Source code for langchain_experimental.autonomous_agents.autogpt.prompt_generator

import json
from typing import List

from langchain.tools.base import BaseTool

FINISH_NAME = "finish"


class PromptGenerator:
    """自定义提示字符串的生成器。

    根据约束、命令、资源和性能评估来实现这一功能。"""

    def __init__(self) -> None:
        """初始化PromptGenerator对象。

        从空的约束、命令、资源和性能评估列表开始。
        """
        self.constraints: List[str] = []
        self.commands: List[BaseTool] = []
        self.resources: List[str] = []
        self.performance_evaluation: List[str] = []
        self.response_format = {
            "thoughts": {
                "text": "thought",
                "reasoning": "reasoning",
                "plan": "- short bulleted\n- list that conveys\n- long-term plan",
                "criticism": "constructive self-criticism",
                "speak": "thoughts summary to say to user",
            },
            "command": {"name": "command name", "args": {"arg name": "value"}},
        }

    def add_constraint(self, constraint: str) -> None:
        """
        将约束添加到约束列表中。

        参数:
            constraint (str): 要添加的约束。
        """
        self.constraints.append(constraint)

    def add_tool(self, tool: BaseTool) -> None:
        self.commands.append(tool)

    def _generate_command_string(self, tool: BaseTool) -> str:
        output = f"{tool.name}: {tool.description}"
        output += f", args json schema: {json.dumps(tool.args)}"
        return output

    def add_resource(self, resource: str) -> None:
        """
        将资源添加到资源列表中。

        参数:
            resource (str): 要添加的资源。
        """

        self.resources.append(resource)

    def add_performance_evaluation(self, evaluation: str) -> None:
        """
        将性能评估项目添加到性能评估列表中。

        参数:
            evaluation (str): 要添加的评估项目。
        """
        self.performance_evaluation.append(evaluation)

    def _generate_numbered_list(self, items: list, item_type: str = "list") -> str:
        """
        根据给定的项目类型，从给定项目生成一个编号列表。

        参数:
            items (list): 要编号的项目列表。
            item_type (str, optional): 列表中项目的类型。
                默认为'list'。

        返回:
            str: 格式化后的编号列表。
        """
        if item_type == "command":
            command_strings = [
                f"{i + 1}. {self._generate_command_string(item)}"
                for i, item in enumerate(items)
            ]
            finish_description = (
                "use this to signal that you have finished all your objectives"
            )
            finish_args = (
                '"response": "final response to let '
                'people know you have finished your objectives"'
            )
            finish_string = (
                f"{len(items) + 1}. {FINISH_NAME}: "
                f"{finish_description}, args: {finish_args}"
            )
            return "\n".join(command_strings + [finish_string])
        else:
            return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items))

    def generate_prompt_string(self) -> str:
        """生成一个提示字符串。

        返回:
            str: 生成的提示字符串。
        """
        formatted_response_format = json.dumps(self.response_format, indent=4)
        prompt_string = (
            f"Constraints:\n{self._generate_numbered_list(self.constraints)}\n\n"
            f"Commands:\n"
            f"{self._generate_numbered_list(self.commands, item_type='command')}\n\n"
            f"Resources:\n{self._generate_numbered_list(self.resources)}\n\n"
            f"Performance Evaluation:\n"
            f"{self._generate_numbered_list(self.performance_evaluation)}\n\n"
            f"You should only respond in JSON format as described below "
            f"\nResponse Format: \n{formatted_response_format} "
            f"\nEnsure the response can be parsed by Python json.loads"
        )

        return prompt_string


def get_prompt(tools: List[BaseTool]) -> str:
    """生成一个提示字符串。

    它包括各种约束、命令、资源和性能评估。

    返回:
        str: 生成的提示字符串。
    """

    # Initialize the PromptGenerator object
    prompt_generator = PromptGenerator()

    # Add constraints to the PromptGenerator object
    prompt_generator.add_constraint(
        "~4000 word limit for short term memory. "
        "Your short term memory is short, "
        "so immediately save important information to files."
    )
    prompt_generator.add_constraint(
        "If you are unsure how you previously did something "
        "or want to recall past events, "
        "thinking about similar events will help you remember."
    )
    prompt_generator.add_constraint("No user assistance")
    prompt_generator.add_constraint(
        'Exclusively use the commands listed in double quotes e.g. "command name"'
    )

    # Add commands to the PromptGenerator object
    for tool in tools:
        prompt_generator.add_tool(tool)

    # Add resources to the PromptGenerator object
    prompt_generator.add_resource(
        "Internet access for searches and information gathering."
    )
    prompt_generator.add_resource("Long Term memory management.")
    prompt_generator.add_resource(
        "GPT-3.5 powered Agents for delegation of simple tasks."
    )
    prompt_generator.add_resource("File output.")

    # Add performance evaluations to the PromptGenerator object
    prompt_generator.add_performance_evaluation(
        "Continuously review and analyze your actions "
        "to ensure you are performing to the best of your abilities."
    )
    prompt_generator.add_performance_evaluation(
        "Constructively self-criticize your big-picture behavior constantly."
    )
    prompt_generator.add_performance_evaluation(
        "Reflect on past decisions and strategies to refine your approach."
    )
    prompt_generator.add_performance_evaluation(
        "Every command has a cost, so be smart and efficient. "
        "Aim to complete tasks in the least number of steps."
    )

    # Generate the prompt string
    prompt_string = prompt_generator.generate_prompt_string()

    return prompt_string