Source code for langchain_experimental.autonomous_agents.autogpt.prompt

import time
from typing import Any, Callable, List, cast

from langchain.tools.base import BaseTool
from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
from langchain_core.prompts.chat import (
    BaseChatPromptTemplate,
)
from langchain_core.vectorstores import VectorStoreRetriever

from langchain_experimental.autonomous_agents.autogpt.prompt_generator import get_prompt
from langchain_experimental.pydantic_v1 import BaseModel


# This class has a metaclass conflict: both `BaseChatPromptTemplate` and `BaseModel`
# define a metaclass to use, and the two metaclasses attempt to define
# the same functions but in mutually-incompatible ways.
# It isn't clear how to resolve this, and this code predates mypy
# beginning to perform that check.
#
# Mypy errors:
# ```
# Definition of "__private_attributes__" in base class "BaseModel" is
#   incompatible with definition in base class "BaseModel"  [misc]
# Definition of "__repr_name__" in base class "Representation" is
#   incompatible with definition in base class "BaseModel"  [misc]
# Definition of "__pretty__" in base class "Representation" is
#   incompatible with definition in base class "BaseModel"  [misc]
# Definition of "__repr_str__" in base class "Representation" is
#   incompatible with definition in base class "BaseModel"  [misc]
# Definition of "__rich_repr__" in base class "Representation" is
#   incompatible with definition in base class "BaseModel"  [misc]
# Metaclass conflict: the metaclass of a derived class must be
#   a (non-strict) subclass of the metaclasses of all its bases  [misc]
# ```
#
# TODO: look into refactoring this class in a way that avoids the mypy type errors
[docs]class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel): # type: ignore[misc] """Prompt for AutoGPT.""" ai_name: str ai_role: str tools: List[BaseTool] token_counter: Callable[[str], int] send_token_limit: int = 4196
[docs] def construct_full_prompt(self, goals: List[str]) -> str: prompt_start = ( "Your decisions must always be made independently " "without seeking user assistance.\n" "Play to your strengths as an LLM and pursue simple " "strategies with no legal complications.\n" "If you have completed all your tasks, make sure to " 'use the "finish" command.' ) # Construct full prompt full_prompt = ( f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n" ) for i, goal in enumerate(goals): full_prompt += f"{i+1}. {goal}\n" full_prompt += f"\n\n{get_prompt(self.tools)}" return full_prompt
[docs] def format_messages(self, **kwargs: Any) -> List[BaseMessage]: base_prompt = SystemMessage(content=self.construct_full_prompt(kwargs["goals"])) time_prompt = SystemMessage( content=f"The current time and date is {time.strftime('%c')}" ) used_tokens = self.token_counter( cast(str, base_prompt.content) ) + self.token_counter(cast(str, time_prompt.content)) memory: VectorStoreRetriever = kwargs["memory"] previous_messages = kwargs["messages"] relevant_docs = memory.invoke(str(previous_messages[-10:])) relevant_memory = [d.page_content for d in relevant_docs] relevant_memory_tokens = sum( [self.token_counter(doc) for doc in relevant_memory] ) while used_tokens + relevant_memory_tokens > 2500: relevant_memory = relevant_memory[:-1] relevant_memory_tokens = sum( [self.token_counter(doc) for doc in relevant_memory] ) content_format = ( f"This reminds you of these events " f"from your past:\n{relevant_memory}\n\n" ) memory_message = SystemMessage(content=content_format) used_tokens += self.token_counter(cast(str, memory_message.content)) historical_messages: List[BaseMessage] = [] for message in previous_messages[-10:][::-1]: message_tokens = self.token_counter(message.content) if used_tokens + message_tokens > self.send_token_limit - 1000: break historical_messages = [message] + historical_messages used_tokens += message_tokens input_message = HumanMessage(content=kwargs["user_input"]) messages: List[BaseMessage] = [base_prompt, time_prompt, memory_message] messages += historical_messages messages.append(input_message) return messages
[docs] def pretty_repr(self, html: bool = False) -> str: raise NotImplementedError