mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
81 lines
3.2 KiB
Python
81 lines
3.2 KiB
Python
import time
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from typing import Any, Callable, List
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from langchain.prompts.chat import (
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BaseChatPromptTemplate,
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)
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from langchain.schema.messages import BaseMessage, HumanMessage, SystemMessage
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from langchain.tools.base import BaseTool
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from langchain.vectorstores.base import VectorStoreRetriever
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from pydantic_v1 import BaseModel
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from langchain_experimental.autonomous_agents.autogpt.prompt_generator import get_prompt
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class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel):
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"""Prompt for AutoGPT."""
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ai_name: str
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ai_role: str
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tools: List[BaseTool]
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token_counter: Callable[[str], int]
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send_token_limit: int = 4196
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def construct_full_prompt(self, goals: List[str]) -> str:
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prompt_start = (
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"Your decisions must always be made independently "
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"without seeking user assistance.\n"
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"Play to your strengths as an LLM and pursue simple "
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"strategies with no legal complications.\n"
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"If you have completed all your tasks, make sure to "
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'use the "finish" command.'
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)
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# Construct full prompt
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full_prompt = (
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f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n"
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)
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for i, goal in enumerate(goals):
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full_prompt += f"{i+1}. {goal}\n"
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full_prompt += f"\n\n{get_prompt(self.tools)}"
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return full_prompt
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def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
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base_prompt = SystemMessage(content=self.construct_full_prompt(kwargs["goals"]))
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time_prompt = SystemMessage(
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content=f"The current time and date is {time.strftime('%c')}"
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)
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used_tokens = self.token_counter(base_prompt.content) + self.token_counter(
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time_prompt.content
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)
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memory: VectorStoreRetriever = kwargs["memory"]
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previous_messages = kwargs["messages"]
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relevant_docs = memory.get_relevant_documents(str(previous_messages[-10:]))
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relevant_memory = [d.page_content for d in relevant_docs]
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relevant_memory_tokens = sum(
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[self.token_counter(doc) for doc in relevant_memory]
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)
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while used_tokens + relevant_memory_tokens > 2500:
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relevant_memory = relevant_memory[:-1]
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relevant_memory_tokens = sum(
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[self.token_counter(doc) for doc in relevant_memory]
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)
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content_format = (
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f"This reminds you of these events "
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f"from your past:\n{relevant_memory}\n\n"
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)
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memory_message = SystemMessage(content=content_format)
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used_tokens += self.token_counter(memory_message.content)
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historical_messages: List[BaseMessage] = []
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for message in previous_messages[-10:][::-1]:
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message_tokens = self.token_counter(message.content)
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if used_tokens + message_tokens > self.send_token_limit - 1000:
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break
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historical_messages = [message] + historical_messages
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used_tokens += message_tokens
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input_message = HumanMessage(content=kwargs["user_input"])
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messages: List[BaseMessage] = [base_prompt, time_prompt, memory_message]
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messages += historical_messages
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messages.append(input_message)
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return messages
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