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import time
from typing import Any, Callable, List
from pydantic import BaseModel
from .prompt_generator import get_prompt
from langchain.prompts.chat import (
BaseChatPromptTemplate,
)
from langchain.schema import BaseMessage, HumanMessage, SystemMessage
from langchain.tools.base import BaseTool
from langchain.vectorstores.base import VectorStoreRetriever
class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel):
ai_name: str
ai_role: str
tools: List[BaseTool]
token_counter: Callable[[str], int]
send_token_limit: int = 4196
def construct_full_prompt(self, goals: List[str]) -> str:
prompt_start = """Your decisions must always be made independently
without seeking user assistance. Play to your strengths
as an LLM and pursue simple strategies with no legal complications.
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
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(base_prompt.content) + self.token_counter(
time_prompt.content
)
memory: VectorStoreRetriever = kwargs["memory"]
previous_messages = kwargs["messages"]
relevant_docs = memory.get_relevant_documents(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 += len(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
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
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