merge: sync upstream/r-dev and resolve real conflicts
This commit is contained in:
A-Dawn
@@ -1,27 +1,28 @@
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import time
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"""PFC 侧消息发送封装。"""
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from typing import Optional
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from maim_message import Seg
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from rich.traceback import install
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from src.common.data_models.mai_message_data_model import MaiMessage, UserInfo
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from src.chat.message_receive.chat_manager import BotChatSession
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from src.chat.message_receive.message import MessageSending
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from src.chat.message_receive.uni_message_sender import UniversalMessageSender
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from src.chat.utils.utils import get_bot_account
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from src.common.data_models.mai_message_data_model import MaiMessage
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from src.common.logger import get_logger
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from src.config.config import global_config
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from src.services import send_service as send_api
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install(extra_lines=3)
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logger = get_logger("message_sender")
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class DirectMessageSender:
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"""直接消息发送器"""
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"""直接消息发送器。"""
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def __init__(self, private_name: str):
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def __init__(self, private_name: str) -> None:
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"""初始化直接消息发送器。
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Args:
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private_name: 当前私聊实例的名称。
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"""
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self.private_name = private_name
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async def send_message(
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@@ -30,58 +31,31 @@ class DirectMessageSender:
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content: str,
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reply_to_message: Optional[MaiMessage] = None,
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) -> None:
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"""发送消息到聊天流
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"""发送文本消息到聊天流。
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Args:
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chat_stream: 聊天会话
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content: 消息内容
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reply_to_message: 要回复的消息（可选）
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chat_stream: 目标聊天会话。
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content: 待发送的文本内容。
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reply_to_message: 可选的引用回复锚点消息。
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Raises:
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RuntimeError: 当消息发送失败时抛出。
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"""
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try:
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# 创建消息内容
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segments = Seg(type="seglist", data=[Seg(type="text", data=content)])
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# 获取麦麦的信息
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bot_user_id = get_bot_account(chat_stream.platform)
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if not bot_user_id:
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logger.error(f"[私聊][{self.private_name}]平台 {chat_stream.platform} 未配置机器人账号，无法发送消息")
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raise RuntimeError(f"平台 {chat_stream.platform} 未配置机器人账号")
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bot_user_info = UserInfo(
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user_id=bot_user_id,
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user_nickname=global_config.bot.nickname,
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sent = await send_api.text_to_stream(
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text=content,
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stream_id=chat_stream.session_id,
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set_reply=reply_to_message is not None,
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reply_message=reply_to_message,
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storage_message=True,
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)
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# 用当前时间作为message_id，和之前那套sender一样
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message_id = f"dm{round(time.time(), 2)}"
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# 构建发送者信息（私聊时为接收者）
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sender_info = None
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if reply_to_message and reply_to_message.message_info and reply_to_message.message_info.user_info:
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sender_info = reply_to_message.message_info.user_info
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# 构建消息对象
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message = MessageSending(
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message_id=message_id,
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session=chat_stream,
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bot_user_info=bot_user_info,
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sender_info=sender_info,
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message_segment=segments,
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reply=reply_to_message,
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is_head=True,
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is_emoji=False,
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thinking_start_time=time.time(),
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)
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# 发送消息
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message_sender = UniversalMessageSender()
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sent = await message_sender.send_message(message, typing=False, set_reply=False, storage_message=True)
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if sent:
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logger.info(f"[私聊][{self.private_name}]PFC消息已发送: {content}")
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else:
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logger.error(f"[私聊][{self.private_name}]PFC消息发送失败")
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raise RuntimeError("消息发送失败")
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return
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except Exception as e:
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logger.error(f"[私聊][{self.private_name}]PFC消息发送失败: {str(e)}")
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logger.error(f"[私聊][{self.private_name}]PFC消息发送失败")
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raise RuntimeError("消息发送失败")
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except Exception as exc:
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logger.error(f"[私聊][{self.private_name}]PFC消息发送失败: {exc}")
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raise
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@@ -8,8 +8,8 @@ from rich.traceback import install
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from src.config.config import global_config
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from src.common.logger import get_logger
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from src.common.utils.utils_config import ExpressionConfigUtils
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from src.bw_learner.expression_learner import ExpressionLearner
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from src.bw_learner.jargon_miner import JargonMiner
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from src.learners.expression_learner import ExpressionLearner
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from src.learners.jargon_miner import JargonMiner
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from src.chat.message_receive.chat_manager import BotChatSession
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from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
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from src.chat.message_receive.message import SessionMessage
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@@ -1,30 +1,32 @@
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from datetime import datetime
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from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
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import json
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import time
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import traceback
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import random
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import re
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from typing import Dict, Optional, Tuple, List, TYPE_CHECKING
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from rich.traceback import install
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from datetime import datetime
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from json_repair import repair_json
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import time
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import traceback
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from json_repair import repair_json
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from rich.traceback import install
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config, model_config
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from src.common.logger import get_logger
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from src.chat.logger.plan_reply_logger import PlanReplyLogger
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from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
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from src.chat.planner_actions.action_manager import ActionManager
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from src.chat.utils.utils import get_chat_type_and_target_info
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from src.common.data_models.info_data_model import ActionPlannerInfo
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from src.common.logger import get_logger
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from src.common.utils.utils_action import ActionUtils
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from src.config.config import global_config, model_config
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from src.core.types import ActionActivationType, ActionInfo, ComponentType
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from src.llm_models.utils_model import LLMRequest
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from src.plugin_runtime.component_query import component_query_service
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from src.prompt.prompt_manager import prompt_manager
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from src.services.message_service import (
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build_readable_messages_with_id,
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get_actions_by_timestamp_with_chat,
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get_messages_before_time_in_chat,
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)
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from src.chat.utils.utils import get_chat_type_and_target_info
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from src.chat.planner_actions.action_manager import ActionManager
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from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
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from src.core.types import ActionActivationType, ActionInfo, ComponentType
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from src.core.component_registry import component_registry
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if TYPE_CHECKING:
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from src.common.data_models.info_data_model import TargetPersonInfo
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@@ -320,7 +322,7 @@ class BrainPlanner:
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current_available_actions_dict = self.action_manager.get_using_actions()
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# 获取完整的动作信息
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all_registered_actions: Dict[str, ActionInfo] = component_registry.get_components_by_type( # type: ignore
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all_registered_actions: Dict[str, ActionInfo] = component_query_service.get_components_by_type( # type: ignore
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ComponentType.ACTION
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)
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current_available_actions = {}
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@@ -1,734 +0,0 @@
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import asyncio
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import time
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import traceback
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import random
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from typing import List, Optional, Dict, Any, Tuple, TYPE_CHECKING
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from rich.traceback import install
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from src.config.config import global_config
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from src.common.logger import get_logger
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from src.common.data_models.info_data_model import ActionPlannerInfo
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from src.common.data_models.message_data_model import ReplyContentType
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from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager
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from src.chat.utils.prompt_builder import global_prompt_manager
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from src.chat.utils.timer_calculator import Timer
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from src.chat.planner_actions.planner import ActionPlanner
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from src.chat.planner_actions.action_modifier import ActionModifier
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from src.chat.planner_actions.action_manager import ActionManager
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from src.chat.heart_flow.hfc_utils import CycleDetail
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from src.bw_learner.expression_learner import expression_learner_manager
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from src.chat.heart_flow.frequency_control import frequency_control_manager
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from src.bw_learner.message_recorder import extract_and_distribute_messages
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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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from src.plugin_system.apis import generator_api, send_api, message_api, database_api
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from src.chat.utils.chat_message_builder import (
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build_readable_messages_with_id,
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get_raw_msg_before_timestamp_with_chat,
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)
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from src.chat.utils.utils import record_replyer_action_temp
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from src.memory_system.chat_history_summarizer import ChatHistorySummarizer
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if TYPE_CHECKING:
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from src.common.data_models.database_data_model import DatabaseMessages
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from src.common.data_models.message_data_model import ReplySetModel
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ERROR_LOOP_INFO = {
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"loop_plan_info": {
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"action_result": {
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"action_type": "error",
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"action_data": {},
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"reasoning": "循环处理失败",
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},
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},
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"loop_action_info": {
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"action_taken": False,
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"reply_text": "",
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"command": "",
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"taken_time": time.time(),
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},
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}
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install(extra_lines=3)
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# 注释：原来的动作修改超时常量已移除，因为改为顺序执行
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logger = get_logger("hfc") # Logger Name Changed
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class HeartFChatting:
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"""
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管理一个连续的Focus Chat循环
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用于在特定聊天流中生成回复。
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其生命周期现在由其关联的 SubHeartflow 的 FOCUSED 状态控制。
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"""
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def __init__(self, chat_id: str):
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"""
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HeartFChatting 初始化函数
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参数:
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chat_id: 聊天流唯一标识符(如stream_id)
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on_stop_focus_chat: 当收到stop_focus_chat命令时调用的回调函数
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performance_version: 性能记录版本号，用于区分不同启动版本
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"""
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# 基础属性
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self.stream_id: str = chat_id # 聊天流ID
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self.chat_stream: ChatStream = get_chat_manager().get_stream(self.stream_id) # type: ignore
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if not self.chat_stream:
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raise ValueError(f"无法找到聊天流: {self.stream_id}")
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self.log_prefix = f"[{get_chat_manager().get_stream_name(self.stream_id) or self.stream_id}]"
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self.expression_learner = expression_learner_manager.get_expression_learner(self.stream_id)
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self.action_manager = ActionManager()
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self.action_planner = ActionPlanner(chat_id=self.stream_id, action_manager=self.action_manager)
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self.action_modifier = ActionModifier(action_manager=self.action_manager, chat_id=self.stream_id)
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# 循环控制内部状态
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self.running: bool = False
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self._loop_task: Optional[asyncio.Task] = None # 主循环任务
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# 添加循环信息管理相关的属性
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self.history_loop: List[CycleDetail] = []
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self._cycle_counter = 0
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self._current_cycle_detail: CycleDetail = None # type: ignore
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self.last_read_time = time.time() - 2
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self.is_mute = False
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self.last_active_time = time.time() # 记录上一次非noreply时间
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self.question_probability_multiplier = 1
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self.questioned = False
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# 跟踪连续 no_reply 次数，用于动态调整阈值
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self.consecutive_no_reply_count = 0
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# 聊天内容概括器
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self.chat_history_summarizer = ChatHistorySummarizer(chat_id=self.stream_id)
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async def start(self):
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"""检查是否需要启动主循环，如果未激活则启动。"""
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# 如果循环已经激活，直接返回
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if self.running:
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logger.debug(f"{self.log_prefix} HeartFChatting 已激活，无需重复启动")
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return
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try:
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# 标记为活动状态，防止重复启动
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self.running = True
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self._loop_task = asyncio.create_task(self._main_chat_loop())
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self._loop_task.add_done_callback(self._handle_loop_completion)
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# 启动聊天内容概括器的后台定期检查循环
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await self.chat_history_summarizer.start()
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logger.info(f"{self.log_prefix} HeartFChatting 启动完成")
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except Exception as e:
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# 启动失败时重置状态
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self.running = False
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self._loop_task = None
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logger.error(f"{self.log_prefix} HeartFChatting 启动失败: {e}")
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raise
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def _handle_loop_completion(self, task: asyncio.Task):
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"""当 _hfc_loop 任务完成时执行的回调。"""
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try:
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if exception := task.exception():
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logger.error(f"{self.log_prefix} HeartFChatting: 脱离了聊天(异常): {exception}")
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logger.error(traceback.format_exc()) # Log full traceback for exceptions
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else:
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logger.info(f"{self.log_prefix} HeartFChatting: 脱离了聊天 (外部停止)")
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except asyncio.CancelledError:
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logger.info(f"{self.log_prefix} HeartFChatting: 结束了聊天")
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def start_cycle(self) -> Tuple[Dict[str, float], str]:
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self._cycle_counter += 1
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self._current_cycle_detail = CycleDetail(self._cycle_counter)
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self._current_cycle_detail.thinking_id = f"tid{str(round(time.time(), 2))}"
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cycle_timers = {}
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return cycle_timers, self._current_cycle_detail.thinking_id
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def end_cycle(self, loop_info, cycle_timers):
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self._current_cycle_detail.set_loop_info(loop_info)
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self.history_loop.append(self._current_cycle_detail)
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self._current_cycle_detail.timers = cycle_timers
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self._current_cycle_detail.end_time = time.time()
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def print_cycle_info(self, cycle_timers):
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# 记录循环信息和计时器结果
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timer_strings = []
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for name, elapsed in cycle_timers.items():
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if elapsed < 0.1:
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# 不显示小于0.1秒的计时器
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continue
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formatted_time = f"{elapsed:.2f}秒"
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timer_strings.append(f"{name}: {formatted_time}")
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logger.info(
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f"{self.log_prefix} 第{self._current_cycle_detail.cycle_id}次思考,"
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f"耗时: {self._current_cycle_detail.end_time - self._current_cycle_detail.start_time:.1f}秒;" # type: ignore
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+ (f"详情: {'; '.join(timer_strings)}" if timer_strings else "")
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)
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async def _loopbody(self):
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recent_messages_list = message_api.get_messages_by_time_in_chat(
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chat_id=self.stream_id,
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start_time=self.last_read_time,
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end_time=time.time(),
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limit=20,
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limit_mode="latest",
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filter_mai=True,
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filter_command=False,
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||||
filter_intercept_message_level=0,
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||||
)
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||||
# 根据连续 no_reply 次数动态调整阈值
|
||||
# 3次 no_reply 时，阈值调高到 1.5（50%概率为1，50%概率为2）
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||||
# 5次 no_reply 时，提高到 2（大于等于两条消息的阈值）
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if self.consecutive_no_reply_count >= 5:
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threshold = 2
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elif self.consecutive_no_reply_count >= 3:
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# 1.5 的含义：50%概率为1，50%概率为2
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threshold = 2 if random.random() < 0.5 else 1
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else:
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threshold = 1
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||||
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if len(recent_messages_list) >= threshold:
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# for message in recent_messages_list:
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# print(message.processed_plain_text)
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||||
self.last_read_time = time.time()
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||||
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||||
# !此处使at或者提及必定回复
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||||
mentioned_message = None
|
||||
for message in recent_messages_list:
|
||||
if (message.is_mentioned or message.is_at) and global_config.chat.mentioned_bot_reply:
|
||||
mentioned_message = message
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||||
|
||||
# logger.info(f"{self.log_prefix} 当前talk_value: {global_config.chat.get_talk_value(self.stream_id)}")
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||||
|
||||
# *控制频率用
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||||
if mentioned_message:
|
||||
await self._observe(recent_messages_list=recent_messages_list, force_reply_message=mentioned_message)
|
||||
elif (
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||||
random.random()
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||||
< global_config.chat.get_talk_value(self.stream_id)
|
||||
* frequency_control_manager.get_or_create_frequency_control(self.stream_id).get_talk_frequency_adjust()
|
||||
):
|
||||
await self._observe(recent_messages_list=recent_messages_list)
|
||||
else:
|
||||
# 没有提到，继续保持沉默，等待5秒防止频繁触发
|
||||
await asyncio.sleep(10)
|
||||
return True
|
||||
else:
|
||||
await asyncio.sleep(0.2)
|
||||
return True
|
||||
return True
|
||||
|
||||
async def _send_and_store_reply(
|
||||
self,
|
||||
response_set: "ReplySetModel",
|
||||
action_message: "DatabaseMessages",
|
||||
cycle_timers: Dict[str, float],
|
||||
thinking_id,
|
||||
actions,
|
||||
selected_expressions: Optional[List[int]] = None,
|
||||
quote_message: Optional[bool] = None,
|
||||
) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
|
||||
with Timer("回复发送", cycle_timers):
|
||||
reply_text = await self._send_response(
|
||||
reply_set=response_set,
|
||||
message_data=action_message,
|
||||
selected_expressions=selected_expressions,
|
||||
quote_message=quote_message,
|
||||
)
|
||||
|
||||
# 获取 platform，如果不存在则从 chat_stream 获取，如果还是 None 则使用默认值
|
||||
platform = action_message.chat_info.platform
|
||||
if platform is None:
|
||||
platform = getattr(self.chat_stream, "platform", "unknown")
|
||||
|
||||
person = Person(platform=platform, user_id=action_message.user_info.user_id)
|
||||
person_name = person.person_name
|
||||
action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
|
||||
|
||||
await database_api.store_action_info(
|
||||
chat_stream=self.chat_stream,
|
||||
action_build_into_prompt=False,
|
||||
action_prompt_display=action_prompt_display,
|
||||
action_done=True,
|
||||
thinking_id=thinking_id,
|
||||
action_data={"reply_text": reply_text},
|
||||
action_name="reply",
|
||||
)
|
||||
|
||||
# 构建循环信息
|
||||
loop_info: Dict[str, Any] = {
|
||||
"loop_plan_info": {
|
||||
"action_result": actions,
|
||||
},
|
||||
"loop_action_info": {
|
||||
"action_taken": True,
|
||||
"reply_text": reply_text,
|
||||
"command": "",
|
||||
"taken_time": time.time(),
|
||||
},
|
||||
}
|
||||
|
||||
return loop_info, reply_text, cycle_timers
|
||||
|
||||
async def _observe(
|
||||
self, # interest_value: float = 0.0,
|
||||
recent_messages_list: Optional[List["DatabaseMessages"]] = None,
|
||||
force_reply_message: Optional["DatabaseMessages"] = None,
|
||||
) -> bool: # sourcery skip: merge-else-if-into-elif, remove-redundant-if
|
||||
if recent_messages_list is None:
|
||||
recent_messages_list = []
|
||||
_reply_text = "" # 初始化reply_text变量，避免UnboundLocalError
|
||||
|
||||
start_time = time.time()
|
||||
async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
|
||||
# 通过 MessageRecorder 统一提取消息并分发给 expression_learner 和 jargon_miner
|
||||
# 在 replyer 执行时触发，统一管理时间窗口，避免重复获取消息
|
||||
asyncio.create_task(extract_and_distribute_messages(self.stream_id))
|
||||
|
||||
# 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
|
||||
# asyncio.create_task(check_and_make_question(self.stream_id))
|
||||
# 添加聊天内容概括任务 - 累积、打包和压缩聊天记录
|
||||
# 注意：后台循环已在start()中启动，这里作为额外触发点，在有思考时立即处理
|
||||
# asyncio.create_task(self.chat_history_summarizer.process())
|
||||
|
||||
cycle_timers, thinking_id = self.start_cycle()
|
||||
logger.info(
|
||||
f"{self.log_prefix} 开始第{self._cycle_counter}次思考(频率: {global_config.chat.get_talk_value(self.stream_id)})"
|
||||
)
|
||||
|
||||
# 第一步：动作检查
|
||||
available_actions: Dict[str, ActionInfo] = {}
|
||||
try:
|
||||
await self.action_modifier.modify_actions()
|
||||
available_actions = self.action_manager.get_using_actions()
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 动作修改失败: {e}")
|
||||
|
||||
# 执行planner
|
||||
is_group_chat, chat_target_info, _ = self.action_planner.get_necessary_info()
|
||||
|
||||
message_list_before_now = get_raw_msg_before_timestamp_with_chat(
|
||||
chat_id=self.stream_id,
|
||||
timestamp=time.time(),
|
||||
limit=int(global_config.chat.max_context_size * 0.6),
|
||||
filter_intercept_message_level=1,
|
||||
)
|
||||
chat_content_block, message_id_list = build_readable_messages_with_id(
|
||||
messages=message_list_before_now,
|
||||
timestamp_mode="normal_no_YMD",
|
||||
read_mark=self.action_planner.last_obs_time_mark,
|
||||
truncate=True,
|
||||
show_actions=True,
|
||||
)
|
||||
|
||||
prompt_info = await self.action_planner.build_planner_prompt(
|
||||
is_group_chat=is_group_chat,
|
||||
chat_target_info=chat_target_info,
|
||||
current_available_actions=available_actions,
|
||||
chat_content_block=chat_content_block,
|
||||
message_id_list=message_id_list,
|
||||
)
|
||||
continue_flag, modified_message = await events_manager.handle_mai_events(
|
||||
EventType.ON_PLAN, None, prompt_info[0], None, self.chat_stream.stream_id
|
||||
)
|
||||
if not continue_flag:
|
||||
return False
|
||||
if modified_message and modified_message._modify_flags.modify_llm_prompt:
|
||||
prompt_info = (modified_message.llm_prompt, prompt_info[1])
|
||||
|
||||
with Timer("规划器", cycle_timers):
|
||||
action_to_use_info = await self.action_planner.plan(
|
||||
loop_start_time=self.last_read_time,
|
||||
available_actions=available_actions,
|
||||
force_reply_message=force_reply_message,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} 决定执行{len(action_to_use_info)}个动作: {' '.join([a.action_type for a in action_to_use_info])}"
|
||||
)
|
||||
|
||||
# 3. 并行执行所有动作
|
||||
action_tasks = [
|
||||
asyncio.create_task(
|
||||
self._execute_action(action, action_to_use_info, thinking_id, available_actions, cycle_timers)
|
||||
)
|
||||
for action in action_to_use_info
|
||||
]
|
||||
|
||||
# 并行执行所有任务
|
||||
results = await asyncio.gather(*action_tasks, return_exceptions=True)
|
||||
|
||||
# 处理执行结果
|
||||
reply_loop_info = None
|
||||
reply_text_from_reply = ""
|
||||
action_success = False
|
||||
action_reply_text = ""
|
||||
|
||||
excute_result_str = ""
|
||||
for result in results:
|
||||
excute_result_str += f"{result['action_type']} 执行结果:{result['result']}\n"
|
||||
|
||||
if isinstance(result, BaseException):
|
||||
logger.error(f"{self.log_prefix} 动作执行异常: {result}")
|
||||
continue
|
||||
|
||||
if result["action_type"] != "reply":
|
||||
action_success = result["success"]
|
||||
action_reply_text = result["result"]
|
||||
elif result["action_type"] == "reply":
|
||||
if result["success"]:
|
||||
reply_loop_info = result["loop_info"]
|
||||
reply_text_from_reply = result["result"]
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} 回复动作执行失败")
|
||||
|
||||
self.action_planner.add_plan_excute_log(result=excute_result_str)
|
||||
|
||||
# 构建最终的循环信息
|
||||
if reply_loop_info:
|
||||
# 如果有回复信息，使用回复的loop_info作为基础
|
||||
loop_info = reply_loop_info
|
||||
# 更新动作执行信息
|
||||
loop_info["loop_action_info"].update(
|
||||
{
|
||||
"action_taken": action_success,
|
||||
"taken_time": time.time(),
|
||||
}
|
||||
)
|
||||
_reply_text = reply_text_from_reply
|
||||
else:
|
||||
# 没有回复信息，构建纯动作的loop_info
|
||||
loop_info = {
|
||||
"loop_plan_info": {
|
||||
"action_result": action_to_use_info,
|
||||
},
|
||||
"loop_action_info": {
|
||||
"action_taken": action_success,
|
||||
"reply_text": action_reply_text,
|
||||
"taken_time": time.time(),
|
||||
},
|
||||
}
|
||||
_reply_text = action_reply_text
|
||||
|
||||
self.end_cycle(loop_info, cycle_timers)
|
||||
self.print_cycle_info(cycle_timers)
|
||||
|
||||
end_time = time.time()
|
||||
if end_time - start_time < global_config.chat.planner_smooth:
|
||||
wait_time = global_config.chat.planner_smooth - (end_time - start_time)
|
||||
await asyncio.sleep(wait_time)
|
||||
else:
|
||||
await asyncio.sleep(0.1)
|
||||
return True
|
||||
|
||||
async def _main_chat_loop(self):
|
||||
"""主循环，持续进行计划并可能回复消息，直到被外部取消。"""
|
||||
try:
|
||||
while self.running:
|
||||
# 主循环
|
||||
success = await self._loopbody()
|
||||
await asyncio.sleep(0.1)
|
||||
if not success:
|
||||
break
|
||||
except asyncio.CancelledError:
|
||||
# 设置了关闭标志位后被取消是正常流程
|
||||
logger.info(f"{self.log_prefix} 麦麦已关闭聊天")
|
||||
except Exception:
|
||||
logger.error(f"{self.log_prefix} 麦麦聊天意外错误，将于3s后尝试重新启动")
|
||||
print(traceback.format_exc())
|
||||
await asyncio.sleep(3)
|
||||
self._loop_task = asyncio.create_task(self._main_chat_loop())
|
||||
logger.error(f"{self.log_prefix} 结束了当前聊天循环")
|
||||
|
||||
async def _handle_action(
|
||||
self,
|
||||
action: str,
|
||||
action_reasoning: str,
|
||||
action_data: dict,
|
||||
cycle_timers: Dict[str, float],
|
||||
thinking_id: str,
|
||||
action_message: Optional["DatabaseMessages"] = None,
|
||||
) -> tuple[bool, str, str]:
|
||||
"""
|
||||
处理规划动作，使用动作工厂创建相应的动作处理器
|
||||
|
||||
参数:
|
||||
action: 动作类型
|
||||
action_reasoning: 决策理由
|
||||
action_data: 动作数据，包含不同动作需要的参数
|
||||
cycle_timers: 计时器字典
|
||||
thinking_id: 思考ID
|
||||
action_message: 消息数据
|
||||
返回:
|
||||
tuple[bool, str, str]: (是否执行了动作, 思考消息ID, 命令)
|
||||
"""
|
||||
try:
|
||||
# 使用工厂创建动作处理器实例
|
||||
try:
|
||||
action_handler = self.action_manager.create_action(
|
||||
action_name=action,
|
||||
action_data=action_data,
|
||||
cycle_timers=cycle_timers,
|
||||
thinking_id=thinking_id,
|
||||
chat_stream=self.chat_stream,
|
||||
log_prefix=self.log_prefix,
|
||||
action_reasoning=action_reasoning,
|
||||
action_message=action_message,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 创建动作处理器时出错: {e}")
|
||||
traceback.print_exc()
|
||||
return False, ""
|
||||
|
||||
# 处理动作并获取结果（固定记录一次动作信息）
|
||||
result = await action_handler.execute()
|
||||
success, action_text = result
|
||||
|
||||
return success, action_text
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 处理{action}时出错: {e}")
|
||||
traceback.print_exc()
|
||||
return False, ""
|
||||
|
||||
async def _send_response(
|
||||
self,
|
||||
reply_set: "ReplySetModel",
|
||||
message_data: "DatabaseMessages",
|
||||
selected_expressions: Optional[List[int]] = None,
|
||||
quote_message: Optional[bool] = None,
|
||||
) -> str:
|
||||
# 根据 llm_quote 配置决定是否使用 quote_message 参数
|
||||
if global_config.chat.llm_quote:
|
||||
# 如果配置为 true，使用 llm_quote 参数决定是否引用回复
|
||||
if quote_message is None:
|
||||
logger.warning(f"{self.log_prefix} quote_message 参数为空，不引用")
|
||||
need_reply = False
|
||||
else:
|
||||
need_reply = quote_message
|
||||
if need_reply:
|
||||
logger.info(f"{self.log_prefix} LLM 决定使用引用回复")
|
||||
else:
|
||||
# 如果配置为 false，使用原来的模式
|
||||
new_message_count = message_api.count_new_messages(
|
||||
chat_id=self.chat_stream.stream_id, start_time=self.last_read_time, end_time=time.time()
|
||||
)
|
||||
need_reply = new_message_count >= random.randint(2, 3) or time.time() - self.last_read_time > 90
|
||||
if need_reply:
|
||||
logger.info(f"{self.log_prefix} 从思考到回复，共有{new_message_count}条新消息，使用引用回复，或者上次回复时间超过90秒")
|
||||
|
||||
reply_text = ""
|
||||
first_replied = False
|
||||
for reply_content in reply_set.reply_data:
|
||||
if reply_content.content_type != ReplyContentType.TEXT:
|
||||
continue
|
||||
data: str = reply_content.content # type: ignore
|
||||
if not first_replied:
|
||||
await send_api.text_to_stream(
|
||||
text=data,
|
||||
stream_id=self.chat_stream.stream_id,
|
||||
reply_message=message_data,
|
||||
set_reply=need_reply,
|
||||
typing=False,
|
||||
selected_expressions=selected_expressions,
|
||||
)
|
||||
first_replied = True
|
||||
else:
|
||||
await send_api.text_to_stream(
|
||||
text=data,
|
||||
stream_id=self.chat_stream.stream_id,
|
||||
reply_message=message_data,
|
||||
set_reply=False,
|
||||
typing=True,
|
||||
selected_expressions=selected_expressions,
|
||||
)
|
||||
reply_text += data
|
||||
|
||||
return reply_text
|
||||
|
||||
async def _execute_action(
|
||||
self,
|
||||
action_planner_info: ActionPlannerInfo,
|
||||
chosen_action_plan_infos: List[ActionPlannerInfo],
|
||||
thinking_id: str,
|
||||
available_actions: Dict[str, ActionInfo],
|
||||
cycle_timers: Dict[str, float],
|
||||
):
|
||||
"""执行单个动作的通用函数"""
|
||||
try:
|
||||
with Timer(f"动作{action_planner_info.action_type}", cycle_timers):
|
||||
# 直接当场执行no_reply逻辑
|
||||
if action_planner_info.action_type == "no_reply":
|
||||
# 直接处理no_reply逻辑，不再通过动作系统
|
||||
reason = action_planner_info.reasoning or "选择不回复"
|
||||
# logger.info(f"{self.log_prefix} 选择不回复，原因: {reason}")
|
||||
|
||||
# 增加连续 no_reply 计数
|
||||
self.consecutive_no_reply_count += 1
|
||||
|
||||
await database_api.store_action_info(
|
||||
chat_stream=self.chat_stream,
|
||||
action_build_into_prompt=False,
|
||||
action_prompt_display=reason,
|
||||
action_done=True,
|
||||
thinking_id=thinking_id,
|
||||
action_data={},
|
||||
action_name="no_reply",
|
||||
action_reasoning=reason,
|
||||
)
|
||||
|
||||
return {"action_type": "no_reply", "success": True, "result": "选择不回复", "command": ""}
|
||||
|
||||
elif action_planner_info.action_type == "reply":
|
||||
# 直接当场执行reply逻辑
|
||||
self.questioned = False
|
||||
# 刷新主动发言状态
|
||||
# 重置连续 no_reply 计数
|
||||
self.consecutive_no_reply_count = 0
|
||||
|
||||
reason = action_planner_info.reasoning or ""
|
||||
# 根据 think_mode 配置决定 think_level 的值
|
||||
think_mode = global_config.chat.think_mode
|
||||
if think_mode == "default":
|
||||
think_level = 0
|
||||
elif think_mode == "deep":
|
||||
think_level = 1
|
||||
elif think_mode == "dynamic":
|
||||
# dynamic 模式：从 planner 返回的 action_data 中获取
|
||||
think_level = action_planner_info.action_data.get("think_level", 1)
|
||||
else:
|
||||
# 默认使用 default 模式
|
||||
think_level = 0
|
||||
# 使用 action_reasoning（planner 的整体思考理由）作为 reply_reason
|
||||
planner_reasoning = action_planner_info.action_reasoning or reason
|
||||
|
||||
record_replyer_action_temp(
|
||||
chat_id=self.stream_id,
|
||||
reason=reason,
|
||||
think_level=think_level,
|
||||
)
|
||||
|
||||
await database_api.store_action_info(
|
||||
chat_stream=self.chat_stream,
|
||||
action_build_into_prompt=False,
|
||||
action_prompt_display=reason,
|
||||
action_done=True,
|
||||
thinking_id=thinking_id,
|
||||
action_data={},
|
||||
action_name="reply",
|
||||
action_reasoning=reason,
|
||||
)
|
||||
|
||||
# 从 Planner 的 action_data 中提取未知词语列表（仅在 reply 时使用）
|
||||
unknown_words = None
|
||||
quote_message = None
|
||||
if isinstance(action_planner_info.action_data, dict):
|
||||
uw = action_planner_info.action_data.get("unknown_words")
|
||||
if isinstance(uw, list):
|
||||
cleaned_uw: List[str] = []
|
||||
for item in uw:
|
||||
if isinstance(item, str):
|
||||
s = item.strip()
|
||||
if s:
|
||||
cleaned_uw.append(s)
|
||||
if cleaned_uw:
|
||||
unknown_words = cleaned_uw
|
||||
|
||||
# 从 Planner 的 action_data 中提取 quote_message 参数
|
||||
qm = action_planner_info.action_data.get("quote")
|
||||
if qm is not None:
|
||||
# 支持多种格式：true/false, "true"/"false", 1/0
|
||||
if isinstance(qm, bool):
|
||||
quote_message = qm
|
||||
elif isinstance(qm, str):
|
||||
quote_message = qm.lower() in ("true", "1", "yes")
|
||||
elif isinstance(qm, (int, float)):
|
||||
quote_message = bool(qm)
|
||||
|
||||
logger.info(f"{self.log_prefix} {qm}引用回复设置: {quote_message}")
|
||||
|
||||
success, llm_response = await generator_api.generate_reply(
|
||||
chat_stream=self.chat_stream,
|
||||
reply_message=action_planner_info.action_message,
|
||||
available_actions=available_actions,
|
||||
chosen_actions=chosen_action_plan_infos,
|
||||
reply_reason=planner_reasoning,
|
||||
unknown_words=unknown_words,
|
||||
enable_tool=global_config.tool.enable_tool,
|
||||
request_type="replyer",
|
||||
from_plugin=False,
|
||||
reply_time_point=action_planner_info.action_data.get("loop_start_time", time.time()),
|
||||
think_level=think_level,
|
||||
)
|
||||
|
||||
if not success or not llm_response or not llm_response.reply_set:
|
||||
if action_planner_info.action_message:
|
||||
logger.info(f"对 {action_planner_info.action_message.processed_plain_text} 的回复生成失败")
|
||||
else:
|
||||
logger.info("回复生成失败")
|
||||
return {"action_type": "reply", "success": False, "result": "回复生成失败", "loop_info": None}
|
||||
|
||||
response_set = llm_response.reply_set
|
||||
selected_expressions = llm_response.selected_expressions
|
||||
loop_info, reply_text, _ = await self._send_and_store_reply(
|
||||
response_set=response_set,
|
||||
action_message=action_planner_info.action_message, # type: ignore
|
||||
cycle_timers=cycle_timers,
|
||||
thinking_id=thinking_id,
|
||||
actions=chosen_action_plan_infos,
|
||||
selected_expressions=selected_expressions,
|
||||
quote_message=quote_message,
|
||||
)
|
||||
self.last_active_time = time.time()
|
||||
return {
|
||||
"action_type": "reply",
|
||||
"success": True,
|
||||
"result": f"你使用reply动作，对' {action_planner_info.action_message.processed_plain_text} '这句话进行了回复，回复内容为: '{reply_text}'",
|
||||
"loop_info": loop_info,
|
||||
}
|
||||
|
||||
else:
|
||||
# 执行普通动作
|
||||
with Timer("动作执行", cycle_timers):
|
||||
success, result = await self._handle_action(
|
||||
action=action_planner_info.action_type,
|
||||
action_reasoning=action_planner_info.action_reasoning or "",
|
||||
action_data=action_planner_info.action_data or {},
|
||||
cycle_timers=cycle_timers,
|
||||
thinking_id=thinking_id,
|
||||
action_message=action_planner_info.action_message,
|
||||
)
|
||||
|
||||
self.last_active_time = time.time()
|
||||
return {
|
||||
"action_type": action_planner_info.action_type,
|
||||
"success": success,
|
||||
"result": result,
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 执行动作时出错: {e}")
|
||||
logger.error(f"{self.log_prefix} 错误信息: {traceback.format_exc()}")
|
||||
return {
|
||||
"action_type": action_planner_info.action_type,
|
||||
"success": False,
|
||||
"result": "",
|
||||
"loop_info": None,
|
||||
"error": str(e),
|
||||
}
|
||||
@@ -1,377 +1,231 @@
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
|
||||
from rich.traceback import install
|
||||
from typing import List, Optional, TYPE_CHECKING
|
||||
|
||||
import asyncio
|
||||
import random
|
||||
import time
|
||||
import traceback
|
||||
|
||||
from rich.traceback import install
|
||||
|
||||
from src.bw_learner.expression_learner import ExpressionLearner
|
||||
from src.bw_learner.jargon_miner import JargonMiner
|
||||
from src.chat.event_helpers import build_event_message
|
||||
from src.chat.logger.plan_reply_logger import PlanReplyLogger
|
||||
from src.chat.message_receive.chat_manager import BotChatSession
|
||||
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
|
||||
from src.chat.planner_actions.action_manager import ActionManager
|
||||
from src.chat.planner_actions.action_modifier import ActionModifier
|
||||
from src.chat.planner_actions.planner import ActionPlanner
|
||||
from src.chat.utils.prompt_builder import global_prompt_manager
|
||||
from src.chat.utils.timer_calculator import Timer
|
||||
from src.chat.utils.utils import record_replyer_action_temp
|
||||
from src.common.data_models.info_data_model import ActionPlannerInfo
|
||||
from src.common.data_models.message_component_data_model import MessageSequence, TextComponent
|
||||
from src.chat.message_receive.chat_manager import chat_manager
|
||||
from src.common.logger import get_logger
|
||||
from src.common.utils.utils_config import ChatConfigUtils, ExpressionConfigUtils
|
||||
from src.config.config import global_config
|
||||
from src.config.file_watcher import FileChange
|
||||
from src.core.event_bus import event_bus
|
||||
from src.core.types import ActionInfo, EventType
|
||||
from src.person_info.person_info import Person
|
||||
from src.services import (
|
||||
database_service as database_api,
|
||||
generator_service as generator_api,
|
||||
message_service as message_api,
|
||||
send_service as send_api,
|
||||
)
|
||||
from src.services.message_service import build_readable_messages_with_id, get_messages_before_time_in_chat
|
||||
from src.learners.expression_learner import ExpressionLearner
|
||||
from src.learners.jargon_miner import JargonMiner
|
||||
|
||||
from .heartFC_utils import CycleDetail
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
|
||||
|
||||
install(extra_lines=5)
|
||||
|
||||
logger = get_logger("heartFC_chat")
|
||||
|
||||
|
||||
class HeartFChatting:
|
||||
"""管理一个持续运行的 Focus Chat 会话。"""
|
||||
"""
|
||||
管理一个连续的Focus Chat聊天会话
|
||||
用于在特定的聊天会话里面生成回复
|
||||
"""
|
||||
|
||||
def __init__(self, session_id: str):
|
||||
self.session_id = session_id
|
||||
self.chat_stream: BotChatSession = _chat_manager.get_session_by_session_id(self.session_id) # type: ignore[assignment]
|
||||
if not self.chat_stream:
|
||||
raise ValueError(f"无法找到聊天会话 {self.session_id}")
|
||||
"""
|
||||
初始化 HeartFChatting 实例
|
||||
|
||||
session_name = _chat_manager.get_session_name(session_id) or session_id
|
||||
Args:
|
||||
session_id: 聊天会话ID
|
||||
"""
|
||||
# 基础属性
|
||||
self.session_id = session_id
|
||||
session_name = chat_manager.get_session_name(session_id) or session_id
|
||||
self.log_prefix = f"[{session_name}]"
|
||||
self.session_name = session_name
|
||||
|
||||
self.action_manager = ActionManager()
|
||||
self.action_planner = ActionPlanner(chat_id=self.session_id, action_manager=self.action_manager)
|
||||
self.action_modifier = ActionModifier(action_manager=self.action_manager, chat_id=self.session_id)
|
||||
|
||||
# 系统运行状态
|
||||
self._running: bool = False
|
||||
self._loop_task: Optional[asyncio.Task] = None
|
||||
self._cycle_counter: int = 0
|
||||
self._hfc_lock: asyncio.Lock = asyncio.Lock() # 用于保护 _hfc_func 的并发访问
|
||||
# 聊天频率相关
|
||||
self._consecutive_no_reply_count = 0 # 跟踪连续 no_reply 次数，用于动态调整阈值
|
||||
self._talk_frequency_adjust: float = 1.0 # 发言频率修正值，默认为1.0，可以根据需要调整
|
||||
|
||||
# HFC内消息缓存
|
||||
self.message_cache: List[SessionMessage] = []
|
||||
|
||||
# Asyncio Event 用于控制循环的开始和结束
|
||||
self._cycle_event = asyncio.Event()
|
||||
self._hfc_lock = asyncio.Lock()
|
||||
|
||||
self._cycle_counter = 0
|
||||
self._current_cycle_detail: Optional[CycleDetail] = None
|
||||
self.history_loop: List[CycleDetail] = []
|
||||
|
||||
self.last_read_time = time.time() - 2
|
||||
self.last_active_time = time.time()
|
||||
self._talk_frequency_adjust = 1.0
|
||||
self._consecutive_no_reply_count = 0
|
||||
|
||||
self.message_cache: List["SessionMessage"] = []
|
||||
|
||||
self._min_messages_for_extraction = 30
|
||||
self._min_extraction_interval = 60
|
||||
self._last_extraction_time = 0.0
|
||||
|
||||
# 表达方式相关内容
|
||||
self._min_messages_for_extraction = 30 # 最少提取消息数
|
||||
self._min_extraction_interval = 60 # 最小提取时间间隔，单位为秒
|
||||
self._last_extraction_time: float = 0.0 # 上次提取的时间戳
|
||||
expr_use, jargon_learn, expr_learn = ExpressionConfigUtils.get_expression_config_for_chat(session_id)
|
||||
self._enable_expression_use = expr_use
|
||||
self._enable_expression_learning = expr_learn
|
||||
self._enable_jargon_learning = jargon_learn
|
||||
self._expression_learner = ExpressionLearner(session_id)
|
||||
self._jargon_miner = JargonMiner(session_id, session_name=session_name)
|
||||
self._enable_expression_use = expr_use # 允许使用表达方式，但不一定启用学习
|
||||
self._enable_expression_learning = expr_learn # 允许学习表达方式
|
||||
self._enable_jargon_learning = jargon_learn # 允许学习黑话
|
||||
# 表达学习器
|
||||
self._expression_learner: ExpressionLearner = ExpressionLearner(session_id)
|
||||
# 黑话挖掘器
|
||||
self._jargon_miner: JargonMiner = JargonMiner(session_id, session_name=session_name)
|
||||
|
||||
# TODO: ChatSummarizer 聊天总结器重构
|
||||
|
||||
# ====== 公开方法 ======
|
||||
|
||||
async def start(self):
|
||||
"""启动 HeartFChatting 的主循环"""
|
||||
# 先检查是否已经启动运行
|
||||
if self._running:
|
||||
logger.debug(f"{self.log_prefix} HeartFChatting 已在运行中")
|
||||
logger.debug(f"{self.log_prefix} 已经在运行中，无需重复启动")
|
||||
return
|
||||
|
||||
try:
|
||||
self._running = True
|
||||
self._cycle_event.clear()
|
||||
self._cycle_event.clear() # 确保事件初始状态为未设置
|
||||
|
||||
self._loop_task = asyncio.create_task(self.main_loop())
|
||||
self._loop_task.add_done_callback(self._handle_loop_completion)
|
||||
|
||||
logger.info(f"{self.log_prefix} HeartFChatting 启动完成")
|
||||
except Exception as exc:
|
||||
logger.error(f"{self.log_prefix} HeartFChatting 启动失败: {exc}", exc_info=True)
|
||||
self._running = False
|
||||
self._cycle_event.set()
|
||||
self._loop_task = None
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 启动 HeartFChatting 失败: {e}", exc_info=True)
|
||||
self._running = False # 确保状态正确
|
||||
self._cycle_event.set() # 确保事件被设置，避免死锁
|
||||
self._loop_task = None # 确保任务引用被清理
|
||||
raise
|
||||
|
||||
async def stop(self):
|
||||
"""停止 HeartFChatting 的主循环"""
|
||||
if not self._running:
|
||||
logger.debug(f"{self.log_prefix} HeartFChatting 已停止")
|
||||
logger.debug(f"{self.log_prefix} HeartFChatting 已经停止，无需重复停止")
|
||||
return
|
||||
|
||||
self._running = False
|
||||
self._cycle_event.set()
|
||||
self._cycle_event.set() # 触发事件，通知循环结束
|
||||
|
||||
if self._loop_task:
|
||||
self._loop_task.cancel()
|
||||
self._loop_task.cancel() # 取消主循环任务
|
||||
try:
|
||||
await self._loop_task
|
||||
await self._loop_task # 等待任务完成
|
||||
except asyncio.CancelledError:
|
||||
logger.info(f"{self.log_prefix} HeartFChatting 主循环已取消")
|
||||
except Exception as exc:
|
||||
logger.error(f"{self.log_prefix} 停止 HeartFChatting 时发生错误: {exc}", exc_info=True)
|
||||
logger.info(f"{self.log_prefix} HeartFChatting 主循环已成功取消")
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 停止 HeartFChatting 时发生错误: {e}", exc_info=True)
|
||||
finally:
|
||||
self._loop_task = None
|
||||
self._loop_task = None # 确保任务引用被清理
|
||||
|
||||
logger.info(f"{self.log_prefix} HeartFChatting 已停止")
|
||||
|
||||
def adjust_talk_frequency(self, new_value: float):
|
||||
"""调整发言频率的调整值
|
||||
|
||||
Args:
|
||||
new_value: 新的修正值，必须为非负数。值越大，修正发言频率越高；值越小，修正发言频率越低。
|
||||
"""
|
||||
self._talk_frequency_adjust = max(0.0, new_value)
|
||||
|
||||
async def register_message(self, message: "SessionMessage"):
|
||||
"""注册一条消息到 HeartFChatting 的缓存中，并检测其是否产生提及，决定是否唤醒聊天
|
||||
|
||||
Args:
|
||||
message: 待注册的消息对象
|
||||
"""
|
||||
self.message_cache.append(message)
|
||||
|
||||
# 先检查at必回复
|
||||
if global_config.chat.inevitable_at_reply and message.is_at:
|
||||
self.last_read_time = time.time()
|
||||
async with self._hfc_lock:
|
||||
await self._judge_and_response(mentioned_message=message, recent_messages_list=[message])
|
||||
return
|
||||
|
||||
async with self._hfc_lock: # 确保与主循环逻辑的互斥访问
|
||||
await self._judge_and_response(message)
|
||||
return # 直接返回，避免同一条消息被主循环再次处理
|
||||
# 再检查提及必回复
|
||||
if global_config.chat.mentioned_bot_reply and message.is_mentioned:
|
||||
self.last_read_time = time.time()
|
||||
async with self._hfc_lock:
|
||||
await self._judge_and_response(mentioned_message=message, recent_messages_list=[message])
|
||||
# 直接获取锁，确保一定一定触发回复逻辑，不受当前是否正在执行主循环的影响
|
||||
async with self._hfc_lock: # 确保与主循环逻辑的互斥访问
|
||||
await self._judge_and_response(message)
|
||||
return
|
||||
|
||||
async def main_loop(self):
|
||||
try:
|
||||
while self._running and not self._cycle_event.is_set():
|
||||
if not self._hfc_lock.locked():
|
||||
async with self._hfc_lock:
|
||||
async with self._hfc_lock: # 确保主循环逻辑的互斥访问
|
||||
await self._hfc_func()
|
||||
await asyncio.sleep(0.1)
|
||||
await asyncio.sleep(5)
|
||||
except asyncio.CancelledError:
|
||||
logger.info(f"{self.log_prefix} HeartFChatting: 主循环被取消")
|
||||
except Exception as exc:
|
||||
logger.error(f"{self.log_prefix} HeartFChatting: 主循环异常: {exc}", exc_info=True)
|
||||
await self.stop()
|
||||
logger.info(f"{self.log_prefix} HeartFChatting: 主循环被取消，正在关闭")
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 麦麦聊天意外错误: {e}，将于3s后尝试重新启动")
|
||||
await self.stop() # 确保状态正确
|
||||
await asyncio.sleep(3)
|
||||
await self.start()
|
||||
await self.start() # 尝试重新启动
|
||||
|
||||
async def _config_callback(self, file_change: Optional[FileChange] = None):
|
||||
del file_change
|
||||
expr_use, jargon_learn, expr_learn = ExpressionConfigUtils.get_expression_config_for_chat(self.session_id)
|
||||
self._enable_expression_use = expr_use
|
||||
self._enable_expression_learning = expr_learn
|
||||
self._enable_jargon_learning = jargon_learn
|
||||
"""配置文件变更回调函数"""
|
||||
# TODO: 根据配置文件变动重新计算相关参数：
|
||||
"""
|
||||
需要计算的参数：
|
||||
self._enable_expression_use = expr_use # 允许使用表达方式，但不一定启用学习
|
||||
self._enable_expression_learning = expr_learn # 允许学习表达方式
|
||||
self._enable_jargon_learning = jargon_learn # 允许学习黑话
|
||||
"""
|
||||
|
||||
async def _hfc_func(self):
|
||||
recent_messages_list = message_api.get_messages_by_time_in_chat(
|
||||
chat_id=self.session_id,
|
||||
start_time=self.last_read_time,
|
||||
end_time=time.time(),
|
||||
limit=20,
|
||||
limit_mode="latest",
|
||||
filter_mai=True,
|
||||
filter_command=False,
|
||||
filter_intercept_message_level=1,
|
||||
)
|
||||
# ====== 心流聊天核心逻辑 ======
|
||||
async def _hfc_func(self, mentioned_message: Optional["SessionMessage"] = None):
|
||||
"""心流聊天的主循环逻辑"""
|
||||
if self._consecutive_no_reply_count >= 5:
|
||||
threshold = 2
|
||||
elif self._consecutive_no_reply_count >= 3:
|
||||
threshold = 2 if random.random() < 0.5 else 1
|
||||
else:
|
||||
threshold = 1
|
||||
|
||||
if len(recent_messages_list) < 1:
|
||||
if len(self.message_cache) < threshold:
|
||||
await asyncio.sleep(0.2)
|
||||
return True
|
||||
|
||||
self.last_read_time = time.time()
|
||||
|
||||
mentioned_message: Optional["SessionMessage"] = None
|
||||
for message in recent_messages_list:
|
||||
if global_config.chat.inevitable_at_reply and message.is_at:
|
||||
mentioned_message = message
|
||||
elif global_config.chat.mentioned_bot_reply and message.is_mentioned:
|
||||
mentioned_message = message
|
||||
|
||||
talk_value = ChatConfigUtils.get_talk_value(self.session_id) * self._talk_frequency_adjust
|
||||
if mentioned_message:
|
||||
await self._judge_and_response(mentioned_message=mentioned_message, recent_messages_list=recent_messages_list)
|
||||
elif random.random() < talk_value:
|
||||
await self._judge_and_response(recent_messages_list=recent_messages_list)
|
||||
talk_value_threshold = (
|
||||
random.random() * ChatConfigUtils.get_talk_value(self.session_id) * self._talk_frequency_adjust
|
||||
)
|
||||
if mentioned_message and global_config.chat.mentioned_bot_reply:
|
||||
await self._judge_and_response(mentioned_message)
|
||||
elif random.random() < talk_value_threshold:
|
||||
await self._judge_and_response()
|
||||
return True
|
||||
|
||||
async def _judge_and_response(
|
||||
self,
|
||||
mentioned_message: Optional["SessionMessage"] = None,
|
||||
recent_messages_list: Optional[List["SessionMessage"]] = None,
|
||||
):
|
||||
recent_messages = list(recent_messages_list or self.message_cache[-20:])
|
||||
if recent_messages:
|
||||
asyncio.create_task(self._trigger_expression_learning(recent_messages))
|
||||
|
||||
cycle_timers, thinking_id = self._start_cycle()
|
||||
async def _judge_and_response(self, mentioned_message: Optional["SessionMessage"] = None):
|
||||
"""判定和生成回复"""
|
||||
asyncio.create_task(self._trigger_expression_learning(self.message_cache))
|
||||
# TODO: 完成反思器之后的逻辑
|
||||
start_time = time.time()
|
||||
current_cycle_detail = self._start_cycle()
|
||||
logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考")
|
||||
|
||||
try:
|
||||
async with global_prompt_manager.async_message_scope(self._get_template_name()):
|
||||
available_actions: Dict[str, ActionInfo] = {}
|
||||
try:
|
||||
await self.action_modifier.modify_actions()
|
||||
available_actions = self.action_manager.get_using_actions()
|
||||
except Exception as exc:
|
||||
logger.error(f"{self.log_prefix} 动作修改失败: {exc}", exc_info=True)
|
||||
# TODO: 动作检查逻辑
|
||||
# TODO: Planner逻辑
|
||||
# TODO: 动作执行逻辑
|
||||
|
||||
is_group_chat, chat_target_info, _ = self.action_planner.get_necessary_info()
|
||||
message_list_before_now = get_messages_before_time_in_chat(
|
||||
chat_id=self.session_id,
|
||||
timestamp=time.time(),
|
||||
limit=int(global_config.chat.max_context_size * 0.6),
|
||||
filter_intercept_message_level=1,
|
||||
)
|
||||
chat_content_block, message_id_list = build_readable_messages_with_id(
|
||||
messages=message_list_before_now,
|
||||
timestamp_mode="normal_no_YMD",
|
||||
read_mark=self.action_planner.last_obs_time_mark,
|
||||
truncate=True,
|
||||
show_actions=True,
|
||||
)
|
||||
|
||||
prompt, filtered_actions = await self._build_planner_prompt_with_event(
|
||||
available_actions=available_actions,
|
||||
is_group_chat=is_group_chat,
|
||||
chat_target_info=chat_target_info,
|
||||
chat_content_block=chat_content_block,
|
||||
message_id_list=message_id_list,
|
||||
)
|
||||
if prompt is None:
|
||||
return False
|
||||
|
||||
with Timer("规划器", cycle_timers):
|
||||
reasoning, action_to_use_info, llm_raw_output, llm_reasoning, llm_duration_ms = (
|
||||
await self.action_planner._execute_main_planner(
|
||||
prompt=prompt,
|
||||
message_id_list=message_id_list,
|
||||
filtered_actions=filtered_actions,
|
||||
available_actions=available_actions,
|
||||
loop_start_time=self.last_read_time,
|
||||
)
|
||||
)
|
||||
|
||||
action_to_use_info = self._ensure_force_reply_action(
|
||||
actions=action_to_use_info,
|
||||
force_reply_message=mentioned_message,
|
||||
available_actions=available_actions,
|
||||
)
|
||||
self.action_planner.add_plan_log(reasoning, action_to_use_info)
|
||||
self.action_planner.last_obs_time_mark = time.time()
|
||||
self._log_plan(
|
||||
prompt=prompt,
|
||||
reasoning=reasoning,
|
||||
llm_raw_output=llm_raw_output,
|
||||
llm_reasoning=llm_reasoning,
|
||||
llm_duration_ms=llm_duration_ms,
|
||||
actions=action_to_use_info,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"{self.log_prefix} 决定执行{len(action_to_use_info)}个动作: {' '.join([a.action_type for a in action_to_use_info])}"
|
||||
)
|
||||
|
||||
action_tasks = [
|
||||
asyncio.create_task(
|
||||
self._execute_action(
|
||||
action,
|
||||
action_to_use_info,
|
||||
thinking_id,
|
||||
available_actions,
|
||||
cycle_timers,
|
||||
)
|
||||
)
|
||||
for action in action_to_use_info
|
||||
]
|
||||
results = await asyncio.gather(*action_tasks, return_exceptions=True)
|
||||
|
||||
reply_loop_info = None
|
||||
reply_text_from_reply = ""
|
||||
action_success = False
|
||||
action_reply_text = ""
|
||||
execute_result_str = ""
|
||||
|
||||
for result in results:
|
||||
if isinstance(result, BaseException):
|
||||
logger.error(f"{self.log_prefix} 动作执行异常: {result}", exc_info=True)
|
||||
continue
|
||||
|
||||
execute_result_str += f"{result['action_type']} 执行结果:{result['result']}\n"
|
||||
if result["action_type"] == "reply":
|
||||
if result["success"]:
|
||||
reply_loop_info = result["loop_info"]
|
||||
reply_text_from_reply = result["result"]
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} reply 动作执行失败")
|
||||
else:
|
||||
action_success = result["success"]
|
||||
action_reply_text = result["result"]
|
||||
|
||||
self.action_planner.add_plan_excute_log(result=execute_result_str)
|
||||
|
||||
if reply_loop_info:
|
||||
loop_info = reply_loop_info
|
||||
loop_info["loop_action_info"].update(
|
||||
{
|
||||
"action_taken": action_success,
|
||||
"taken_time": time.time(),
|
||||
}
|
||||
)
|
||||
else:
|
||||
loop_info = {
|
||||
"loop_plan_info": {
|
||||
"action_result": action_to_use_info,
|
||||
},
|
||||
"loop_action_info": {
|
||||
"action_taken": action_success,
|
||||
"reply_text": action_reply_text,
|
||||
"taken_time": time.time(),
|
||||
},
|
||||
}
|
||||
reply_text_from_reply = action_reply_text
|
||||
|
||||
current_cycle_detail = self._end_cycle(self._current_cycle_detail, loop_info)
|
||||
logger.debug(f"{self.log_prefix} 本轮最终输出: {reply_text_from_reply}")
|
||||
return current_cycle_detail is not None
|
||||
except Exception as exc:
|
||||
logger.error(f"{self.log_prefix} 判定与回复流程失败: {exc}", exc_info=True)
|
||||
if self._current_cycle_detail:
|
||||
self._end_cycle(
|
||||
self._current_cycle_detail,
|
||||
{
|
||||
"loop_plan_info": {"action_result": []},
|
||||
"loop_action_info": {
|
||||
"action_taken": False,
|
||||
"reply_text": "",
|
||||
"taken_time": time.time(),
|
||||
"error": str(exc),
|
||||
},
|
||||
},
|
||||
)
|
||||
return False
|
||||
cycle_detail = self._end_cycle(current_cycle_detail)
|
||||
if wait_time := global_config.chat.planner_smooth - (time.time() - start_time) > 0:
|
||||
await asyncio.sleep(wait_time)
|
||||
else:
|
||||
await asyncio.sleep(0.1) # 最小等待时间，避免过快循环
|
||||
return True
|
||||
|
||||
def _handle_loop_completion(self, task: asyncio.Task):
|
||||
"""当 _hfc_func 任务完成时执行的回调。"""
|
||||
try:
|
||||
if exception := task.exception():
|
||||
logger.error(f"{self.log_prefix} HeartFChatting: 主循环异常退出: {exception}")
|
||||
logger.error(traceback.format_exc())
|
||||
logger.error(f"{self.log_prefix} HeartFChatting: 脱离了聊天(异常): {exception}")
|
||||
logger.error(traceback.format_exc()) # Log full traceback for exceptions
|
||||
else:
|
||||
logger.info(f"{self.log_prefix} HeartFChatting: 主循环已退出")
|
||||
logger.info(f"{self.log_prefix} HeartFChatting: 脱离了聊天 (外部停止)")
|
||||
except asyncio.CancelledError:
|
||||
logger.info(f"{self.log_prefix} HeartFChatting: 聊天已结束")
|
||||
logger.info(f"{self.log_prefix} HeartFChatting: 结束了聊天")
|
||||
|
||||
# ====== 学习器触发逻辑 ======
|
||||
async def _trigger_expression_learning(self, messages: List["SessionMessage"]):
|
||||
if not messages:
|
||||
return
|
||||
|
||||
self._expression_learner.add_messages(messages)
|
||||
if time.time() - self._last_extraction_time < self._min_extraction_interval:
|
||||
return
|
||||
@@ -379,14 +233,12 @@ class HeartFChatting:
|
||||
return
|
||||
if not self._enable_expression_learning:
|
||||
return
|
||||
|
||||
extraction_end_time = time.time()
|
||||
logger.info(
|
||||
f"聊天流 {self.session_name} 提取到 {len(messages)} 条消息，"
|
||||
f"时间窗口: {self._last_extraction_time:.2f} - {extraction_end_time:.2f}"
|
||||
)
|
||||
self._last_extraction_time = extraction_end_time
|
||||
|
||||
try:
|
||||
jargon_miner = self._jargon_miner if self._enable_jargon_learning else None
|
||||
learnt_style = await self._expression_learner.learn(jargon_miner)
|
||||
@@ -394,398 +246,43 @@ class HeartFChatting:
|
||||
logger.info(f"{self.log_prefix} 表达学习完成")
|
||||
else:
|
||||
logger.debug(f"{self.log_prefix} 表达学习未获得有效结果")
|
||||
except Exception as exc:
|
||||
logger.error(f"{self.log_prefix} 表达学习失败: {exc}", exc_info=True)
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 表达学习失败: {e}", exc_info=True)
|
||||
|
||||
def _start_cycle(self) -> Tuple[Dict[str, float], str]:
|
||||
# ====== 记录循环执行信息相关逻辑 ======
|
||||
def _start_cycle(self) -> CycleDetail:
|
||||
self._cycle_counter += 1
|
||||
self._current_cycle_detail = CycleDetail(cycle_id=self._cycle_counter)
|
||||
self._current_cycle_detail.thinking_id = f"tid{str(round(time.time(), 2))}"
|
||||
return self._current_cycle_detail.time_records, self._current_cycle_detail.thinking_id
|
||||
current_cycle_detail = CycleDetail(cycle_id=self._cycle_counter)
|
||||
current_cycle_detail.thinking_id = f"tid{str(round(time.time(), 2))}"
|
||||
return current_cycle_detail
|
||||
|
||||
def _end_cycle(self, cycle_detail: Optional[CycleDetail], loop_info: Optional[Dict[str, Any]] = None):
|
||||
if cycle_detail is None:
|
||||
return None
|
||||
|
||||
cycle_detail.loop_plan_info = (loop_info or {}).get("loop_plan_info")
|
||||
cycle_detail.loop_action_info = (loop_info or {}).get("loop_action_info")
|
||||
def _end_cycle(self, cycle_detail: CycleDetail, only_long_execution: bool = True):
|
||||
cycle_detail.end_time = time.time()
|
||||
self.history_loop.append(cycle_detail)
|
||||
|
||||
timer_strings = [
|
||||
timer_strings: List[str] = [
|
||||
f"{name}: {duration:.2f}s"
|
||||
for name, duration in cycle_detail.time_records.items()
|
||||
if duration >= 0.1
|
||||
if not only_long_execution or duration >= 0.1
|
||||
]
|
||||
logger.info(
|
||||
f"{self.log_prefix} 第{cycle_detail.cycle_id} 个心流循环完成，"
|
||||
f"耗时: {cycle_detail.end_time - cycle_detail.start_time:.2f}s；"
|
||||
f"{self.log_prefix} 第 {cycle_detail.cycle_id} 个心流循环完成"
|
||||
f"耗时: {cycle_detail.end_time - cycle_detail.start_time:.2f}秒\n"
|
||||
f"详细计时: {', '.join(timer_strings) if timer_strings else '无'}"
|
||||
)
|
||||
|
||||
return cycle_detail
|
||||
|
||||
async def _execute_action(
|
||||
self,
|
||||
action_planner_info: ActionPlannerInfo,
|
||||
chosen_action_plan_infos: List[ActionPlannerInfo],
|
||||
thinking_id: str,
|
||||
available_actions: Dict[str, ActionInfo],
|
||||
cycle_timers: Dict[str, float],
|
||||
):
|
||||
try:
|
||||
with Timer(f"动作{action_planner_info.action_type}", cycle_timers):
|
||||
if action_planner_info.action_type == "no_reply":
|
||||
reason = action_planner_info.reasoning or "选择不回复"
|
||||
self._consecutive_no_reply_count += 1
|
||||
await database_api.store_action_info(
|
||||
chat_stream=self.chat_stream,
|
||||
display_prompt=reason,
|
||||
thinking_id=thinking_id,
|
||||
action_data={},
|
||||
action_name="no_reply",
|
||||
action_reasoning=reason,
|
||||
)
|
||||
return {
|
||||
"action_type": "no_reply",
|
||||
"success": True,
|
||||
"result": "选择不回复",
|
||||
"loop_info": None,
|
||||
}
|
||||
# ====== Action相关逻辑 ======
|
||||
async def _execute_action(self, *args, **kwargs):
|
||||
"""原ExecuteAction"""
|
||||
raise NotImplementedError("执行动作的逻辑尚未实现") # TODO: 实现动作执行的逻辑，替换掉*args, **kwargs*占位符
|
||||
|
||||
if action_planner_info.action_type == "reply":
|
||||
self._consecutive_no_reply_count = 0
|
||||
reason = action_planner_info.reasoning or ""
|
||||
think_level = self._get_think_level(action_planner_info)
|
||||
planner_reasoning = action_planner_info.action_reasoning or reason
|
||||
async def _execute_other_actions(self, *args, **kwargs):
|
||||
"""原HandleAction"""
|
||||
raise NotImplementedError(
|
||||
"执行其他动作的逻辑尚未实现"
|
||||
) # TODO: 实现其他动作执行的逻辑, 替换掉*args, **kwargs*占位符
|
||||
|
||||
record_replyer_action_temp(
|
||||
chat_id=self.session_id,
|
||||
reason=reason,
|
||||
think_level=think_level,
|
||||
)
|
||||
await database_api.store_action_info(
|
||||
chat_stream=self.chat_stream,
|
||||
display_prompt=reason,
|
||||
thinking_id=thinking_id,
|
||||
action_data={},
|
||||
action_name="reply",
|
||||
action_reasoning=reason,
|
||||
)
|
||||
|
||||
unknown_words, quote_message = self._extract_reply_metadata(action_planner_info)
|
||||
success, llm_response = await generator_api.generate_reply(
|
||||
chat_stream=self.chat_stream,
|
||||
reply_message=action_planner_info.action_message,
|
||||
available_actions=available_actions,
|
||||
chosen_actions=chosen_action_plan_infos,
|
||||
reply_reason=planner_reasoning,
|
||||
unknown_words=unknown_words,
|
||||
enable_tool=global_config.tool.enable_tool,
|
||||
request_type="replyer",
|
||||
from_plugin=False,
|
||||
reply_time_point=action_planner_info.action_data.get("loop_start_time", time.time())
|
||||
if action_planner_info.action_data
|
||||
else time.time(),
|
||||
think_level=think_level,
|
||||
)
|
||||
if not success or not llm_response or not llm_response.reply_set:
|
||||
if action_planner_info.action_message:
|
||||
logger.info(
|
||||
f"对 {action_planner_info.action_message.processed_plain_text} 的回复生成失败"
|
||||
)
|
||||
else:
|
||||
logger.info(f"{self.log_prefix} 回复生成失败")
|
||||
return {
|
||||
"action_type": "reply",
|
||||
"success": False,
|
||||
"result": "回复生成失败",
|
||||
"loop_info": None,
|
||||
}
|
||||
|
||||
loop_info, reply_text, _ = await self._send_and_store_reply(
|
||||
response_set=llm_response.reply_set,
|
||||
action_message=action_planner_info.action_message, # type: ignore[arg-type]
|
||||
cycle_timers=cycle_timers,
|
||||
thinking_id=thinking_id,
|
||||
actions=chosen_action_plan_infos,
|
||||
selected_expressions=llm_response.selected_expressions,
|
||||
quote_message=quote_message,
|
||||
)
|
||||
self.last_active_time = time.time()
|
||||
return {
|
||||
"action_type": "reply",
|
||||
"success": True,
|
||||
"result": reply_text,
|
||||
"loop_info": loop_info,
|
||||
}
|
||||
|
||||
with Timer("动作执行", cycle_timers):
|
||||
success, result = await self._handle_action(
|
||||
action=action_planner_info.action_type,
|
||||
action_reasoning=action_planner_info.action_reasoning or "",
|
||||
action_data=action_planner_info.action_data or {},
|
||||
cycle_timers=cycle_timers,
|
||||
thinking_id=thinking_id,
|
||||
action_message=action_planner_info.action_message,
|
||||
)
|
||||
if success:
|
||||
self.last_active_time = time.time()
|
||||
return {
|
||||
"action_type": action_planner_info.action_type,
|
||||
"success": success,
|
||||
"result": result,
|
||||
"loop_info": None,
|
||||
}
|
||||
except Exception as exc:
|
||||
logger.error(f"{self.log_prefix} 执行动作时出错: {exc}", exc_info=True)
|
||||
return {
|
||||
"action_type": action_planner_info.action_type,
|
||||
"success": False,
|
||||
"result": "",
|
||||
"loop_info": None,
|
||||
"error": str(exc),
|
||||
}
|
||||
|
||||
async def _handle_action(
|
||||
self,
|
||||
action: str,
|
||||
action_reasoning: str,
|
||||
action_data: dict,
|
||||
cycle_timers: Dict[str, float],
|
||||
thinking_id: str,
|
||||
action_message: Optional["SessionMessage"] = None,
|
||||
) -> Tuple[bool, str]:
|
||||
try:
|
||||
action_handler = self.action_manager.create_action(
|
||||
action_name=action,
|
||||
action_data=action_data,
|
||||
action_reasoning=action_reasoning,
|
||||
cycle_timers=cycle_timers,
|
||||
thinking_id=thinking_id,
|
||||
chat_stream=self.chat_stream,
|
||||
log_prefix=self.log_prefix,
|
||||
action_message=action_message,
|
||||
)
|
||||
if not action_handler:
|
||||
logger.warning(f"{self.log_prefix} 未能创建动作处理器: {action}")
|
||||
return False, ""
|
||||
|
||||
success, action_text = await action_handler.execute()
|
||||
return success, action_text
|
||||
except Exception as exc:
|
||||
logger.error(f"{self.log_prefix} 处理动作 {action} 时出错: {exc}", exc_info=True)
|
||||
return False, ""
|
||||
|
||||
async def _send_and_store_reply(
|
||||
self,
|
||||
response_set: MessageSequence,
|
||||
action_message: "SessionMessage",
|
||||
cycle_timers: Dict[str, float],
|
||||
thinking_id: str,
|
||||
actions: List[ActionPlannerInfo],
|
||||
selected_expressions: Optional[List[int]] = None,
|
||||
quote_message: Optional[bool] = None,
|
||||
) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
|
||||
with Timer("回复发送", cycle_timers):
|
||||
reply_text = await self._send_response(
|
||||
reply_set=response_set,
|
||||
message_data=action_message,
|
||||
selected_expressions=selected_expressions,
|
||||
quote_message=quote_message,
|
||||
)
|
||||
|
||||
platform = action_message.platform or getattr(self.chat_stream, "platform", "unknown")
|
||||
person = Person(platform=platform, user_id=action_message.message_info.user_info.user_id)
|
||||
action_prompt_display = f"你对{person.person_name}进行了回复：{reply_text}"
|
||||
await database_api.store_action_info(
|
||||
chat_stream=self.chat_stream,
|
||||
display_prompt=action_prompt_display,
|
||||
thinking_id=thinking_id,
|
||||
action_data={"reply_text": reply_text},
|
||||
action_name="reply",
|
||||
)
|
||||
|
||||
loop_info: Dict[str, Any] = {
|
||||
"loop_plan_info": {
|
||||
"action_result": actions,
|
||||
},
|
||||
"loop_action_info": {
|
||||
"action_taken": True,
|
||||
"reply_text": reply_text,
|
||||
"command": "",
|
||||
"taken_time": time.time(),
|
||||
},
|
||||
}
|
||||
return loop_info, reply_text, cycle_timers
|
||||
|
||||
async def _send_response(
|
||||
self,
|
||||
reply_set: MessageSequence,
|
||||
message_data: "SessionMessage",
|
||||
selected_expressions: Optional[List[int]] = None,
|
||||
quote_message: Optional[bool] = None,
|
||||
) -> str:
|
||||
if global_config.chat.llm_quote:
|
||||
need_reply = bool(quote_message)
|
||||
else:
|
||||
new_message_count = message_api.count_new_messages(
|
||||
chat_id=self.session_id,
|
||||
start_time=self.last_read_time,
|
||||
end_time=time.time(),
|
||||
)
|
||||
need_reply = new_message_count >= random.randint(2, 3) or time.time() - self.last_read_time > 90
|
||||
|
||||
reply_text = ""
|
||||
first_replied = False
|
||||
for component in reply_set.components:
|
||||
if not isinstance(component, TextComponent):
|
||||
continue
|
||||
data = component.text
|
||||
if not first_replied:
|
||||
await send_api.text_to_stream(
|
||||
text=data,
|
||||
stream_id=self.session_id,
|
||||
reply_message=message_data,
|
||||
set_reply=need_reply,
|
||||
typing=False,
|
||||
selected_expressions=selected_expressions,
|
||||
)
|
||||
first_replied = True
|
||||
else:
|
||||
await send_api.text_to_stream(
|
||||
text=data,
|
||||
stream_id=self.session_id,
|
||||
reply_message=message_data,
|
||||
set_reply=False,
|
||||
typing=True,
|
||||
selected_expressions=selected_expressions,
|
||||
)
|
||||
reply_text += data
|
||||
return reply_text
|
||||
|
||||
async def _build_planner_prompt_with_event(
|
||||
self,
|
||||
available_actions: Dict[str, ActionInfo],
|
||||
is_group_chat: bool,
|
||||
chat_target_info: Any,
|
||||
chat_content_block: str,
|
||||
message_id_list: List[Tuple[str, "SessionMessage"]],
|
||||
) -> Tuple[Optional[str], Dict[str, ActionInfo]]:
|
||||
filtered_actions = self.action_planner._filter_actions_by_activation_type(available_actions, chat_content_block)
|
||||
prompt, _ = await self.action_planner.build_planner_prompt(
|
||||
is_group_chat=is_group_chat,
|
||||
chat_target_info=chat_target_info,
|
||||
current_available_actions=filtered_actions,
|
||||
chat_content_block=chat_content_block,
|
||||
message_id_list=message_id_list,
|
||||
)
|
||||
event_message = build_event_message(EventType.ON_PLAN, llm_prompt=prompt, stream_id=self.session_id)
|
||||
continue_flag, modified_message = await event_bus.emit(EventType.ON_PLAN, event_message)
|
||||
if not continue_flag:
|
||||
logger.info(f"{self.log_prefix} ON_PLAN 事件中止了本轮 HFC")
|
||||
return None, filtered_actions
|
||||
if modified_message and modified_message._modify_flags.modify_llm_prompt and modified_message.llm_prompt:
|
||||
prompt = modified_message.llm_prompt
|
||||
return prompt, filtered_actions
|
||||
|
||||
def _ensure_force_reply_action(
|
||||
self,
|
||||
actions: List[ActionPlannerInfo],
|
||||
force_reply_message: Optional["SessionMessage"],
|
||||
available_actions: Dict[str, ActionInfo],
|
||||
) -> List[ActionPlannerInfo]:
|
||||
if not force_reply_message:
|
||||
return actions
|
||||
|
||||
has_reply_to_force_message = any(
|
||||
action.action_type == "reply"
|
||||
and action.action_message
|
||||
and action.action_message.message_id == force_reply_message.message_id
|
||||
for action in actions
|
||||
)
|
||||
if has_reply_to_force_message:
|
||||
return actions
|
||||
|
||||
actions = [action for action in actions if action.action_type != "no_reply"]
|
||||
actions.insert(
|
||||
0,
|
||||
ActionPlannerInfo(
|
||||
action_type="reply",
|
||||
reasoning="用户提及了我，必须回复该消息",
|
||||
action_data={"loop_start_time": self.last_read_time},
|
||||
action_message=force_reply_message,
|
||||
available_actions=available_actions,
|
||||
action_reasoning=None,
|
||||
),
|
||||
)
|
||||
logger.info(f"{self.log_prefix} 检测到强制回复消息，已补充 reply 动作")
|
||||
return actions
|
||||
|
||||
def _log_plan(
|
||||
self,
|
||||
prompt: str,
|
||||
reasoning: str,
|
||||
llm_raw_output: Optional[str],
|
||||
llm_reasoning: Optional[str],
|
||||
llm_duration_ms: Optional[float],
|
||||
actions: List[ActionPlannerInfo],
|
||||
) -> None:
|
||||
try:
|
||||
PlanReplyLogger.log_plan(
|
||||
chat_id=self.session_id,
|
||||
prompt=prompt,
|
||||
reasoning=reasoning,
|
||||
raw_output=llm_raw_output,
|
||||
raw_reasoning=llm_reasoning,
|
||||
actions=actions,
|
||||
timing={
|
||||
"llm_duration_ms": round(llm_duration_ms, 2) if llm_duration_ms is not None else None,
|
||||
"loop_start_time": self.last_read_time,
|
||||
},
|
||||
extra=None,
|
||||
)
|
||||
except Exception:
|
||||
logger.exception(f"{self.log_prefix} 记录 plan 日志失败")
|
||||
|
||||
def _extract_reply_metadata(
|
||||
self,
|
||||
action_planner_info: ActionPlannerInfo,
|
||||
) -> Tuple[Optional[List[str]], Optional[bool]]:
|
||||
unknown_words: Optional[List[str]] = None
|
||||
quote_message: Optional[bool] = None
|
||||
action_data = action_planner_info.action_data or {}
|
||||
|
||||
raw_unknown_words = action_data.get("unknown_words")
|
||||
if isinstance(raw_unknown_words, list):
|
||||
cleaned_unknown_words = []
|
||||
for item in raw_unknown_words:
|
||||
if isinstance(item, str) and (cleaned_item := item.strip()):
|
||||
cleaned_unknown_words.append(cleaned_item)
|
||||
if cleaned_unknown_words:
|
||||
unknown_words = cleaned_unknown_words
|
||||
|
||||
raw_quote = action_data.get("quote")
|
||||
if isinstance(raw_quote, bool):
|
||||
quote_message = raw_quote
|
||||
elif isinstance(raw_quote, str):
|
||||
quote_message = raw_quote.lower() in {"true", "1", "yes"}
|
||||
elif isinstance(raw_quote, (int, float)):
|
||||
quote_message = bool(raw_quote)
|
||||
|
||||
return unknown_words, quote_message
|
||||
|
||||
def _get_think_level(self, action_planner_info: ActionPlannerInfo) -> int:
|
||||
think_mode = global_config.chat.think_mode
|
||||
if think_mode == "default":
|
||||
return 0
|
||||
if think_mode == "deep":
|
||||
return 1
|
||||
if think_mode == "dynamic":
|
||||
action_data = action_planner_info.action_data or {}
|
||||
return int(action_data.get("think_level", 1))
|
||||
return 0
|
||||
|
||||
def _get_template_name(self) -> Optional[str]:
|
||||
if self.chat_stream.context:
|
||||
return self.chat_stream.context.template_name
|
||||
return None
|
||||
# ====== 响应发送相关方法 ======
|
||||
async def _send_response(self, *args, **kwargs):
|
||||
raise NotImplementedError("发送回复的逻辑尚未实现") # TODO: 实现发送回复的逻辑，替换掉*args, **kwargs*占位符
|
||||
# 传入的消息至少应该是个MessageSequence实例，最好是SessionMessage实例，随后可直接转化为MessageSending实例
|
||||
|
||||
@@ -1,42 +0,0 @@
|
||||
import traceback
|
||||
from typing import Any, Optional, Dict
|
||||
|
||||
from src.chat.message_receive.chat_stream import get_chat_manager
|
||||
from src.common.logger import get_logger
|
||||
from src.chat.heart_flow.heartFC_chat import HeartFChatting
|
||||
from src.chat.brain_chat.brain_chat import BrainChatting
|
||||
from src.chat.message_receive.chat_stream import ChatStream
|
||||
|
||||
logger = get_logger("heartflow")
|
||||
|
||||
|
||||
class Heartflow:
|
||||
"""主心流协调器，负责初始化并协调聊天"""
|
||||
|
||||
def __init__(self):
|
||||
self.heartflow_chat_list: Dict[Any, HeartFChatting | BrainChatting] = {}
|
||||
|
||||
async def get_or_create_heartflow_chat(self, chat_id: Any) -> Optional[HeartFChatting | BrainChatting]:
|
||||
"""获取或创建一个新的HeartFChatting实例"""
|
||||
try:
|
||||
if chat_id in self.heartflow_chat_list:
|
||||
if chat := self.heartflow_chat_list.get(chat_id):
|
||||
return chat
|
||||
else:
|
||||
chat_stream: ChatStream | None = get_chat_manager().get_stream(chat_id)
|
||||
if not chat_stream:
|
||||
raise ValueError(f"未找到 chat_id={chat_id} 的聊天流")
|
||||
if chat_stream.group_info:
|
||||
new_chat = HeartFChatting(chat_id=chat_id)
|
||||
else:
|
||||
new_chat = BrainChatting(chat_id=chat_id)
|
||||
await new_chat.start()
|
||||
self.heartflow_chat_list[chat_id] = new_chat
|
||||
return new_chat
|
||||
except Exception as e:
|
||||
logger.error(f"创建心流聊天 {chat_id} 失败: {e}", exc_info=True)
|
||||
traceback.print_exc()
|
||||
return None
|
||||
|
||||
|
||||
heartflow = Heartflow()
|
||||
@@ -1,19 +1,20 @@
|
||||
from contextlib import suppress
|
||||
import traceback
|
||||
import os
|
||||
|
||||
from maim_message import MessageBase
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
import os
|
||||
import traceback
|
||||
|
||||
from maim_message import MessageBase
|
||||
|
||||
from src.chat.heart_flow.heartflow_message_processor import HeartFCMessageReceiver
|
||||
from src.common.logger import get_logger
|
||||
from src.common.utils.utils_message import MessageUtils
|
||||
from src.common.utils.utils_session import SessionUtils
|
||||
from src.chat.heart_flow.heartflow_message_processor import HeartFCMessageReceiver
|
||||
from src.platform_io.route_key_factory import RouteKeyFactory
|
||||
|
||||
# from src.chat.brain_chat.PFC.pfc_manager import PFCManager
|
||||
from src.core.announcement_manager import global_announcement_manager
|
||||
from src.core.component_registry import component_registry
|
||||
from src.plugin_runtime.component_query import component_query_service
|
||||
|
||||
from .message import SessionMessage
|
||||
from .chat_manager import chat_manager
|
||||
@@ -58,16 +59,22 @@ class ChatBot:
|
||||
logger.error(f"创建PFC聊天失败: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
async def _process_commands(self, message: SessionMessage):
|
||||
# sourcery skip: use-named-expression
|
||||
"""使用新插件系统处理命令"""
|
||||
async def _process_commands(self, message: SessionMessage) -> tuple[bool, Optional[str], bool]:
|
||||
"""使用统一组件注册表处理命令。
|
||||
|
||||
Args:
|
||||
message: 当前待处理的会话消息。
|
||||
|
||||
Returns:
|
||||
tuple[bool, Optional[str], bool]: ``(是否命中命令, 命令响应文本, 是否继续后续处理)``。
|
||||
"""
|
||||
if not message.processed_plain_text:
|
||||
return False, None, True # 没有文本内容，继续处理消息
|
||||
try:
|
||||
text = message.processed_plain_text
|
||||
|
||||
# 使用核心组件注册表查找命令
|
||||
command_result = component_registry.find_command_by_text(text)
|
||||
# 使用插件运行时统一查询服务查找命令
|
||||
command_result = component_query_service.find_command_by_text(text)
|
||||
if command_result:
|
||||
command_executor, matched_groups, command_info = command_result
|
||||
plugin_name = command_info.plugin_name
|
||||
@@ -81,7 +88,7 @@ class ChatBot:
|
||||
message.is_command = True
|
||||
|
||||
# 获取插件配置
|
||||
plugin_config = component_registry.get_plugin_config(plugin_name)
|
||||
plugin_config = component_query_service.get_plugin_config(plugin_name)
|
||||
|
||||
try:
|
||||
# 调用命令执行器
|
||||
@@ -112,88 +119,32 @@ class ChatBot:
|
||||
# 命令出错时，根据命令的拦截设置决定是否继续处理消息
|
||||
return True, str(e), False # 出错时继续处理消息
|
||||
|
||||
# 没有找到旧系统命令，尝试新版本插件运行时
|
||||
new_cmd_result = await self._process_new_runtime_command(message)
|
||||
return new_cmd_result if new_cmd_result is not None else (False, None, True)
|
||||
return False, None, True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"处理命令时出错: {e}")
|
||||
return False, None, True # 出错时继续处理消息
|
||||
|
||||
async def _process_new_runtime_command(self, message: SessionMessage):
|
||||
"""尝试在新版本插件运行时中查找并执行命令
|
||||
|
||||
Returns:
|
||||
(found, response, continue_processing) 三元组，
|
||||
或 None 表示新运行时中也未找到匹配命令。
|
||||
"""
|
||||
from src.plugin_runtime.integration import get_plugin_runtime_manager
|
||||
|
||||
prm = get_plugin_runtime_manager()
|
||||
if not prm.is_running:
|
||||
return None
|
||||
|
||||
matched = prm.find_command_by_text(message.processed_plain_text)
|
||||
if matched is None:
|
||||
return None
|
||||
|
||||
command_name = matched["name"]
|
||||
if message.session_id and command_name in global_announcement_manager.get_disabled_chat_commands(
|
||||
message.session_id
|
||||
):
|
||||
logger.info(f"[新运行时] 用户禁用的命令，跳过处理: {matched['full_name']}")
|
||||
return False, None, True
|
||||
|
||||
message.is_command = True
|
||||
logger.info(f"[新运行时] 匹配命令: {matched['full_name']}")
|
||||
|
||||
try:
|
||||
resp = await prm.invoke_plugin(
|
||||
method="plugin.invoke_command",
|
||||
plugin_id=matched["plugin_id"],
|
||||
component_name=matched["name"],
|
||||
args={
|
||||
"text": message.processed_plain_text,
|
||||
"stream_id": message.session_id or "",
|
||||
"matched_groups": matched.get("matched_groups") or {},
|
||||
},
|
||||
timeout_ms=30000,
|
||||
)
|
||||
|
||||
payload = resp.payload
|
||||
success = payload.get("success", False)
|
||||
cmd_result = payload.get("result")
|
||||
|
||||
# 拦截位优先从命令返回值中获取（支持运行时动态决定），
|
||||
# 回退到组件 metadata 中的静态声明
|
||||
if isinstance(cmd_result, (list, tuple)) and len(cmd_result) >= 3:
|
||||
# 命令返回 (found, response_text, intercept_bool) 三元组
|
||||
response_text = cmd_result[1] if cmd_result[1] is not None else ""
|
||||
intercept = bool(cmd_result[2])
|
||||
else:
|
||||
response_text = cmd_result if cmd_result is not None else ""
|
||||
intercept = bool(matched["metadata"].get("intercept_message_level", 0))
|
||||
|
||||
self._mark_command_message(message, int(intercept))
|
||||
|
||||
if success:
|
||||
logger.info(f"[新运行时] 命令执行成功: {matched['full_name']}")
|
||||
else:
|
||||
logger.warning(f"[新运行时] 命令执行失败: {matched['full_name']} - {response_text}")
|
||||
|
||||
return True, response_text, not intercept
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[新运行时] 执行命令 {matched['full_name']} 异常: {e}", exc_info=True)
|
||||
return True, str(e), True
|
||||
|
||||
@staticmethod
|
||||
def _mark_command_message(message: SessionMessage, intercept_message_level: int) -> None:
|
||||
"""标记消息已经被命令链消费。
|
||||
|
||||
Args:
|
||||
message: 待标记的会话消息。
|
||||
intercept_message_level: 命令设置的拦截级别。
|
||||
"""
|
||||
|
||||
message.is_command = True
|
||||
message.message_info.additional_config["intercept_message_level"] = intercept_message_level
|
||||
|
||||
@staticmethod
|
||||
def _store_intercepted_command_message(message: SessionMessage) -> None:
|
||||
"""将被命令链拦截的消息写入数据库。
|
||||
|
||||
Args:
|
||||
message: 已完成命令处理的会话消息。
|
||||
"""
|
||||
|
||||
MessageUtils.store_message_to_db(message)
|
||||
|
||||
async def _handle_command_processing_result(
|
||||
@@ -310,13 +261,28 @@ class ChatBot:
|
||||
# logger.debug(str(message_data))
|
||||
maim_raw_message = MessageBase.from_dict(message_data)
|
||||
message = SessionMessage.from_maim_message(maim_raw_message)
|
||||
await self.receive_message(message)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"预处理消息失败: {e}")
|
||||
traceback.print_exc()
|
||||
|
||||
async def receive_message(self, message: SessionMessage):
|
||||
try:
|
||||
group_info = message.message_info.group_info
|
||||
user_info = message.message_info.user_info
|
||||
account_id = None
|
||||
scope = None
|
||||
additional_config = message.message_info.additional_config
|
||||
if isinstance(additional_config, dict):
|
||||
account_id, scope = RouteKeyFactory.extract_components(additional_config)
|
||||
|
||||
session_id = SessionUtils.calculate_session_id(
|
||||
message.platform,
|
||||
user_id=message.message_info.user_info.user_id,
|
||||
group_id=group_info.group_id if group_info else None,
|
||||
account_id=account_id,
|
||||
scope=scope,
|
||||
)
|
||||
|
||||
message.session_id = session_id # 正确初始化session_id
|
||||
@@ -359,24 +325,24 @@ class ChatBot:
|
||||
platform = message.platform
|
||||
user_id = user_info.user_id
|
||||
group_id = group_info.group_id if group_info else None
|
||||
_ = await chat_manager.get_or_create_session(platform, user_id, group_id) # 确保会话存在
|
||||
try:
|
||||
from src.services.memory_flow_service import memory_automation_service
|
||||
|
||||
await memory_automation_service.on_incoming_message(message)
|
||||
except Exception as exc:
|
||||
logger.warning(f"[长期记忆自动总结] 注册会话总结器失败: {exc}")
|
||||
_ = await chat_manager.get_or_create_session(
|
||||
platform,
|
||||
user_id,
|
||||
group_id,
|
||||
account_id=account_id,
|
||||
scope=scope,
|
||||
) # 确保会话存在
|
||||
|
||||
# message.update_chat_stream(chat)
|
||||
|
||||
# 命令处理 - 使用新插件系统检查并处理命令
|
||||
# 注意：命令返回的 response 当前只用于日志记录和流程判断，
|
||||
# 不会在这里自动作为回复消息发送回会话。
|
||||
is_command, cmd_result, continue_process = await self._process_commands(message)
|
||||
# is_command, cmd_result, continue_process = await self._process_commands(message)
|
||||
|
||||
# 如果是命令且不需要继续处理，则直接返回
|
||||
if is_command and await self._handle_command_processing_result(message, cmd_result, continue_process):
|
||||
return
|
||||
# # 如果是命令且不需要继续处理，则直接返回
|
||||
# if is_command and await self._handle_command_processing_result(message, cmd_result, continue_process):
|
||||
# return
|
||||
|
||||
# continue_flag, modified_message = await events_manager.handle_mai_events(EventType.ON_MESSAGE, message)
|
||||
# if not continue_flag:
|
||||
|
||||
@@ -1,15 +1,16 @@
|
||||
import asyncio
|
||||
from datetime import datetime
|
||||
from typing import TYPE_CHECKING, Dict, List, Optional
|
||||
|
||||
from rich.traceback import install
|
||||
from sqlmodel import select
|
||||
from typing import Optional, TYPE_CHECKING, List, Dict
|
||||
|
||||
import asyncio
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from src.common.data_models.chat_session_data_model import MaiChatSession
|
||||
from src.common.database.database_model import ChatSession
|
||||
from src.common.database.database import get_db_session
|
||||
from src.common.database.database_model import ChatSession
|
||||
from src.common.logger import get_logger
|
||||
from src.common.utils.utils_session import SessionUtils
|
||||
from src.platform_io.route_key_factory import RouteKeyFactory
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from .message import SessionMessage
|
||||
@@ -82,7 +83,12 @@ class ChatManager:
|
||||
logger.error(f"初始化聊天管理器出现错误: {e}")
|
||||
|
||||
async def get_or_create_session(
|
||||
self, platform: str, user_id: str, group_id: Optional[str] = None
|
||||
self,
|
||||
platform: str,
|
||||
user_id: str,
|
||||
group_id: Optional[str] = None,
|
||||
account_id: Optional[str] = None,
|
||||
scope: Optional[str] = None,
|
||||
) -> BotChatSession:
|
||||
"""获取会话，如果不存在则创建一个新会话；一个封装方法。
|
||||
|
||||
@@ -90,12 +96,20 @@ class ChatManager:
|
||||
platform: 平台
|
||||
user_id: 用户ID
|
||||
group_id: 群ID（如果是群聊）
|
||||
account_id: 平台账号 ID
|
||||
scope: 路由作用域
|
||||
Returns:
|
||||
return (BotChatSession) 会话对象
|
||||
Raises:
|
||||
Exception: 获取或创建会话时发生错误
|
||||
"""
|
||||
session_id = SessionUtils.calculate_session_id(platform, user_id=user_id, group_id=group_id)
|
||||
session_id = SessionUtils.calculate_session_id(
|
||||
platform,
|
||||
user_id=user_id,
|
||||
group_id=group_id,
|
||||
account_id=account_id,
|
||||
scope=scope,
|
||||
)
|
||||
if session := self.get_session_by_session_id(session_id):
|
||||
session.update_active_time()
|
||||
return session
|
||||
@@ -131,7 +145,18 @@ class ChatManager:
|
||||
raise ValueError("消息缺少平台信息")
|
||||
user_id = message.message_info.user_info.user_id
|
||||
group_id = message.message_info.group_info.group_id if message.message_info.group_info else None
|
||||
session_id = SessionUtils.calculate_session_id(platform, user_id=user_id, group_id=group_id)
|
||||
account_id = None
|
||||
scope = None
|
||||
additional_config = message.message_info.additional_config
|
||||
if isinstance(additional_config, dict):
|
||||
account_id, scope = RouteKeyFactory.extract_components(additional_config)
|
||||
session_id = SessionUtils.calculate_session_id(
|
||||
platform,
|
||||
user_id=user_id,
|
||||
group_id=group_id,
|
||||
account_id=account_id,
|
||||
scope=scope,
|
||||
)
|
||||
message.session_id = session_id # 确保消息的session_id正确设置
|
||||
self.last_messages[session_id] = message
|
||||
|
||||
@@ -188,7 +213,12 @@ class ChatManager:
|
||||
return None
|
||||
|
||||
def get_session_by_info(
|
||||
self, platform: str, user_id: Optional[str] = None, group_id: Optional[str] = None
|
||||
self,
|
||||
platform: str,
|
||||
user_id: Optional[str] = None,
|
||||
group_id: Optional[str] = None,
|
||||
account_id: Optional[str] = None,
|
||||
scope: Optional[str] = None,
|
||||
) -> Optional[BotChatSession]:
|
||||
"""根据平台、用户ID和群ID获取对应的会话
|
||||
|
||||
@@ -196,10 +226,18 @@ class ChatManager:
|
||||
platform: 平台
|
||||
user_id: 用户ID
|
||||
group_id: 群ID（如果是群聊）
|
||||
account_id: 平台账号 ID
|
||||
scope: 路由作用域
|
||||
Returns:
|
||||
return (Optional[BotChatSession]): 会话对象，如果不存在则返回None
|
||||
"""
|
||||
session_id = SessionUtils.calculate_session_id(platform, user_id=user_id, group_id=group_id)
|
||||
session_id = SessionUtils.calculate_session_id(
|
||||
platform,
|
||||
user_id=user_id,
|
||||
group_id=group_id,
|
||||
account_id=account_id,
|
||||
scope=scope,
|
||||
)
|
||||
return self.get_session_by_session_id(session_id)
|
||||
|
||||
def get_session_by_session_id(self, session_id: str) -> Optional[BotChatSession]:
|
||||
|
||||
@@ -1,31 +1,37 @@
|
||||
from rich.traceback import install
|
||||
from typing import Optional
|
||||
from typing import Any, Optional, Tuple
|
||||
|
||||
import asyncio
|
||||
import traceback
|
||||
|
||||
from rich.traceback import install
|
||||
|
||||
from src.common.message_server.api import get_global_api
|
||||
from src.common.logger import get_logger
|
||||
from src.common.database.database import get_db_session
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.chat.utils.utils import calculate_typing_time, truncate_message
|
||||
from src.common.data_models.message_component_data_model import ReplyComponent
|
||||
from src.chat.utils.utils import truncate_message
|
||||
from src.chat.utils.utils import calculate_typing_time
|
||||
from src.common.database.database import get_db_session
|
||||
from src.common.logger import get_logger
|
||||
from src.common.message_server.api import get_global_api
|
||||
from src.webui.routers.chat.serializers import serialize_message_sequence
|
||||
|
||||
install(extra_lines=3)
|
||||
|
||||
logger = get_logger("sender")
|
||||
|
||||
# WebUI 聊天室的消息广播器（延迟导入避免循环依赖）
|
||||
_webui_chat_broadcaster = None
|
||||
_webui_chat_broadcaster: Optional[Tuple[Any, Optional[str]]] = None
|
||||
|
||||
# 虚拟群 ID 前缀（与 chat_routes.py 保持一致）
|
||||
VIRTUAL_GROUP_ID_PREFIX = "webui_virtual_group_"
|
||||
|
||||
|
||||
# TODO: 重构完成后完成webui相关
|
||||
def get_webui_chat_broadcaster():
|
||||
"""获取 WebUI 聊天室广播器"""
|
||||
def get_webui_chat_broadcaster() -> Tuple[Any, Optional[str]]:
|
||||
"""获取 WebUI 聊天室广播器。
|
||||
|
||||
Returns:
|
||||
Tuple[Any, Optional[str]]: ``(chat_manager, platform_name)`` 二元组；
|
||||
若 WebUI 相关模块不可用，则元素会退化为 ``None``。
|
||||
"""
|
||||
global _webui_chat_broadcaster
|
||||
if _webui_chat_broadcaster is None:
|
||||
try:
|
||||
@@ -38,102 +44,35 @@ def get_webui_chat_broadcaster():
|
||||
|
||||
|
||||
def is_webui_virtual_group(group_id: str) -> bool:
|
||||
"""检查是否是 WebUI 虚拟群"""
|
||||
return group_id and group_id.startswith(VIRTUAL_GROUP_ID_PREFIX)
|
||||
|
||||
|
||||
def parse_message_segments(segment) -> list:
|
||||
"""解析消息段，转换为 WebUI 可用的格式
|
||||
|
||||
参考 NapCat 适配器的消息解析逻辑
|
||||
"""检查是否是 WebUI 虚拟群。
|
||||
|
||||
Args:
|
||||
segment: Seg 消息段对象
|
||||
group_id: 待判断的群 ID。
|
||||
|
||||
Returns:
|
||||
list: 消息段列表，每个元素为 {"type": "...", "data": ...}
|
||||
bool: 若群 ID 属于 WebUI 虚拟群则返回 ``True``。
|
||||
"""
|
||||
|
||||
result = []
|
||||
|
||||
if segment is None:
|
||||
return result
|
||||
|
||||
if segment.type == "seglist":
|
||||
# 处理消息段列表
|
||||
if segment.data:
|
||||
for seg in segment.data:
|
||||
result.extend(parse_message_segments(seg))
|
||||
elif segment.type == "text":
|
||||
# 文本消息
|
||||
if segment.data:
|
||||
result.append({"type": "text", "data": segment.data})
|
||||
elif segment.type == "image":
|
||||
# 图片消息（base64）
|
||||
if segment.data:
|
||||
result.append({"type": "image", "data": f"data:image/png;base64,{segment.data}"})
|
||||
elif segment.type == "emoji":
|
||||
# 表情包消息（base64）
|
||||
if segment.data:
|
||||
result.append({"type": "emoji", "data": f"data:image/gif;base64,{segment.data}"})
|
||||
elif segment.type == "imageurl":
|
||||
# 图片链接消息
|
||||
if segment.data:
|
||||
result.append({"type": "image", "data": segment.data})
|
||||
elif segment.type == "face":
|
||||
# 原生表情
|
||||
result.append({"type": "face", "data": segment.data})
|
||||
elif segment.type == "voice":
|
||||
# 语音消息（base64）
|
||||
if segment.data:
|
||||
result.append({"type": "voice", "data": f"data:audio/wav;base64,{segment.data}"})
|
||||
elif segment.type == "voiceurl":
|
||||
# 语音链接
|
||||
if segment.data:
|
||||
result.append({"type": "voice", "data": segment.data})
|
||||
elif segment.type == "video":
|
||||
# 视频消息（base64）
|
||||
if segment.data:
|
||||
result.append({"type": "video", "data": f"data:video/mp4;base64,{segment.data}"})
|
||||
elif segment.type == "videourl":
|
||||
# 视频链接
|
||||
if segment.data:
|
||||
result.append({"type": "video", "data": segment.data})
|
||||
elif segment.type == "music":
|
||||
# 音乐消息
|
||||
result.append({"type": "music", "data": segment.data})
|
||||
elif segment.type == "file":
|
||||
# 文件消息
|
||||
result.append({"type": "file", "data": segment.data})
|
||||
elif segment.type == "reply":
|
||||
# 回复消息
|
||||
result.append({"type": "reply", "data": segment.data})
|
||||
elif segment.type == "forward":
|
||||
# 转发消息
|
||||
forward_items = []
|
||||
if segment.data:
|
||||
for item in segment.data:
|
||||
forward_items.append(
|
||||
{
|
||||
"content": parse_message_segments(item.get("message_segment", {}))
|
||||
if isinstance(item, dict)
|
||||
else []
|
||||
}
|
||||
)
|
||||
result.append({"type": "forward", "data": forward_items})
|
||||
else:
|
||||
# 未知类型，尝试作为文本处理
|
||||
if segment.data:
|
||||
result.append({"type": "unknown", "original_type": segment.type, "data": str(segment.data)})
|
||||
|
||||
return result
|
||||
return bool(group_id) and group_id.startswith(VIRTUAL_GROUP_ID_PREFIX)
|
||||
|
||||
|
||||
async def _send_message(message: MessageSending, show_log=True) -> bool:
|
||||
"""合并后的消息发送函数，包含WS发送和日志记录"""
|
||||
async def _send_message(message: SessionMessage, show_log: bool = True) -> bool:
|
||||
"""执行统一的消息发送流程。
|
||||
|
||||
发送顺序为：
|
||||
1. WebUI 特殊链路
|
||||
2. 旧版 ``maim_message`` / API Server 链路
|
||||
|
||||
Args:
|
||||
message: 待发送的内部会话消息。
|
||||
show_log: 是否输出发送成功日志。
|
||||
|
||||
Returns:
|
||||
bool: 是否最终发送成功。
|
||||
"""
|
||||
message_preview = truncate_message(message.processed_plain_text, max_length=200)
|
||||
platform = message.platform
|
||||
group_id = message.session.group_id
|
||||
group_info = message.message_info.group_info
|
||||
group_id = group_info.group_id if group_info is not None else ""
|
||||
|
||||
try:
|
||||
# 检查是否是 WebUI 平台的消息，或者是 WebUI 虚拟群的消息
|
||||
@@ -146,7 +85,7 @@ async def _send_message(message: MessageSending, show_log=True) -> bool:
|
||||
from src.config.config import global_config
|
||||
|
||||
# 解析消息段，获取富文本内容
|
||||
message_segments = parse_message_segments(message.message_segment)
|
||||
message_segments = serialize_message_sequence(message.raw_message)
|
||||
|
||||
# 判断消息类型
|
||||
# 如果只有一个文本段，使用简单的 text 类型
|
||||
@@ -185,7 +124,15 @@ async def _send_message(message: MessageSending, show_log=True) -> bool:
|
||||
return True
|
||||
|
||||
# Fallback 逻辑: 尝试通过 API Server 发送
|
||||
async def send_with_new_api(legacy_exception=None):
|
||||
async def send_with_new_api(legacy_exception: Optional[Exception] = None) -> bool:
|
||||
"""通过 API Server 回退链路发送消息。
|
||||
|
||||
Args:
|
||||
legacy_exception: 旧发送链已经抛出的异常；若回退也失败，则重新抛出。
|
||||
|
||||
Returns:
|
||||
bool: 回退链路是否发送成功。
|
||||
"""
|
||||
try:
|
||||
from src.config.config import global_config
|
||||
|
||||
@@ -286,10 +233,24 @@ async def _send_message(message: MessageSending, show_log=True) -> bool:
|
||||
raise e # 重新抛出其他异常
|
||||
|
||||
|
||||
class UniversalMessageSender:
|
||||
"""管理消息的注册、即时处理、发送和存储，并跟踪思考状态。"""
|
||||
async def send_prepared_message_to_platform(message: SessionMessage, show_log: bool = True) -> bool:
|
||||
"""发送一条已完成预处理的消息到底层平台。
|
||||
|
||||
def __init__(self):
|
||||
Args:
|
||||
message: 已经完成回复组件注入、文本处理等预处理的消息对象。
|
||||
show_log: 是否输出发送成功日志。
|
||||
|
||||
Returns:
|
||||
bool: 发送成功时返回 ``True``。
|
||||
"""
|
||||
return await _send_message(message, show_log=show_log)
|
||||
|
||||
|
||||
class UniversalMessageSender:
|
||||
"""旧链与 WebUI 的底层发送器。"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
"""初始化统一消息发送器。"""
|
||||
pass
|
||||
|
||||
async def send_message(
|
||||
@@ -300,18 +261,19 @@ class UniversalMessageSender:
|
||||
reply_message_id: Optional[str] = None,
|
||||
storage_message: bool = True,
|
||||
show_log: bool = True,
|
||||
):
|
||||
"""
|
||||
处理、发送并存储一条消息。
|
||||
) -> bool:
|
||||
"""通过旧链或 WebUI 发送并存储一条消息。
|
||||
|
||||
参数：
|
||||
message: MessageSession 对象，待发送的消息。
|
||||
Args:
|
||||
message: 待发送的内部消息对象。
|
||||
typing: 是否模拟打字等待。
|
||||
set_reply: 是否构建回复引用消息。
|
||||
set_reply: 是否构建引用回复消息。
|
||||
reply_message_id: 被引用消息的 ID。
|
||||
storage_message: 是否在发送成功后写入数据库。
|
||||
show_log: 是否输出发送日志。
|
||||
|
||||
|
||||
用法：
|
||||
- typing=True 时，发送前会有打字等待。
|
||||
Returns:
|
||||
bool: 发送成功时返回 ``True``。
|
||||
"""
|
||||
if not message.message_id:
|
||||
logger.error("消息缺少 message_id，无法发送")
|
||||
@@ -364,7 +326,7 @@ class UniversalMessageSender:
|
||||
)
|
||||
await asyncio.sleep(typing_time)
|
||||
|
||||
sent_msg = await _send_message(message, show_log=show_log)
|
||||
sent_msg = await send_prepared_message_to_platform(message, show_log=show_log)
|
||||
if not sent_msg:
|
||||
return False
|
||||
|
||||
|
||||
@@ -3,8 +3,8 @@ from typing import Dict, Optional, Tuple
|
||||
from src.chat.message_receive.chat_manager import BotChatSession
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.common.logger import get_logger
|
||||
from src.core.component_registry import component_registry, ActionExecutor
|
||||
from src.core.types import ActionInfo
|
||||
from src.plugin_runtime.component_query import ActionExecutor, component_query_service
|
||||
|
||||
logger = get_logger("action_manager")
|
||||
|
||||
@@ -28,7 +28,7 @@ class ActionManager:
|
||||
"""
|
||||
动作管理器，用于管理各种类型的动作
|
||||
|
||||
使用核心组件注册表的 executor-based 模式。
|
||||
使用插件运行时统一查询服务的 executor-based 模式。
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
@@ -38,7 +38,7 @@ class ActionManager:
|
||||
self._using_actions: Dict[str, ActionInfo] = {}
|
||||
|
||||
# 初始化时将默认动作加载到使用中的动作
|
||||
self._using_actions = component_registry.get_default_actions()
|
||||
self._using_actions = component_query_service.get_default_actions()
|
||||
|
||||
# === 执行Action方法 ===
|
||||
|
||||
@@ -72,17 +72,17 @@ class ActionManager:
|
||||
Optional[ActionHandle]: 执行句柄，如果动作未注册则返回 None
|
||||
"""
|
||||
try:
|
||||
executor = component_registry.get_action_executor(action_name)
|
||||
executor = component_query_service.get_action_executor(action_name)
|
||||
if not executor:
|
||||
logger.warning(f"{log_prefix} 未找到Action组件: {action_name}")
|
||||
return None
|
||||
|
||||
info = component_registry.get_action_info(action_name)
|
||||
info = component_query_service.get_action_info(action_name)
|
||||
if not info:
|
||||
logger.warning(f"{log_prefix} 未找到Action组件信息: {action_name}")
|
||||
return None
|
||||
|
||||
plugin_config = component_registry.get_plugin_config(info.plugin_name) or {}
|
||||
plugin_config = component_query_service.get_plugin_config(info.plugin_name) or {}
|
||||
|
||||
handle = ActionHandle(
|
||||
executor,
|
||||
@@ -133,5 +133,5 @@ class ActionManager:
|
||||
def restore_actions(self) -> None:
|
||||
"""恢复到默认动作集"""
|
||||
actions_to_restore = list(self._using_actions.keys())
|
||||
self._using_actions = component_registry.get_default_actions()
|
||||
self._using_actions = component_query_service.get_default_actions()
|
||||
logger.debug(f"恢复动作集: 从 {actions_to_restore} 恢复到默认动作集 {list(self._using_actions.keys())}")
|
||||
|
||||
@@ -1,33 +1,36 @@
|
||||
from collections import OrderedDict
|
||||
from datetime import datetime
|
||||
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import contextlib
|
||||
import json
|
||||
import time
|
||||
import traceback
|
||||
import random
|
||||
import re
|
||||
import contextlib
|
||||
from typing import Dict, Optional, Tuple, List, TYPE_CHECKING, Union
|
||||
from collections import OrderedDict
|
||||
from rich.traceback import install
|
||||
from datetime import datetime
|
||||
import time
|
||||
import traceback
|
||||
|
||||
from json_repair import repair_json
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.config.config import global_config, model_config
|
||||
from src.common.logger import get_logger
|
||||
from rich.traceback import install
|
||||
|
||||
from src.chat.logger.plan_reply_logger import PlanReplyLogger
|
||||
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.chat.planner_actions.action_manager import ActionManager
|
||||
from src.chat.utils.utils import get_chat_type_and_target_info, is_bot_self
|
||||
from src.common.data_models.info_data_model import ActionPlannerInfo
|
||||
from src.common.logger import get_logger
|
||||
from src.config.config import global_config, model_config
|
||||
from src.core.types import ActionActivationType, ActionInfo, ComponentType
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.person_info.person_info import Person
|
||||
from src.plugin_runtime.component_query import component_query_service
|
||||
from src.prompt.prompt_manager import prompt_manager
|
||||
from src.services.message_service import (
|
||||
build_readable_messages_with_id,
|
||||
replace_user_references,
|
||||
get_messages_before_time_in_chat,
|
||||
replace_user_references,
|
||||
translate_pid_to_description,
|
||||
)
|
||||
from src.chat.utils.utils import get_chat_type_and_target_info, is_bot_self
|
||||
from src.chat.planner_actions.action_manager import ActionManager
|
||||
from src.chat.message_receive.chat_manager import chat_manager as _chat_manager
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.core.types import ActionActivationType, ActionInfo, ComponentType
|
||||
from src.core.component_registry import component_registry
|
||||
from src.person_info.person_info import Person
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from src.common.data_models.info_data_model import TargetPersonInfo
|
||||
@@ -634,7 +637,7 @@ class ActionPlanner:
|
||||
current_available_actions_dict = self.action_manager.get_using_actions()
|
||||
|
||||
# 获取完整的动作信息
|
||||
all_registered_actions: Dict[str, ActionInfo] = component_registry.get_components_by_type( # type: ignore
|
||||
all_registered_actions: Dict[str, ActionInfo] = component_query_service.get_components_by_type( # type: ignore
|
||||
ComponentType.ACTION
|
||||
)
|
||||
current_available_actions = {}
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
import traceback
|
||||
import time
|
||||
import asyncio
|
||||
import importlib
|
||||
import random
|
||||
import re
|
||||
|
||||
@@ -16,7 +17,6 @@ from maim_message import BaseMessageInfo, MessageBase, Seg, UserInfo as MaimUser
|
||||
from src.common.data_models.mai_message_data_model import MaiMessage
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.chat.message_receive.chat_manager import BotChatSession
|
||||
from src.chat.message_receive.uni_message_sender import UniversalMessageSender
|
||||
from src.chat.utils.timer_calculator import Timer # <--- Import Timer
|
||||
from src.chat.utils.utils import get_bot_account, get_chat_type_and_target_info, is_bot_self
|
||||
from src.prompt.prompt_manager import prompt_manager
|
||||
@@ -26,7 +26,7 @@ from src.services.message_service import (
|
||||
replace_user_references,
|
||||
translate_pid_to_description,
|
||||
)
|
||||
from src.bw_learner.expression_selector import expression_selector
|
||||
from src.learners.expression_selector import expression_selector
|
||||
|
||||
# from src.memory_system.memory_activator import MemoryActivator
|
||||
from src.person_info.person_info import Person
|
||||
@@ -35,8 +35,7 @@ from src.services import llm_service as llm_api
|
||||
|
||||
from src.chat.logger.plan_reply_logger import PlanReplyLogger
|
||||
from src.memory_system.memory_retrieval import init_memory_retrieval_sys, build_memory_retrieval_prompt
|
||||
from src.memory_system.retrieval_tools import get_tool_registry
|
||||
from src.bw_learner.jargon_explainer_old import explain_jargon_in_context, retrieve_concepts_with_jargon
|
||||
from src.learners.jargon_explainer_old import explain_jargon_in_context, retrieve_concepts_with_jargon
|
||||
from src.chat.utils.common_utils import TempMethodsExpression
|
||||
|
||||
init_memory_retrieval_sys()
|
||||
@@ -51,10 +50,15 @@ class DefaultReplyer:
|
||||
chat_stream: BotChatSession,
|
||||
request_type: str = "replyer",
|
||||
):
|
||||
"""初始化群聊回复器。
|
||||
|
||||
Args:
|
||||
chat_stream: 当前绑定的聊天会话。
|
||||
request_type: LLM 请求类型标识。
|
||||
"""
|
||||
self.express_model = LLMRequest(model_set=model_config.model_task_config.replyer, request_type=request_type)
|
||||
self.chat_stream = chat_stream
|
||||
self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.session_id)
|
||||
self.heart_fc_sender = UniversalMessageSender()
|
||||
|
||||
from src.chat.tool_executor import ToolExecutor
|
||||
|
||||
@@ -1129,7 +1133,10 @@ class DefaultReplyer:
|
||||
user_id=bot_user_id,
|
||||
user_nickname=global_config.bot.nickname,
|
||||
),
|
||||
additional_config={},
|
||||
additional_config={
|
||||
"platform_io_target_group_id": self.chat_stream.group_id,
|
||||
"platform_io_target_user_id": self.chat_stream.user_id,
|
||||
},
|
||||
),
|
||||
message_segment=message_segment,
|
||||
)
|
||||
@@ -1164,14 +1171,29 @@ class DefaultReplyer:
|
||||
async def get_prompt_info(self, message: str, sender: str, target: str):
|
||||
related_info = ""
|
||||
start_time = time.time()
|
||||
search_knowledge_tool = get_tool_registry().get_tool("search_long_term_memory")
|
||||
if search_knowledge_tool is None:
|
||||
logger.debug("长期记忆检索工具未注册，跳过获取知识内容")
|
||||
try:
|
||||
knowledge_module = importlib.import_module("src.plugins.built_in.knowledge.lpmm_get_knowledge")
|
||||
except ImportError:
|
||||
logger.debug("LPMM知识库工具模块不存在，跳过获取知识库内容")
|
||||
return ""
|
||||
|
||||
logger.debug(f"获取长期记忆内容，元消息：{message[:30]}...，消息长度: {len(message)}")
|
||||
search_knowledge_tool = getattr(knowledge_module, "SearchKnowledgeFromLPMMTool", None)
|
||||
if search_knowledge_tool is None:
|
||||
logger.debug("LPMM知识库工具未提供 SearchKnowledgeFromLPMMTool，跳过获取知识库内容")
|
||||
return ""
|
||||
|
||||
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
|
||||
# 从LPMM知识库获取知识
|
||||
try:
|
||||
template_prompt = prompt_manager.get_prompt("memory_get_knowledge")
|
||||
# 检查LPMM知识库是否启用
|
||||
if not global_config.lpmm_knowledge.enable:
|
||||
logger.debug("LPMM知识库未启用，跳过获取知识库内容")
|
||||
return ""
|
||||
|
||||
if global_config.lpmm_knowledge.lpmm_mode == "agent":
|
||||
return ""
|
||||
|
||||
template_prompt = prompt_manager.get_prompt("lpmm_get_knowledge")
|
||||
template_prompt.add_context("bot_name", global_config.bot.nickname)
|
||||
template_prompt.add_context("time_now", lambda _: time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
|
||||
template_prompt.add_context("chat_history", message)
|
||||
@@ -1187,31 +1209,24 @@ class DefaultReplyer:
|
||||
# logger.info(f"工具调用提示词: {prompt}")
|
||||
# logger.info(f"工具调用: {tool_calls}")
|
||||
|
||||
if not tool_calls:
|
||||
logger.debug("模型认为不需要使用长期记忆")
|
||||
if tool_calls:
|
||||
result = await self.tool_executor.execute_tool_call(tool_calls[0])
|
||||
end_time = time.time()
|
||||
if not result or not result.get("content"):
|
||||
logger.debug("从LPMM知识库获取知识失败，返回空知识...")
|
||||
return ""
|
||||
found_knowledge_from_lpmm = result.get("content", "")
|
||||
logger.info(
|
||||
f"从LPMM知识库获取知识，相关信息：{found_knowledge_from_lpmm[:100]}...，信息长度: {len(found_knowledge_from_lpmm)}"
|
||||
)
|
||||
related_info += found_knowledge_from_lpmm
|
||||
logger.debug(f"获取知识库内容耗时: {(end_time - start_time):.3f}秒")
|
||||
logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
|
||||
|
||||
return f"你有以下这些**知识**：\n{related_info}\n请你**记住上面的知识**，之后可能会用到。\n"
|
||||
else:
|
||||
logger.debug("模型认为不需要使用LPMM知识库")
|
||||
return ""
|
||||
|
||||
related_chunks: List[str] = []
|
||||
for tool_call in tool_calls:
|
||||
if tool_call.func_name != "search_long_term_memory":
|
||||
continue
|
||||
tool_args = dict(tool_call.args or {})
|
||||
tool_args.setdefault("chat_id", self.chat_stream.session_id)
|
||||
result_text = await search_knowledge_tool.execute(**tool_args)
|
||||
if result_text and "未找到" not in result_text:
|
||||
related_chunks.append(result_text)
|
||||
|
||||
if not related_chunks:
|
||||
logger.debug("长期记忆未返回有效信息")
|
||||
return ""
|
||||
|
||||
related_info = "\n".join(related_chunks)
|
||||
end_time = time.time()
|
||||
logger.info(f"从长期记忆获取知识，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
|
||||
logger.debug(f"获取知识库内容耗时: {(end_time - start_time):.3f}秒")
|
||||
logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
|
||||
|
||||
return f"你有以下这些**知识**：\n{related_info}\n请你**记住上面的知识**，之后可能会用到。\n"
|
||||
except Exception as e:
|
||||
logger.error(f"获取知识库内容时发生异常: {str(e)}")
|
||||
return ""
|
||||
|
||||
@@ -16,7 +16,6 @@ from maim_message import BaseMessageInfo, MessageBase, Seg, UserInfo as MaimUser
|
||||
from src.common.data_models.mai_message_data_model import MaiMessage
|
||||
from src.chat.message_receive.message import SessionMessage
|
||||
from src.chat.message_receive.chat_manager import BotChatSession
|
||||
from src.chat.message_receive.uni_message_sender import UniversalMessageSender
|
||||
from src.chat.utils.timer_calculator import Timer
|
||||
from src.chat.utils.utils import get_bot_account, get_chat_type_and_target_info, is_bot_self
|
||||
from src.prompt.prompt_manager import prompt_manager
|
||||
@@ -27,13 +26,13 @@ from src.services.message_service import (
|
||||
replace_user_references,
|
||||
translate_pid_to_description,
|
||||
)
|
||||
from src.bw_learner.expression_selector import expression_selector
|
||||
from src.learners.expression_selector import expression_selector
|
||||
|
||||
# from src.memory_system.memory_activator import MemoryActivator
|
||||
from src.person_info.person_info import Person, is_person_known
|
||||
from src.core.types import ActionInfo, EventType
|
||||
from src.memory_system.memory_retrieval import init_memory_retrieval_sys, build_memory_retrieval_prompt
|
||||
from src.bw_learner.jargon_explainer_old import explain_jargon_in_context
|
||||
from src.learners.jargon_explainer_old import explain_jargon_in_context
|
||||
|
||||
init_memory_retrieval_sys()
|
||||
|
||||
@@ -47,10 +46,15 @@ class PrivateReplyer:
|
||||
chat_stream: BotChatSession,
|
||||
request_type: str = "replyer",
|
||||
):
|
||||
"""初始化私聊回复器。
|
||||
|
||||
Args:
|
||||
chat_stream: 当前绑定的聊天会话。
|
||||
request_type: LLM 请求类型标识。
|
||||
"""
|
||||
self.express_model = LLMRequest(model_set=model_config.model_task_config.replyer, request_type=request_type)
|
||||
self.chat_stream = chat_stream
|
||||
self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.session_id)
|
||||
self.heart_fc_sender = UniversalMessageSender()
|
||||
# self.memory_activator = MemoryActivator()
|
||||
|
||||
from src.chat.tool_executor import ToolExecutor
|
||||
@@ -970,7 +974,9 @@ class PrivateReplyer:
|
||||
user_nickname=global_config.bot.nickname,
|
||||
),
|
||||
group_info=None,
|
||||
additional_config={},
|
||||
additional_config={
|
||||
"platform_io_target_user_id": self.chat_stream.user_id,
|
||||
},
|
||||
),
|
||||
message_segment=message_segment,
|
||||
)
|
||||
|
||||
@@ -1,22 +1,20 @@
|
||||
"""
|
||||
工具执行器
|
||||
"""工具执行器。
|
||||
|
||||
独立的工具执行组件，可以直接输入聊天消息内容，
|
||||
自动判断并执行相应的工具，返回结构化的工具执行结果。
|
||||
|
||||
从 src.plugin_system.core.tool_use 迁移，使用新的核心组件注册表。
|
||||
"""
|
||||
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import hashlib
|
||||
import time
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
from src.common.logger import get_logger
|
||||
from src.config.config import global_config, model_config
|
||||
from src.core.announcement_manager import global_announcement_manager
|
||||
from src.core.component_registry import component_registry
|
||||
from src.llm_models.payload_content import ToolCall
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.plugin_runtime.component_query import component_query_service
|
||||
from src.prompt.prompt_manager import prompt_manager
|
||||
|
||||
logger = get_logger("tool_use")
|
||||
@@ -89,7 +87,7 @@ class ToolExecutor:
|
||||
|
||||
def _get_tool_definitions(self) -> List[Dict[str, Any]]:
|
||||
"""获取 LLM 可用的工具定义列表"""
|
||||
all_tools = component_registry.get_llm_available_tools()
|
||||
all_tools = component_query_service.get_llm_available_tools()
|
||||
user_disabled_tools = global_announcement_manager.get_disabled_chat_tools(self.chat_id)
|
||||
return [info.get_llm_definition() for name, info in all_tools.items() if name not in user_disabled_tools]
|
||||
|
||||
@@ -152,7 +150,7 @@ class ToolExecutor:
|
||||
function_args = tool_call.args or {}
|
||||
function_args["llm_called"] = True
|
||||
|
||||
executor = component_registry.get_tool_executor(function_name)
|
||||
executor = component_query_service.get_tool_executor(function_name)
|
||||
if not executor:
|
||||
logger.warning(f"未知工具名称: {function_name}")
|
||||
return None
|
||||
|
||||
@@ -436,14 +436,14 @@ class StatisticOutputTask(AsyncTask):
|
||||
|
||||
@staticmethod
|
||||
def _fetch_online_time_since(query_start_time: datetime) -> list[tuple[datetime, datetime]]:
|
||||
with get_db_session() as session:
|
||||
with get_db_session(auto_commit=False) as session:
|
||||
statement = select(OnlineTime).where(col(OnlineTime.end_timestamp) >= query_start_time)
|
||||
records = session.exec(statement).all()
|
||||
return [(record.start_timestamp, record.end_timestamp) for record in records]
|
||||
|
||||
@staticmethod
|
||||
def _fetch_model_usage_since(query_start_time: datetime) -> list[dict[str, object]]:
|
||||
with get_db_session() as session:
|
||||
with get_db_session(auto_commit=False) as session:
|
||||
statement = select(ModelUsage).where(col(ModelUsage.timestamp) >= query_start_time)
|
||||
records = session.exec(statement).all()
|
||||
return [
|
||||
@@ -664,7 +664,7 @@ class StatisticOutputTask(AsyncTask):
|
||||
}
|
||||
|
||||
query_start_timestamp = collect_period[-1][1]
|
||||
with get_db_session() as session:
|
||||
with get_db_session(auto_commit=False) as session:
|
||||
statement = select(Messages).where(col(Messages.timestamp) >= query_start_timestamp)
|
||||
messages = session.exec(statement).all()
|
||||
for message in messages:
|
||||
@@ -713,7 +713,7 @@ class StatisticOutputTask(AsyncTask):
|
||||
# 使用 ActionRecords 中的 reply 动作次数作为回复数基准
|
||||
try:
|
||||
action_query_start_timestamp = collect_period[-1][1]
|
||||
with get_db_session() as session:
|
||||
with get_db_session(auto_commit=False) as session:
|
||||
statement = select(ActionRecord).where(col(ActionRecord.timestamp) >= action_query_start_timestamp)
|
||||
actions = session.exec(statement).all()
|
||||
for action in actions:
|
||||
@@ -1750,7 +1750,7 @@ class StatisticOutputTask(AsyncTask):
|
||||
|
||||
# 查询消息记录
|
||||
query_start_timestamp = start_time.timestamp()
|
||||
with get_db_session() as session:
|
||||
with get_db_session(auto_commit=False) as session:
|
||||
statement = select(Messages).where(col(Messages.timestamp) >= start_time)
|
||||
messages = session.exec(statement).all()
|
||||
for message in messages:
|
||||
@@ -2131,7 +2131,7 @@ class StatisticOutputTask(AsyncTask):
|
||||
|
||||
# 查询消息记录
|
||||
query_start_timestamp = start_time.timestamp()
|
||||
with get_db_session() as session:
|
||||
with get_db_session(auto_commit=False) as session:
|
||||
statement = select(Messages).where(col(Messages.timestamp) >= start_time)
|
||||
messages = session.exec(statement).all()
|
||||
for message in messages:
|
||||
|
||||
Reference in New Issue
Block a user