to the AI. - triggering_prompt (str): The input from the user. - token_limit (int): The maximum number of tokens allowed in the API call. - model (str, optional): The model to use. By default, the config.smart_llm will be used. - - Returns: - str: The AI's response. - """ - if model is None: - model = config.smart_llm - - # Reserve 1000 tokens for the response - logger.debug(f"Token limit: {token_limit}") - send_token_limit = token_limit - 1000 - - # if len(agent.history) == 0: - # relevant_memory = "" - # else: - # recent_history = agent.history[-5:] - # shuffle(recent_history) - # relevant_memories = agent.memory.get_relevant( - # str(recent_history), 5 - # ) - # if relevant_memories: - # shuffle(relevant_memories) - # relevant_memory = str(relevant_memories) - # logger.debug(f"Memory Stats: {agent.memory.get_stats()}") - relevant_memory = [] - - message_sequence = ChatSequence.for_model( - model, - [ - Message("system", system_prompt), - Message("system", f"The current time and date is {time.strftime('%c')}"), - # Message( - # "system", - # f"This reminds you of these events from your past:\n{relevant_memory}\n\n", - # ), - ], - ) - - # Count the currently used tokens - current_tokens_used = message_sequence.token_length - insertion_index = len(message_sequence) - - # Account for tokens used by OpenAI functions - openai_functions = None - if agent.config.openai_functions: - openai_functions = get_openai_command_specs(agent.command_registry) - functions_tlength = count_openai_functions_tokens(openai_functions, model) - current_tokens_used += functions_tlength - logger.debug(f"OpenAI Functions take up {functions_tlength} tokens in API call") - - # Account for user input (appended later) - user_input_msg = Message("user", triggering_prompt) - current_tokens_used += count_message_tokens(user_input_msg, model) - - current_tokens_used += agent.history.max_summary_tlength # Reserve space - current_tokens_used += 500 # Reserve space for the openai functions TODO improve - - # Add historical Messages until the token limit is reached - # or there are no more messages to add. - for cycle in reversed(list(agent.history.per_cycle())): - messages_to_add = [msg for msg in cycle if msg is not None] - tokens_to_add = count_message_tokens(messages_to_add, model) - if current_tokens_used + tokens_to_add > send_token_limit: - break - - # Add the most recent message to the start of the chain, - # after the system prompts. - message_sequence.insert(insertion_index, *messages_to_add) - current_tokens_used += tokens_to_add - - # Update & add summary of trimmed messages - if len(agent.history) > 0: - new_summary_message, trimmed_messages = agent.history.trim_messages( - current_message_chain=list(message_sequence), config=agent.config - ) - tokens_to_add = count_message_tokens(new_summary_message, model) - message_sequence.insert(insertion_index, new_summary_message) - current_tokens_used += tokens_to_add - agent.history.max_summary_tlength - - # FIXME: uncomment when memory is back in use - # memory_store = get_memory(config) - # for _, ai_msg, result_msg in agent.history.per_cycle(trimmed_messages): - # memory_to_add = MemoryItem.from_ai_action(ai_msg, result_msg) - # logger.debug(f"Storing the following memory:\n{memory_to_add.dump()}") - # memory_store.add(memory_to_add) - - api_manager = ApiManager() - # inform the AI about its remaining budget (if it has one) - if api_manager.get_total_budget() > 0.0: - remaining_budget = api_manager.get_total_budget() - api_manager.get_total_cost() - if remaining_budget < 0: - remaining_budget = 0 - budget_message = f"Your remaining API budget is ${remaining_budget:.3f}" + ( - " BUDGET EXCEEDED! SHUT DOWN!\n\n" - if remaining_budget == 0 - else " Budget very nearly exceeded! Shut down gracefully!\n\n" - if remaining_budget < 0.005 - else " Budget nearly exceeded. Finish up.\n\n" - if remaining_budget < 0.01 - else "\n\n" - ) - logger.debug(budget_message) - message_sequence.add("system", budget_message) - current_tokens_used += count_message_tokens(message_sequence[-1], model) - - # Append user input, the length of this is accounted for above - message_sequence.append(user_input_msg) - - plugin_count = len(config.plugins) - for i, plugin in enumerate(config.plugins): - if not plugin.can_handle_on_planning(): - continue - plugin_response = plugin.on_planning( - agent.ai_config.prompt_generator, message_sequence.raw() - ) - if not plugin_response or plugin_response == "": - continue - tokens_to_add = count_message_tokens(Message("system", plugin_response), model) - if current_tokens_used + tokens_to_add > send_token_limit: - logger.debug(f"Plugin response too long, skipping: {plugin_response}") - logger.debug(f"Plugins remaining at stop: {plugin_count - i}") - break - message_sequence.add("system", plugin_response) - current_tokens_used += tokens_to_add - - # Calculate remaining tokens - tokens_remaining = token_limit - current_tokens_used - # assert tokens_remaining >= 0, "Tokens remaining is negative. - # This should never happen, please submit a bug report at - # https://www.github.com/Torantulino/Auto-GPT" - - # Debug print the current context - logger.debug(f"Token limit: {token_limit}") - logger.debug(f"Send Token Count: {current_tokens_used}") - logger.debug(f"Tokens remaining for response: {tokens_remaining}") - logger.debug("------------ CONTEXT SENT TO AI ---------------") - for message in message_sequence: - # Skip printing the prompt - if message.role == "system" and message.content == system_prompt: - continue - logger.debug(f"{message.role.capitalize()}: {message.content}") - logger.debug("") - logger.debug("----------- END OF CONTEXT ----------------") - agent.log_cycle_handler.log_cycle( - agent.ai_name, - agent.created_at, - agent.cycle_count, - message_sequence.raw(), - CURRENT_CONTEXT_FILE_NAME, - ) - - # TODO: use a model defined elsewhere, so that model can contain - # temperature and other settings we care about - assistant_reply = create_chat_completion( - prompt=message_sequence, - config=agent.config, - functions=openai_functions, - max_tokens=tokens_remaining, - ) - - # Update full message history - agent.history.append(user_input_msg) - agent.history.add("assistant", assistant_reply.content, "ai_response") - - return assistant_reply |