ignore third party
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binary-husky
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autogpt/chat.py
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autogpt/chat.py
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import time
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from openai.error import RateLimitError
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from autogpt import token_counter
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from autogpt.api_manager import api_manager
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from autogpt.config import Config
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from autogpt.llm_utils import create_chat_completion
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from autogpt.logs import logger
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from autogpt.types.openai import Message
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cfg = Config()
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def create_chat_message(role, content) -> Message:
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"""
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Create a chat message with the given role and content.
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Args:
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role (str): The role of the message sender, e.g., "system", "user", or "assistant".
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content (str): The content of the message.
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Returns:
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dict: A dictionary containing the role and content of the message.
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"""
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return {"role": role, "content": content}
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def generate_context(prompt, relevant_memory, full_message_history, model):
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current_context = [
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create_chat_message("system", prompt),
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create_chat_message(
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"system", f"The current time and date is {time.strftime('%c')}"
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),
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create_chat_message(
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"system",
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f"This reminds you of these events from your past:\n{relevant_memory}\n\n",
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),
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]
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# Add messages from the full message history until we reach the token limit
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next_message_to_add_index = len(full_message_history) - 1
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insertion_index = len(current_context)
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# Count the currently used tokens
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current_tokens_used = token_counter.count_message_tokens(current_context, model)
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return (
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next_message_to_add_index,
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current_tokens_used,
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insertion_index,
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current_context,
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)
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# TODO: Change debug from hardcode to argument
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def chat_with_ai(
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agent, prompt, user_input, full_message_history, permanent_memory, token_limit
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):
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"""Interact with the OpenAI API, sending the prompt, user input, message history,
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and permanent memory."""
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while True:
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try:
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"""
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Interact with the OpenAI API, sending the prompt, user input,
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message history, and permanent memory.
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Args:
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prompt (str): The prompt explaining the rules to the AI.
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user_input (str): The input from the user.
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full_message_history (list): The list of all messages sent between the
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user and the AI.
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permanent_memory (Obj): The memory object containing the permanent
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memory.
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token_limit (int): The maximum number of tokens allowed in the API call.
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Returns:
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str: The AI's response.
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"""
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model = cfg.fast_llm_model # TODO: Change model from hardcode to argument
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# Reserve 1000 tokens for the response
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logger.debug(f"Token limit: {token_limit}")
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send_token_limit = token_limit - 1000
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relevant_memory = (
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""
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if len(full_message_history) == 0
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else permanent_memory.get_relevant(str(full_message_history[-9:]), 10)
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)
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logger.debug(f"Memory Stats: {permanent_memory.get_stats()}")
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(
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next_message_to_add_index,
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current_tokens_used,
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insertion_index,
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current_context,
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) = generate_context(prompt, relevant_memory, full_message_history, model)
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while current_tokens_used > 2500:
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# remove memories until we are under 2500 tokens
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relevant_memory = relevant_memory[:-1]
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(
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next_message_to_add_index,
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current_tokens_used,
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insertion_index,
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current_context,
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) = generate_context(
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prompt, relevant_memory, full_message_history, model
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)
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current_tokens_used += token_counter.count_message_tokens(
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[create_chat_message("user", user_input)], model
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) # Account for user input (appended later)
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while next_message_to_add_index >= 0:
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# print (f"CURRENT TOKENS USED: {current_tokens_used}")
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message_to_add = full_message_history[next_message_to_add_index]
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tokens_to_add = token_counter.count_message_tokens(
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[message_to_add], model
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)
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if current_tokens_used + tokens_to_add > send_token_limit:
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break
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# Add the most recent message to the start of the current context,
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# after the two system prompts.
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current_context.insert(
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insertion_index, full_message_history[next_message_to_add_index]
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)
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# Count the currently used tokens
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current_tokens_used += tokens_to_add
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# Move to the next most recent message in the full message history
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next_message_to_add_index -= 1
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# inform the AI about its remaining budget (if it has one)
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if api_manager.get_total_budget() > 0.0:
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remaining_budget = (
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api_manager.get_total_budget() - api_manager.get_total_cost()
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)
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if remaining_budget < 0:
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remaining_budget = 0
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system_message = (
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f"Your remaining API budget is ${remaining_budget:.3f}"
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+ (
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" BUDGET EXCEEDED! SHUT DOWN!\n\n"
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if remaining_budget == 0
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else " Budget very nearly exceeded! Shut down gracefully!\n\n"
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if remaining_budget < 0.005
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else " Budget nearly exceeded. Finish up.\n\n"
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if remaining_budget < 0.01
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else "\n\n"
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)
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)
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logger.debug(system_message)
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current_context.append(create_chat_message("system", system_message))
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# Append user input, the length of this is accounted for above
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current_context.extend([create_chat_message("user", user_input)])
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plugin_count = len(cfg.plugins)
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for i, plugin in enumerate(cfg.plugins):
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if not plugin.can_handle_on_planning():
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continue
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plugin_response = plugin.on_planning(
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agent.prompt_generator, current_context
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)
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if not plugin_response or plugin_response == "":
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continue
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tokens_to_add = token_counter.count_message_tokens(
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[create_chat_message("system", plugin_response)], model
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)
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if current_tokens_used + tokens_to_add > send_token_limit:
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if cfg.debug_mode:
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print("Plugin response too long, skipping:", plugin_response)
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print("Plugins remaining at stop:", plugin_count - i)
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break
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current_context.append(create_chat_message("system", plugin_response))
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# Calculate remaining tokens
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tokens_remaining = token_limit - current_tokens_used
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# assert tokens_remaining >= 0, "Tokens remaining is negative.
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# This should never happen, please submit a bug report at
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# https://www.github.com/Torantulino/Auto-GPT"
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# Debug print the current context
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logger.debug(f"Token limit: {token_limit}")
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logger.debug(f"Send Token Count: {current_tokens_used}")
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logger.debug(f"Tokens remaining for response: {tokens_remaining}")
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logger.debug("------------ CONTEXT SENT TO AI ---------------")
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for message in current_context:
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# Skip printing the prompt
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if message["role"] == "system" and message["content"] == prompt:
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continue
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logger.debug(f"{message['role'].capitalize()}: {message['content']}")
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logger.debug("")
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logger.debug("----------- END OF CONTEXT ----------------")
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# TODO: use a model defined elsewhere, so that model can contain
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# temperature and other settings we care about
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assistant_reply = create_chat_completion(
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model=model,
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messages=current_context,
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max_tokens=tokens_remaining,
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)
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# Update full message history
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full_message_history.append(create_chat_message("user", user_input))
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full_message_history.append(
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create_chat_message("assistant", assistant_reply)
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)
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return assistant_reply
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except RateLimitError:
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# TODO: When we switch to langchain, this is built in
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print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
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time.sleep(10)
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