mirror of https://github.com/Mai-with-u/MaiBot.git
ref:将make_question从Plugin改为回复前工作；为tool添加chat_id字段
SengokuCola
parent
5ee3d7ea43
commit
bd5fc8969b
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@ -22,6 +22,7 @@ from src.express.expression_learner import expression_learner_manager
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from src.chat.frequency_control.frequency_control import frequency_control_manager
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from src.memory_system.question_maker import QuestionMaker
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from src.memory_system.questions import global_conflict_tracker
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from src.memory_system.curious import check_and_make_question
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from src.person_info.person_info import Person
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from src.plugin_system.base.component_types import EventType, ActionInfo
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from src.plugin_system.core import events_manager
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@ -184,12 +185,12 @@ class HeartFChatting:
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)
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question_probability = 0
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if time.time() - self.last_active_time > 3600:
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question_probability = 0.001
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elif time.time() - self.last_active_time > 1200:
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if time.time() - self.last_active_time > 7200:
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question_probability = 0.0003
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else:
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elif time.time() - self.last_active_time > 3600:
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question_probability = 0.0001
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else:
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question_probability = 0.00003
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question_probability = question_probability * global_config.chat.get_auto_chat_value(self.stream_id)
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@ -333,6 +334,9 @@ class HeartFChatting:
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asyncio.create_task(global_memory_chest.build_running_content(chat_id=self.stream_id))
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asyncio.create_task(frequency_control_manager.get_or_create_frequency_control(self.stream_id).trigger_frequency_adjust())
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# 添加curious检测任务 - 检测聊天记录中的矛盾、冲突或需要提问的内容
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asyncio.create_task(check_and_make_question(self.stream_id, recent_messages_list))
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cycle_timers, thinking_id = self.start_cycle()
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logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考")
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@ -0,0 +1,185 @@
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import time
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import asyncio
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from typing import List, Optional, Tuple
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from src.common.logger import get_logger
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from src.chat.utils.chat_message_builder import (
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get_raw_msg_before_timestamp_with_chat,
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build_readable_messages_with_id,
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)
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import model_config, global_config
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from src.memory_system.questions import global_conflict_tracker
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from src.memory_system.memory_utils import parse_md_json
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logger = get_logger("curious")
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class CuriousDetector:
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"""
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好奇心检测器 - 检测聊天记录中的矛盾、冲突或需要提问的内容
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"""
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def __init__(self, chat_id: str):
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self.chat_id = chat_id
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self.llm_request = LLMRequest(
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model_set=model_config.model_task_config.utils,
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request_type="curious_detector",
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)
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async def detect_questions(self, recent_messages: List) -> Optional[str]:
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"""
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检测最近消息中是否有需要提问的内容
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Args:
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recent_messages: 最近的消息列表
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Returns:
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Optional[str]: 如果检测到需要提问的内容，返回问题文本；否则返回None
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"""
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try:
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if not recent_messages or len(recent_messages) < 2:
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return None
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# 构建聊天内容
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chat_content_block, _ = build_readable_messages_with_id(
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messages=recent_messages,
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timestamp_mode="normal_no_YMD",
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read_mark=0.0,
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truncate=True,
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show_actions=True,
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)
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# 检查是否已经有问题在跟踪中
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existing_questions = global_conflict_tracker.get_questions_by_chat_id(self.chat_id)
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if len(existing_questions) > 0:
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logger.debug(f"当前已有{len(existing_questions)}个问题在跟踪中，跳过检测")
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return None
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# 构建检测提示词
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prompt = f"""你是一个严谨的聊天内容分析器。请分析以下聊天记录，检测是否存在需要提问的内容。
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检测条件：
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1. 聊天中存在逻辑矛盾或冲突的信息
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2. 有人反对或否定之前提出的信息
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3. 存在观点不一致的情况
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4. 有模糊不清或需要澄清的概念
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5. 有人提出了质疑或反驳
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**重要限制：**
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- 忽略涉及违法、暴力、色情、政治等敏感话题的内容
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- 不要对敏感话题提问
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- 只有在确实存在矛盾或冲突时才提问
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- 如果聊天内容正常，没有矛盾，请输出：NO
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**聊天记录**
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{chat_content_block}
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请分析上述聊天记录，如果发现需要提问的内容，请用JSON格式输出：
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```json
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{{
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"question": "具体的问题描述，要完整描述涉及的概念和问题",
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"reason": "为什么需要提问这个问题的理由"
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}}
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```
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如果没有需要提问的内容，请只输出：NO"""
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if global_config.debug.show_prompt:
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logger.info(f"好奇心检测提示词: {prompt}")
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else:
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logger.debug("已发送好奇心检测提示词")
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result_text, _ = await self.llm_request.generate_response_async(prompt, temperature=0.3)
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if not result_text:
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return None
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result_text = result_text.strip()
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# 检查是否输出NO
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if result_text.upper() == "NO":
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logger.debug("未检测到需要提问的内容")
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return None
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# 尝试解析JSON
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try:
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questions, reasoning = parse_md_json(result_text)
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if questions and len(questions) > 0:
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question_data = questions[0]
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question = question_data.get("question", "")
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reason = question_data.get("reason", "")
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if question and question.strip():
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logger.info(f"检测到需要提问的内容: {question}")
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logger.info(f"提问理由: {reason}")
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return question
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except Exception as e:
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logger.warning(f"解析问题JSON失败: {e}")
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logger.debug(f"原始响应: {result_text}")
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return None
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except Exception as e:
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logger.error(f"好奇心检测失败: {e}")
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return None
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async def make_question_from_detection(self, question: str, context: str = "") -> bool:
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"""
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将检测到的问题记录到冲突追踪器中
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Args:
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question: 检测到的问题
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context: 问题上下文
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Returns:
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bool: 是否成功记录
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"""
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try:
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if not question or not question.strip():
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return False
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# 记录问题到冲突追踪器，并开始跟踪
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await global_conflict_tracker.track_conflict(
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question=question.strip(),
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context=context,
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start_following=False,
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chat_id=self.chat_id
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)
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logger.info(f"已记录问题到冲突追踪器: {question}")
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return True
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except Exception as e:
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logger.error(f"记录问题失败: {e}")
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return False
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async def check_and_make_question(chat_id: str, recent_messages: List) -> bool:
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"""
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检查聊天记录并生成问题（如果检测到需要提问的内容）
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Args:
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chat_id: 聊天ID
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recent_messages: 最近的消息列表
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Returns:
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bool: 是否检测到并记录了问题
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"""
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try:
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detector = CuriousDetector(chat_id)
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# 检测是否需要提问
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question = await detector.detect_questions(recent_messages)
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if question:
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# 记录问题
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success = await detector.make_question_from_detection(question)
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if success:
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logger.info(f"成功检测并记录问题: {question}")
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return True
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return False
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except Exception as e:
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logger.error(f"检查并生成问题失败: {e}")
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return False
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@ -1,14 +1,25 @@
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from typing import Optional, Type
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from typing import Optional, Type, TYPE_CHECKING
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from src.plugin_system.base.base_tool import BaseTool
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from src.plugin_system.base.component_types import ComponentType
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from src.common.logger import get_logger
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if TYPE_CHECKING:
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from src.chat.message_receive.chat_stream import ChatStream
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logger = get_logger("tool_api")
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def get_tool_instance(tool_name: str) -> Optional[BaseTool]:
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"""获取公开工具实例"""
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def get_tool_instance(tool_name: str, chat_stream: Optional["ChatStream"] = None) -> Optional[BaseTool]:
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"""获取公开工具实例
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Args:
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tool_name: 工具名称
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chat_stream: 聊天流对象，用于传递聊天上下文信息
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Returns:
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Optional[BaseTool]: 工具实例，如果未找到则返回None
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"""
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from src.plugin_system.core import component_registry
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# 获取插件配置
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@ -19,7 +30,7 @@ def get_tool_instance(tool_name: str) -> Optional[BaseTool]:
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plugin_config = None
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tool_class: Type[BaseTool] = component_registry.get_component_class(tool_name, ComponentType.TOOL) # type: ignore
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return tool_class(plugin_config) if tool_class else None
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return tool_class(plugin_config, chat_stream) if tool_class else None
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def get_llm_available_tool_definitions():
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@ -1,10 +1,13 @@
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from abc import ABC, abstractmethod
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from typing import Any, List, Optional, Tuple
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from typing import Any, List, Optional, Tuple, TYPE_CHECKING
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from rich.traceback import install
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from src.common.logger import get_logger
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from src.plugin_system.base.component_types import ComponentType, ToolInfo, ToolParamType
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if TYPE_CHECKING:
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from src.chat.message_receive.chat_stream import ChatStream
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install(extra_lines=3)
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logger = get_logger("base_tool")
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@ -29,8 +32,23 @@ class BaseTool(ABC):
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available_for_llm: bool = False
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"""是否可供LLM使用"""
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def __init__(self, plugin_config: Optional[dict] = None):
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def __init__(self, plugin_config: Optional[dict] = None, chat_stream: Optional["ChatStream"] = None):
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"""初始化工具基类
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Args:
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plugin_config: 插件配置字典
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chat_stream: 聊天流对象，用于获取聊天上下文信息
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"""
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self.plugin_config = plugin_config or {} # 直接存储插件配置字典
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# =============================================================================
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# 便捷属性 - 直接在初始化时获取常用聊天信息（与BaseAction保持一致）
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# =============================================================================
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# 获取聊天流对象
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self.chat_stream = chat_stream
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self.chat_id = self.chat_stream.stream_id if self.chat_stream else None
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self.platform = getattr(self.chat_stream, "platform", None) if self.chat_stream else None
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@classmethod
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def get_tool_definition(cls) -> dict[str, Any]:
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@ -223,7 +223,7 @@ class ToolExecutor:
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function_args["llm_called"] = True # 标记为LLM调用
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# 获取对应工具实例
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tool_instance = tool_instance or get_tool_instance(function_name)
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tool_instance = tool_instance or get_tool_instance(function_name, self.chat_stream)
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if not tool_instance:
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logger.warning(f"未知工具名称: {function_name}")
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return None
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@ -1,34 +0,0 @@
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{
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"manifest_version": 1,
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"name": "MaiCurious插件 (MaiCurious Actions)",
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"version": "1.0.0",
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"description": "可以好奇",
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"author": {
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"name": "SengokuCola",
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"url": "https://github.com/MaiM-with-u"
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},
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"license": "GPL-v3.0-or-later",
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"host_application": {
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"min_version": "0.11.0"
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},
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"homepage_url": "https://github.com/MaiM-with-u/maibot",
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"repository_url": "https://github.com/MaiM-with-u/maibot",
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"keywords": ["curious", "action", "built-in"],
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"categories": ["Deep Think"],
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"default_locale": "zh-CN",
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"locales_path": "_locales",
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"plugin_info": {
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"is_built_in": true,
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"plugin_type": "action_provider",
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"components": [
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{
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"type": "action",
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"name": "maicurious",
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"description": "发送好奇"
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}
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]
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}
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}
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@ -1,118 +0,0 @@
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from typing import List, Tuple, Type, Any
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# 导入新插件系统
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from src.plugin_system import BasePlugin, register_plugin, ComponentInfo
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from src.plugin_system.base.config_types import ConfigField
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from src.person_info.person_info import Person
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from src.plugin_system.base.base_tool import BaseTool, ToolParamType
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# 导入依赖的系统组件
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from src.common.logger import get_logger
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from src.plugins.built_in.relation.relation import BuildRelationAction
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from src.plugin_system.apis import llm_api
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from src.plugin_system.base.base_action import BaseAction
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from src.plugin_system.base.component_types import ActionActivationType
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from src.plugin_system.apis import config_api
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from src.plugin_system.apis import frequency_api
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from src.plugin_system.apis import generator_api
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from src.memory_system.questions import global_conflict_tracker
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logger = get_logger("question_actions")
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class CuriousAction(BaseAction):
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"""频率调节动作 - 调整聊天发言频率"""
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activation_type = ActionActivationType.ALWAYS
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parallel_action = True
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# 动作基本信息
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action_name = "make_question"
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action_description = "提出一个问题，当有人反驳你的观点，或其他人之间有观点冲突时使用"
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# 动作参数定义
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action_parameters = {
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"question": "对存在疑问的信息提出一个问题，描述全面，完整的描述涉及的概念和问题",
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}
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action_require = [
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f"当聊天记录中的概念存在逻辑上的矛盾时使用",
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f"当有人反对或否定你提出的信息时使用",
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f"或当你对现有的概念或事物存在疑问时使用",
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f"有人认为你的观点是错误的，请选择question动作",
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f"有人与你观点不一致，请选择question动作",
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f"请忽略涉及违法，暴力，色情，政治等敏感话题的内容，不要对敏感话题提问",
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]
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# 关联类型
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associated_types = ["text"]
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async def execute(self) -> Tuple[bool, str]:
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"""执行频率调节动作"""
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try:
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if len(global_conflict_tracker.question_tracker_list) > 1:
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return False, "当前已有问题，请先解答完再提问，不要再使用make_question动作"
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question = self.action_data.get("question", "")
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# 存储问题到冲突追踪器
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if question:
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await global_conflict_tracker.record_conflict(conflict_content=question, start_following=True,chat_id=self.chat_id)
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logger.info(f"已存储问题到冲突追踪器: {question}")
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await self.store_action_info(
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action_build_into_prompt=True,
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action_prompt_display=f"你产生了一个问题：{question}",
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action_done=True,
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)
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return True, f"问题{question}已记录，不要重复提问该问题"
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except Exception as e:
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error_msg = f"问题生成失败: {str(e)}"
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logger.error(f"{self.log_prefix} {error_msg}", exc_info=True)
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await self.send_text("问题生成失败")
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return False, error_msg
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||||
@register_plugin
|
||||
class CuriousPlugin(BasePlugin):
|
||||
"""关系动作插件
|
||||
|
||||
系统内置插件，提供基础的聊天交互功能：
|
||||
- Reply: 回复动作
|
||||
- NoReply: 不回复动作
|
||||
- Emoji: 表情动作
|
||||
|
||||
注意：插件基本信息优先从_manifest.json文件中读取
|
||||
"""
|
||||
|
||||
# 插件基本信息
|
||||
plugin_name: str = "maicurious" # 内部标识符
|
||||
enable_plugin: bool = True
|
||||
dependencies: list[str] = [] # 插件依赖列表
|
||||
python_dependencies: list[str] = [] # Python包依赖列表
|
||||
config_file_name: str = "config.toml"
|
||||
|
||||
# 配置节描述
|
||||
config_section_descriptions = {
|
||||
"plugin": "插件启用配置",
|
||||
"components": "核心组件启用配置",
|
||||
}
|
||||
|
||||
# 配置Schema定义
|
||||
config_schema: dict = {
|
||||
"plugin": {
|
||||
"enabled": ConfigField(type=bool, default=True, description="是否启用插件"),
|
||||
"config_version": ConfigField(type=str, default="3.0.0", description="配置文件版本"),
|
||||
}
|
||||
}
|
||||
|
||||
def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]:
|
||||
"""返回插件包含的组件列表"""
|
||||
|
||||
# --- 根据配置注册组件 ---
|
||||
components = []
|
||||
components.append((CuriousAction.get_action_info(), CuriousAction))
|
||||
|
||||
return components
|
||||
|
|
@ -1,4 +1,6 @@
|
|||
from typing import Tuple
|
||||
import asyncio
|
||||
from datetime import datetime
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from src.config.config import global_config
|
||||
|
|
@ -8,22 +10,73 @@ from src.plugin_system import BaseAction, ActionActivationType
|
|||
from src.chat.utils.utils import cut_key_words
|
||||
from src.memory_system.Memory_chest import global_memory_chest
|
||||
from src.plugin_system.base.base_tool import BaseTool
|
||||
from src.plugin_system.apis.message_api import get_messages_by_time_in_chat, build_readable_messages
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import model_config
|
||||
from typing import Any
|
||||
|
||||
logger = get_logger("memory")
|
||||
|
||||
def parse_datetime_to_timestamp(value: str) -> float:
|
||||
"""
|
||||
接受多种常见格式并转换为时间戳（秒）
|
||||
支持示例：
|
||||
- 2025-09-29
|
||||
- 2025-09-29 00:00:00
|
||||
- 2025/09/29 00:00
|
||||
- 2025-09-29T00:00:00
|
||||
"""
|
||||
value = value.strip()
|
||||
fmts = [
|
||||
"%Y-%m-%d %H:%M:%S",
|
||||
"%Y-%m-%d %H:%M",
|
||||
"%Y/%m/%d %H:%M:%S",
|
||||
"%Y/%m/%d %H:%M",
|
||||
"%Y-%m-%d",
|
||||
"%Y/%m/%d",
|
||||
"%Y-%m-%dT%H:%M:%S",
|
||||
"%Y-%m-%dT%H:%M",
|
||||
]
|
||||
last_err = None
|
||||
for fmt in fmts:
|
||||
try:
|
||||
dt = datetime.strptime(value, fmt)
|
||||
return dt.timestamp()
|
||||
except Exception as e:
|
||||
last_err = e
|
||||
raise ValueError(f"无法解析时间: {value} ({last_err})")
|
||||
|
||||
def parse_time_range(time_range: str) -> tuple[float, float]:
|
||||
"""
|
||||
解析时间范围字符串，返回开始和结束时间戳
|
||||
格式: "YYYY-MM-DD HH:MM:SS - YYYY-MM-DD HH:MM:SS"
|
||||
"""
|
||||
if " - " not in time_range:
|
||||
raise ValueError("时间范围格式错误，应使用 ' - ' 分隔开始和结束时间")
|
||||
|
||||
start_str, end_str = time_range.split(" - ", 1)
|
||||
start_timestamp = parse_datetime_to_timestamp(start_str.strip())
|
||||
end_timestamp = parse_datetime_to_timestamp(end_str.strip())
|
||||
|
||||
if start_timestamp > end_timestamp:
|
||||
raise ValueError("开始时间不能晚于结束时间")
|
||||
|
||||
return start_timestamp, end_timestamp
|
||||
class GetMemoryTool(BaseTool):
|
||||
"""获取用户信息"""
|
||||
|
||||
name = "get_memory"
|
||||
description = "在记忆中搜索，获取某个问题的答案"
|
||||
description = "在记忆中搜索，获取某个问题的答案，可以指定搜索的时间范围或时间点"
|
||||
parameters = [
|
||||
("question", ToolParamType.STRING, "需要获取答案的问题", True, None)
|
||||
("question", ToolParamType.STRING, "需要获取答案的问题", True, None),
|
||||
("time_point", ToolParamType.STRING, "需要获取记忆的时间点，格式为YYYY-MM-DD HH:MM:SS", False, None),
|
||||
("time_range", ToolParamType.STRING, "需要获取记忆的时间范围，格式为YYYY-MM-DD HH:MM:SS - YYYY-MM-DD HH:MM:SS", False, None)
|
||||
]
|
||||
|
||||
available_for_llm = True
|
||||
|
||||
async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
|
||||
"""执行比较两个数的大小
|
||||
"""执行记忆搜索
|
||||
|
||||
Args:
|
||||
function_args: 工具参数
|
||||
|
|
@ -32,12 +85,141 @@ class GetMemoryTool(BaseTool):
|
|||
dict: 工具执行结果
|
||||
"""
|
||||
question: str = function_args.get("question") # type: ignore
|
||||
time_point: str = function_args.get("time_point") # type: ignore
|
||||
time_range: str = function_args.get("time_range") # type: ignore
|
||||
|
||||
answer = await global_memory_chest.get_answer_by_question(question=question)
|
||||
if not answer:
|
||||
return {"content": f"问题：{question}，没有找到相关记忆"}
|
||||
# 检查是否指定了时间参数
|
||||
has_time_params = bool(time_point or time_range)
|
||||
|
||||
return {"content": f"问题：{question}，答案：{answer}"}
|
||||
if has_time_params and not self.chat_id:
|
||||
return {"content": f"问题：{question}，无法获取聊天记录：缺少chat_id"}
|
||||
|
||||
# 创建并行任务
|
||||
tasks = []
|
||||
|
||||
# 原任务：从记忆仓库获取答案
|
||||
memory_task = asyncio.create_task(
|
||||
global_memory_chest.get_answer_by_question(question=question)
|
||||
)
|
||||
tasks.append(("memory", memory_task))
|
||||
|
||||
# 新任务：从聊天记录获取答案（如果指定了时间参数）
|
||||
chat_task = None
|
||||
if has_time_params:
|
||||
chat_task = asyncio.create_task(
|
||||
self._get_answer_from_chat_history(question, time_point, time_range)
|
||||
)
|
||||
tasks.append(("chat", chat_task))
|
||||
|
||||
# 等待所有任务完成
|
||||
results = {}
|
||||
for task_name, task in tasks:
|
||||
try:
|
||||
results[task_name] = await task
|
||||
except Exception as e:
|
||||
logger.error(f"任务 {task_name} 执行失败: {e}")
|
||||
results[task_name] = None
|
||||
|
||||
# 处理结果
|
||||
memory_answer = results.get("memory")
|
||||
chat_answer = results.get("chat")
|
||||
|
||||
# 构建返回内容
|
||||
content_parts = [f"问题：{question}"]
|
||||
|
||||
if memory_answer:
|
||||
content_parts.append(f"记忆仓库答案：{memory_answer}")
|
||||
else:
|
||||
content_parts.append("记忆仓库：没有找到相关记忆")
|
||||
|
||||
if chat_answer:
|
||||
content_parts.append(f"聊天记录答案：{chat_answer}")
|
||||
elif has_time_params:
|
||||
content_parts.append("聊天记录：没有找到相关记录")
|
||||
|
||||
return {"content": "\n".join(content_parts)}
|
||||
|
||||
async def _get_answer_from_chat_history(self, question: str, time_point: str = None, time_range: str = None) -> str:
|
||||
"""从聊天记录中获取问题的答案"""
|
||||
try:
|
||||
# 确定时间范围
|
||||
if time_point:
|
||||
# 时间点：搜索前后25条记录
|
||||
target_timestamp = parse_datetime_to_timestamp(time_point)
|
||||
# 获取前后各25条记录，总共50条
|
||||
messages_before = get_messages_by_time_in_chat(
|
||||
chat_id=self.chat_id,
|
||||
start_time=0,
|
||||
end_time=target_timestamp,
|
||||
limit=25,
|
||||
limit_mode="latest"
|
||||
)
|
||||
messages_after = get_messages_by_time_in_chat(
|
||||
chat_id=self.chat_id,
|
||||
start_time=target_timestamp,
|
||||
end_time=float('inf'),
|
||||
limit=25,
|
||||
limit_mode="earliest"
|
||||
)
|
||||
messages = messages_before + messages_after
|
||||
elif time_range:
|
||||
# 时间范围：搜索范围内最多50条记录
|
||||
start_timestamp, end_timestamp = parse_time_range(time_range)
|
||||
messages = get_messages_by_time_in_chat(
|
||||
chat_id=self.chat_id,
|
||||
start_time=start_timestamp,
|
||||
end_time=end_timestamp,
|
||||
limit=50,
|
||||
limit_mode="latest"
|
||||
)
|
||||
else:
|
||||
return "未指定时间参数"
|
||||
|
||||
if not messages:
|
||||
return "没有找到相关聊天记录"
|
||||
|
||||
# 将消息转换为可读格式
|
||||
chat_content = build_readable_messages(messages, timestamp_mode="relative")
|
||||
|
||||
if not chat_content.strip():
|
||||
return "聊天记录为空"
|
||||
|
||||
# 使用LLM分析聊天内容并回答问题
|
||||
try:
|
||||
llm_request = LLMRequest(
|
||||
model_set=model_config.model_task_config.utils_small,
|
||||
request_type="chat_history_analysis"
|
||||
)
|
||||
|
||||
analysis_prompt = f"""请根据以下聊天记录内容，回答用户的问题。
|
||||
|
||||
聊天记录：
|
||||
{chat_content}
|
||||
|
||||
用户问题：{question}
|
||||
|
||||
请仔细分析聊天记录，提取与问题相关的信息，并给出准确的答案。如果聊天记录中没有相关信息，请说明"聊天记录中没有找到相关信息"。
|
||||
|
||||
答案："""
|
||||
|
||||
response, (reasoning, model_name, tool_calls) = await llm_request.generate_response_async(
|
||||
prompt=analysis_prompt,
|
||||
temperature=0.3,
|
||||
max_tokens=500
|
||||
)
|
||||
|
||||
return f"基于聊天记录分析：{response}"
|
||||
|
||||
except Exception as llm_error:
|
||||
logger.error(f"LLM分析聊天记录失败: {llm_error}")
|
||||
# 如果LLM分析失败，返回聊天内容的摘要
|
||||
if len(chat_content) > 300:
|
||||
chat_content = chat_content[:300] + "..."
|
||||
return f"聊天记录摘要：{chat_content}"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"从聊天记录获取答案失败: {e}")
|
||||
return f"聊天记录分析失败: {str(e)}"
|
||||
|
||||
class GetMemoryAction(BaseAction):
|
||||
"""关系动作 - 获取记忆"""
|
||||
|
|
@ -45,7 +227,7 @@ class GetMemoryAction(BaseAction):
|
|||
activation_type = ActionActivationType.LLM_JUDGE
|
||||
parallel_action = True
|
||||
|
||||
# 动作基本信息
|
||||
# 动作基本信息
|
||||
action_name = "get_memory"
|
||||
action_description = (
|
||||
"在记忆中搜寻某个问题的答案"
|
||||
|
|
@ -60,7 +242,7 @@ class GetMemoryAction(BaseAction):
|
|||
action_require = [
|
||||
"在记忆中搜寻某个问题的答案",
|
||||
"有你不了解的概念",
|
||||
"有人提问关于过去的事情"
|
||||
"有人提问关于过去的事情",
|
||||
"你需要根据记忆回答某个问题",
|
||||
]
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue