fix：意外启动s4u
This commit is contained in:
SengokuCola
@@ -21,9 +21,8 @@ from src.plugin_system.base.component_types import ActionInfo, ChatMode
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from src.plugin_system.apis import generator_api, send_api, message_api
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from src.chat.willing.willing_manager import get_willing_manager
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from src.chat.mai_thinking.mai_think import mai_thinking_manager
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from maim_message.message_base import GroupInfo,UserInfo
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ENABLE_THINKING = False
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from maim_message.message_base import GroupInfo
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from src.mais4u.constant_s4u import ENABLE_THINKING
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ERROR_LOOP_INFO = {
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"loop_plan_info": {
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@@ -296,7 +295,8 @@ class HeartFChatting:
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logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考[模式：{self.loop_mode}]")
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await self.send_typing()
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if ENABLE_THINKING:
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await self.send_typing()
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async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
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loop_start_time = time.time()
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@@ -366,11 +366,12 @@ class HeartFChatting:
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# 发送回复 (不再需要传入 chat)
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reply_text = await self._send_response(response_set, reply_to_str, loop_start_time,message_data)
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await self.stop_typing()
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if ENABLE_THINKING:
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await self.stop_typing()
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await mai_thinking_manager.get_mai_think(self.stream_id).do_think_after_response(reply_text)
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@@ -3,7 +3,7 @@ import time
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config
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from src.chat.message_receive.message import MessageSending, MessageRecv, MessageRecvS4U
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from src.chat.message_receive.message import MessageRecvS4U
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from src.mais4u.mais4u_chat.s4u_msg_processor import S4UMessageProcessor
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from src.mais4u.mais4u_chat.internal_manager import internal_manager
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from src.common.logger import get_logger
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1
src/mais4u/constant_s4u.py
Normal file
1
src/mais4u/constant_s4u.py
Normal file
@@ -0,0 +1 @@
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ENABLE_THINKING = False
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@@ -5,15 +5,10 @@
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这是系统的内置插件，提供基础的聊天交互功能
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"""
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import random
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import time
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from typing import List, Tuple, Type
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import asyncio
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import re
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import traceback
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# 导入新插件系统
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from src.plugin_system import BasePlugin, register_plugin, BaseAction, ComponentInfo, ActionActivationType, ChatMode
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from src.plugin_system import BasePlugin, register_plugin, ComponentInfo, ActionActivationType
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from src.plugin_system.base.config_types import ConfigField
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from src.config.config import global_config
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@@ -21,140 +16,12 @@ from src.config.config import global_config
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from src.common.logger import get_logger
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# 导入API模块 - 标准Python包方式
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from src.plugin_system.apis import generator_api, message_api
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from src.plugins.built_in.core_actions.no_reply import NoReplyAction
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from src.plugins.built_in.core_actions.emoji import EmojiAction
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from src.person_info.person_info import get_person_info_manager
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from src.chat.mai_thinking.mai_think import mai_thinking_manager
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from src.plugins.built_in.core_actions.reply import ReplyAction
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logger = get_logger("core_actions")
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# 常量定义
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WAITING_TIME_THRESHOLD = 1200 # 等待新消息时间阈值，单位秒
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ENABLE_THINKING = False
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class ReplyAction(BaseAction):
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"""回复动作 - 参与聊天回复"""
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# 激活设置
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focus_activation_type = ActionActivationType.NEVER
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normal_activation_type = ActionActivationType.NEVER
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mode_enable = ChatMode.FOCUS
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parallel_action = False
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# 动作基本信息
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action_name = "reply"
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action_description = "参与聊天回复，发送文本进行表达"
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# 动作参数定义
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action_parameters = {}
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# 动作使用场景
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action_require = ["你想要闲聊或者随便附和", "有人提到你", "如果你刚刚进行了回复，不要对同一个话题重复回应"]
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# 关联类型
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associated_types = ["text"]
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def _parse_reply_target(self, target_message: str) -> tuple:
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sender = ""
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target = ""
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if ":" in target_message or "：" in target_message:
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# 使用正则表达式匹配中文或英文冒号
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parts = re.split(pattern=r"[:：]", string=target_message, maxsplit=1)
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if len(parts) == 2:
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sender = parts[0].strip()
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target = parts[1].strip()
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return sender, target
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async def execute(self) -> Tuple[bool, str]:
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"""执行回复动作"""
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logger.info(f"{self.log_prefix} 决定进行回复")
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start_time = self.action_data.get("loop_start_time", time.time())
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user_id = self.user_id
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platform = self.platform
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# logger.info(f"{self.log_prefix} 用户ID: {user_id}, 平台: {platform}")
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person_id = get_person_info_manager().get_person_id(platform, user_id)
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# logger.info(f"{self.log_prefix} 人物ID: {person_id}")
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person_name = get_person_info_manager().get_value_sync(person_id, "person_name")
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reply_to = f"{person_name}:{self.action_message.get('processed_plain_text', '')}"
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logger.info(f"{self.log_prefix} 回复目标: {reply_to}")
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try:
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if prepared_reply := self.action_data.get("prepared_reply", ""):
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reply_text = prepared_reply
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else:
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try:
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success, reply_set, _ = await asyncio.wait_for(
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generator_api.generate_reply(
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extra_info="",
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reply_to=reply_to,
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chat_id=self.chat_id,
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request_type="chat.replyer.focus",
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enable_tool=global_config.tool.enable_in_focus_chat,
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),
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timeout=global_config.chat.thinking_timeout,
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)
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except asyncio.TimeoutError:
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logger.warning(f"{self.log_prefix} 回复生成超时 ({global_config.chat.thinking_timeout}s)")
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return False, "timeout"
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# 检查从start_time以来的新消息数量
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# 获取动作触发时间或使用默认值
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current_time = time.time()
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new_message_count = message_api.count_new_messages(
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chat_id=self.chat_id, start_time=start_time, end_time=current_time
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)
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# 根据新消息数量决定是否使用reply_to
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need_reply = new_message_count >= random.randint(2, 4)
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logger.info(
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f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，{'使用' if need_reply else '不使用'}引用回复"
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)
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# 构建回复文本
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reply_text = ""
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first_replied = False
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reply_to_platform_id = f"{platform}:{user_id}"
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for reply_seg in reply_set:
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data = reply_seg[1]
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if not first_replied:
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if need_reply:
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await self.send_text(
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content=data, reply_to=reply_to, reply_to_platform_id=reply_to_platform_id, typing=False
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)
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else:
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await self.send_text(content=data, reply_to_platform_id=reply_to_platform_id, typing=False)
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first_replied = True
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else:
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await self.send_text(content=data, reply_to_platform_id=reply_to_platform_id, typing=True)
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reply_text += data
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# 存储动作记录
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reply_text = f"你对{person_name}进行了回复：{reply_text}"
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if ENABLE_THINKING:
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await mai_thinking_manager.get_mai_think(self.chat_id).do_think_after_response(reply_text)
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await self.store_action_info(
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action_build_into_prompt=False,
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action_prompt_display=reply_text,
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action_done=True,
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)
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# 重置NoReplyAction的连续计数器
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NoReplyAction.reset_consecutive_count()
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return success, reply_text
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except Exception as e:
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logger.error(f"{self.log_prefix} 回复动作执行失败: {e}")
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traceback.print_exc()
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return False, f"回复失败: {str(e)}"
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@register_plugin
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class CoreActionsPlugin(BasePlugin):
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"""核心动作插件
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141
src/plugins/built_in/core_actions/reply.py
Normal file
141
src/plugins/built_in/core_actions/reply.py
Normal file
@@ -0,0 +1,141 @@
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# 导入新插件系统
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from src.plugin_system import BaseAction, ActionActivationType, ChatMode
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from src.config.config import global_config
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import random
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import time
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from typing import Tuple
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import asyncio
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import re
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import traceback
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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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# 导入API模块 - 标准Python包方式
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from src.plugin_system.apis import generator_api, message_api
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from src.plugins.built_in.core_actions.no_reply import NoReplyAction
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from src.person_info.person_info import get_person_info_manager
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from src.chat.mai_thinking.mai_think import mai_thinking_manager
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from src.mais4u.constant_s4u import ENABLE_THINKING
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logger = get_logger("reply_action")
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class ReplyAction(BaseAction):
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"""回复动作 - 参与聊天回复"""
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# 激活设置
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focus_activation_type = ActionActivationType.NEVER
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normal_activation_type = ActionActivationType.NEVER
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mode_enable = ChatMode.FOCUS
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parallel_action = False
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# 动作基本信息
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action_name = "reply"
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action_description = "参与聊天回复，发送文本进行表达"
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# 动作参数定义
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action_parameters = {}
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# 动作使用场景
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action_require = ["你想要闲聊或者随便附和", "有人提到你", "如果你刚刚进行了回复，不要对同一个话题重复回应"]
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# 关联类型
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associated_types = ["text"]
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def _parse_reply_target(self, target_message: str) -> tuple:
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sender = ""
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target = ""
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if ":" in target_message or "：" in target_message:
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# 使用正则表达式匹配中文或英文冒号
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parts = re.split(pattern=r"[:：]", string=target_message, maxsplit=1)
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if len(parts) == 2:
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sender = parts[0].strip()
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target = parts[1].strip()
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return sender, target
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async def execute(self) -> Tuple[bool, str]:
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"""执行回复动作"""
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logger.info(f"{self.log_prefix} 决定进行回复")
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start_time = self.action_data.get("loop_start_time", time.time())
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user_id = self.user_id
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platform = self.platform
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# logger.info(f"{self.log_prefix} 用户ID: {user_id}, 平台: {platform}")
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person_id = get_person_info_manager().get_person_id(platform, user_id)
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# logger.info(f"{self.log_prefix} 人物ID: {person_id}")
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person_name = get_person_info_manager().get_value_sync(person_id, "person_name")
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reply_to = f"{person_name}:{self.action_message.get('processed_plain_text', '')}"
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logger.info(f"{self.log_prefix} 回复目标: {reply_to}")
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try:
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if prepared_reply := self.action_data.get("prepared_reply", ""):
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reply_text = prepared_reply
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else:
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try:
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success, reply_set, _ = await asyncio.wait_for(
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generator_api.generate_reply(
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extra_info="",
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reply_to=reply_to,
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chat_id=self.chat_id,
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request_type="chat.replyer.focus",
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enable_tool=global_config.tool.enable_in_focus_chat,
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),
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timeout=global_config.chat.thinking_timeout,
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)
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except asyncio.TimeoutError:
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logger.warning(f"{self.log_prefix} 回复生成超时 ({global_config.chat.thinking_timeout}s)")
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return False, "timeout"
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# 检查从start_time以来的新消息数量
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# 获取动作触发时间或使用默认值
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current_time = time.time()
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new_message_count = message_api.count_new_messages(
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chat_id=self.chat_id, start_time=start_time, end_time=current_time
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)
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# 根据新消息数量决定是否使用reply_to
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need_reply = new_message_count >= random.randint(2, 4)
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logger.info(
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f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，{'使用' if need_reply else '不使用'}引用回复"
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)
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# 构建回复文本
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reply_text = ""
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first_replied = False
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reply_to_platform_id = f"{platform}:{user_id}"
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for reply_seg in reply_set:
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data = reply_seg[1]
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if not first_replied:
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if need_reply:
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await self.send_text(
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content=data, reply_to=reply_to, reply_to_platform_id=reply_to_platform_id, typing=False
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)
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else:
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await self.send_text(content=data, reply_to_platform_id=reply_to_platform_id, typing=False)
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first_replied = True
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else:
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await self.send_text(content=data, reply_to_platform_id=reply_to_platform_id, typing=True)
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reply_text += data
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# 存储动作记录
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reply_text = f"你对{person_name}进行了回复：{reply_text}"
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if ENABLE_THINKING:
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await mai_thinking_manager.get_mai_think(self.chat_id).do_think_after_response(reply_text)
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await self.store_action_info(
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action_build_into_prompt=False,
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action_prompt_display=reply_text,
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action_done=True,
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)
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# 重置NoReplyAction的连续计数器
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NoReplyAction.reset_consecutive_count()
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return success, reply_text
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except Exception as e:
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logger.error(f"{self.log_prefix} 回复动作执行失败: {e}")
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traceback.print_exc()
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return False, f"回复失败: {str(e)}"
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