mirror of https://github.com/Mai-with-u/MaiBot.git
613 lines
29 KiB
Python
613 lines
29 KiB
Python
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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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.common.data_models.info_data_model import ActionPlannerInfo
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from src.prompt.prompt_manager import prompt_manager
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from src.chat.utils.chat_message_builder import (
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build_readable_actions,
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get_actions_by_timestamp_with_chat,
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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 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_stream import get_chat_manager
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from src.plugin_system.base.component_types import ActionInfo, ComponentType, ActionActivationType
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from src.plugin_system.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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from src.common.data_models.database_data_model import DatabaseMessages
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logger = get_logger("planner")
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install(extra_lines=3)
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class BrainPlanner:
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def __init__(self, chat_id: str, action_manager: ActionManager):
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self.chat_id = chat_id
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self.log_prefix = f"[{get_chat_manager().get_stream_name(chat_id) or chat_id}]"
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self.action_manager = action_manager
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# LLM规划器配置
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self.planner_llm = LLMRequest(
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model_set=model_config.model_task_config.planner, request_type="planner"
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) # 用于动作规划
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self.last_obs_time_mark = 0.0
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# 计划日志记录
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self.plan_log: List[Tuple[str, float, List[ActionPlannerInfo]]] = []
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def find_message_by_id(
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self, message_id: str, message_id_list: List[Tuple[str, "DatabaseMessages"]]
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) -> Optional["DatabaseMessages"]:
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# sourcery skip: use-next
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"""
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根据message_id从message_id_list中查找对应的原始消息
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Args:
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message_id: 要查找的消息ID
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message_id_list: 消息ID列表，格式为[{'id': str, 'message': dict}, ...]
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Returns:
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找到的原始消息字典，如果未找到则返回None
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"""
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for item in message_id_list:
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if item[0] == message_id:
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return item[1]
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return None
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def _parse_single_action(
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self,
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action_json: dict,
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message_id_list: List[Tuple[str, "DatabaseMessages"]],
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current_available_actions: List[Tuple[str, ActionInfo]],
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) -> List[ActionPlannerInfo]:
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"""解析单个action JSON并返回ActionPlannerInfo列表"""
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action_planner_infos = []
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try:
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action = action_json.get("action", "complete_talk")
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logger.debug(f"{self.log_prefix}解析动作JSON: action={action}, json={action_json}")
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reasoning = action_json.get("reason", "未提供原因")
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action_data = {key: value for key, value in action_json.items() if key not in ["action", "reason"]}
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# 非complete_talk动作需要target_message_id
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target_message = None
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if target_message_id := action_json.get("target_message_id"):
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# 根据target_message_id查找原始消息
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target_message = self.find_message_by_id(target_message_id, message_id_list)
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if target_message is None:
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logger.warning(f"{self.log_prefix}无法找到target_message_id '{target_message_id}' 对应的消息")
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# 选择最新消息作为target_message
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target_message = message_id_list[-1][1]
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else:
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target_message = message_id_list[-1][1]
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logger.debug(f"{self.log_prefix}动作'{action}'缺少target_message_id，使用最新消息作为target_message")
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# 验证action是否可用
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available_action_names = [action_name for action_name, _ in current_available_actions]
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# 内部保留动作（不依赖插件系统）
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# 注意：listening 已合并到 wait 中，如果遇到 listening 则转换为 wait
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internal_action_names = ["complete_talk", "reply", "wait_time", "wait", "listening"]
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logger.debug(
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f"{self.log_prefix}动作验证: action={action}, internal={internal_action_names}, available={available_action_names}"
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)
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# 将 listening 转换为 wait（向后兼容）
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if action == "listening":
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logger.debug(f"{self.log_prefix}检测到 listening 动作，已合并到 wait，自动转换")
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action = "wait"
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if action not in internal_action_names and action not in available_action_names:
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logger.warning(
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f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (内部动作: {internal_action_names}, 可用插件动作: {available_action_names})，将强制使用 'complete_talk'"
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)
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reasoning = (
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f"LLM 返回了当前不可用的动作 '{action}' (可用: {available_action_names})。原始理由: {reasoning}"
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)
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action = "complete_talk"
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logger.warning(f"{self.log_prefix}动作已转换为 complete_talk")
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# 创建ActionPlannerInfo对象
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# 将列表转换为字典格式
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available_actions_dict = dict(current_available_actions)
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action_planner_infos.append(
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ActionPlannerInfo(
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action_type=action,
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reasoning=reasoning,
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action_data=action_data,
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action_message=target_message,
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available_actions=available_actions_dict,
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)
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)
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except Exception as e:
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logger.error(f"{self.log_prefix}解析单个action时出错: {e}")
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# 将列表转换为字典格式
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available_actions_dict = dict(current_available_actions)
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action_planner_infos.append(
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ActionPlannerInfo(
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action_type="complete_talk",
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reasoning=f"解析单个action时出错: {e}",
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action_data={},
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action_message=None,
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available_actions=available_actions_dict,
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)
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)
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return action_planner_infos
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async def plan(
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self,
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available_actions: Dict[str, ActionInfo],
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loop_start_time: float = 0.0,
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) -> List[ActionPlannerInfo]:
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# sourcery skip: use-named-expression
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"""
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规划器 (Planner): 使用LLM根据上下文决定做出什么动作（ReAct模式）。
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"""
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plan_start = time.perf_counter()
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# 获取聊天上下文
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message_list_before_now = get_raw_msg_before_timestamp_with_chat(
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chat_id=self.chat_id,
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timestamp=time.time(),
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limit=int(global_config.chat.max_context_size * 0.6),
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filter_intercept_message_level=1,
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)
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message_id_list: list[Tuple[str, "DatabaseMessages"]] = []
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chat_content_block, message_id_list = build_readable_messages_with_id(
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messages=message_list_before_now,
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timestamp_mode="normal_no_YMD",
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read_mark=self.last_obs_time_mark,
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truncate=True,
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show_actions=True,
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)
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message_list_before_now_short = message_list_before_now[-int(global_config.chat.max_context_size * 0.3) :]
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chat_content_block_short, message_id_list_short = build_readable_messages_with_id(
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messages=message_list_before_now_short,
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timestamp_mode="normal_no_YMD",
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truncate=False,
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show_actions=False,
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)
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self.last_obs_time_mark = time.time()
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# 获取必要信息
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is_group_chat, chat_target_info, current_available_actions = self.get_necessary_info()
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# 提及/被@ 的处理由心流或统一判定模块驱动；Planner 不再做硬编码强制回复
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# 应用激活类型过滤
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filtered_actions = self._filter_actions_by_activation_type(available_actions, chat_content_block_short)
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logger.debug(f"{self.log_prefix}过滤后有{len(filtered_actions)}个可用动作")
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prompt_build_start = time.perf_counter()
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# 构建包含所有动作的提示词：使用统一的 ReAct Prompt
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prompt_key = "brain_planner"
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# 这里不记录日志，避免重复打印，由调用方按需控制 log_prompt
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prompt, message_id_list = await self.build_planner_prompt(
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chat_target_info=chat_target_info,
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current_available_actions=filtered_actions,
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chat_content_block=chat_content_block,
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message_id_list=message_id_list,
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prompt_key=prompt_key,
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)
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prompt_build_ms = (time.perf_counter() - prompt_build_start) * 1000
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# 调用LLM获取决策
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reasoning, actions, llm_raw_output, llm_reasoning, llm_duration_ms = await self._execute_main_planner(
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prompt=prompt,
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message_id_list=message_id_list,
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filtered_actions=filtered_actions,
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available_actions=available_actions,
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loop_start_time=loop_start_time,
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)
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# 记录和展示计划日志
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logger.info(
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f"{self.log_prefix}Planner: {reasoning}。选择了{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
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)
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self.add_plan_log(reasoning, actions)
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try:
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PlanReplyLogger.log_plan(
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chat_id=self.chat_id,
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prompt=prompt,
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reasoning=reasoning,
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raw_output=llm_raw_output,
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raw_reasoning=llm_reasoning,
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actions=actions,
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timing={
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"prompt_build_ms": round(prompt_build_ms, 2),
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"llm_duration_ms": round(llm_duration_ms, 2) if llm_duration_ms is not None else None,
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"total_plan_ms": round((time.perf_counter() - plan_start) * 1000, 2),
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"loop_start_time": loop_start_time,
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},
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extra=None,
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)
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except Exception:
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logger.exception(f"{self.log_prefix}记录plan日志失败")
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return actions
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async def build_planner_prompt(
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self,
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chat_target_info: Optional["TargetPersonInfo"],
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current_available_actions: Dict[str, ActionInfo],
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message_id_list: List[Tuple[str, "DatabaseMessages"]],
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chat_content_block: str = "",
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interest: str = "",
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prompt_key: str = "brain_planner",
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) -> tuple[str, List[Tuple[str, "DatabaseMessages"]]]:
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"""构建 Planner LLM 的提示词 (获取模板并填充数据)"""
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try:
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# 获取最近执行过的动作
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actions_before_now = get_actions_by_timestamp_with_chat(
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chat_id=self.chat_id,
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timestamp_start=time.time() - 600,
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timestamp_end=time.time(),
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limit=6,
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)
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actions_before_now_block = build_readable_actions(actions=actions_before_now)
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if actions_before_now_block:
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actions_before_now_block = f"你刚刚选择并执行过的action是：\n{actions_before_now_block}"
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else:
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actions_before_now_block = ""
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chat_context_description: str = ""
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if chat_target_info:
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# 构建聊天上下文描述
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chat_context_description = (
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f"你正在和 {chat_target_info.person_name or chat_target_info.user_nickname or '对方'} 聊天中"
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)
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# 构建动作选项块
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action_options_block = await self._build_action_options_block(current_available_actions)
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# 其他信息
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moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
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time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
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bot_name = global_config.bot.nickname
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bot_nickname = (
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f",也可以叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
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)
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name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。"
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# 获取主规划器模板并填充
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planner_prompt_template = prompt_manager.get_prompt(prompt_key)
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planner_prompt_template.add_context("time_block", time_block)
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planner_prompt_template.add_context("chat_context_description", chat_context_description)
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planner_prompt_template.add_context("chat_content_block", chat_content_block)
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planner_prompt_template.add_context("actions_before_now_block", actions_before_now_block)
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planner_prompt_template.add_context("action_options_text", action_options_block)
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planner_prompt_template.add_context("moderation_prompt", moderation_prompt_block)
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planner_prompt_template.add_context("name_block", name_block)
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planner_prompt_template.add_context("interest", interest)
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planner_prompt_template.add_context("plan_style", global_config.experimental.private_plan_style)
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prompt = await prompt_manager.render_prompt(planner_prompt_template)
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return prompt, message_id_list
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except Exception as e:
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logger.error(f"构建 Planner 提示词时出错: {e}")
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logger.error(traceback.format_exc())
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return "构建 Planner Prompt 时出错", []
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def get_necessary_info(self) -> Tuple[bool, Optional["TargetPersonInfo"], Dict[str, ActionInfo]]:
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"""
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获取 Planner 需要的必要信息
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"""
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is_group_chat = True
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is_group_chat, chat_target_info = get_chat_type_and_target_info(self.chat_id)
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logger.debug(f"{self.log_prefix}获取到聊天信息 - 群聊: {is_group_chat}, 目标信息: {chat_target_info}")
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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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ComponentType.ACTION
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)
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current_available_actions = {}
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for action_name in current_available_actions_dict:
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if action_name in all_registered_actions:
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current_available_actions[action_name] = all_registered_actions[action_name]
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else:
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logger.warning(f"{self.log_prefix}使用中的动作 {action_name} 未在已注册动作中找到")
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return is_group_chat, chat_target_info, current_available_actions
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def _filter_actions_by_activation_type(
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self, available_actions: Dict[str, ActionInfo], chat_content_block: str
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) -> Dict[str, ActionInfo]:
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"""根据激活类型过滤动作"""
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filtered_actions = {}
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for action_name, action_info in available_actions.items():
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if action_info.activation_type == ActionActivationType.NEVER:
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logger.debug(f"{self.log_prefix}动作 {action_name} 设置为 NEVER 激活类型，跳过")
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continue
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elif action_info.activation_type == ActionActivationType.ALWAYS:
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filtered_actions[action_name] = action_info
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elif action_info.activation_type == ActionActivationType.RANDOM:
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if random.random() < action_info.random_activation_probability:
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filtered_actions[action_name] = action_info
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elif action_info.activation_type == ActionActivationType.KEYWORD:
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if action_info.activation_keywords:
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for keyword in action_info.activation_keywords:
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if keyword in chat_content_block:
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filtered_actions[action_name] = action_info
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break
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else:
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logger.warning(f"{self.log_prefix}未知的激活类型: {action_info.activation_type}，跳过处理")
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return filtered_actions
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async def _build_action_options_block(self, current_available_actions: Dict[str, ActionInfo]) -> str:
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# sourcery skip: use-join
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"""构建动作选项块"""
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if not current_available_actions:
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return ""
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action_options_block = ""
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for action_name, action_info in current_available_actions.items():
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# 构建参数文本
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param_text = ""
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if action_info.action_parameters:
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param_text = "\n"
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for param_name, param_description in action_info.action_parameters.items():
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param_text += f' "{param_name}":"{param_description}"\n'
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param_text = param_text.rstrip("\n")
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# 构建要求文本
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require_text = ""
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for require_item in action_info.action_require:
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require_text += f"- {require_item}\n"
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require_text = require_text.rstrip("\n")
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# 获取动作提示模板并填充
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using_action_prompt_template = prompt_manager.get_prompt("brain_action")
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using_action_prompt_template.add_context("action_name", action_name)
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using_action_prompt_template.add_context("action_description", action_info.description)
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using_action_prompt_template.add_context("action_parameters", param_text)
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using_action_prompt_template.add_context("action_require", require_text)
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using_action_prompt = await prompt_manager.render_prompt(using_action_prompt_template)
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action_options_block += using_action_prompt
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return action_options_block
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async def _execute_main_planner(
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self,
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prompt: str,
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message_id_list: List[Tuple[str, "DatabaseMessages"]],
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filtered_actions: Dict[str, ActionInfo],
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available_actions: Dict[str, ActionInfo],
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loop_start_time: float,
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) -> Tuple[str, List[ActionPlannerInfo], Optional[str], Optional[str], Optional[float]]:
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"""执行主规划器"""
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llm_content = None
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actions: List[ActionPlannerInfo] = []
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extracted_reasoning = ""
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llm_reasoning = None
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llm_duration_ms = None
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try:
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# 调用LLM
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llm_start = time.perf_counter()
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llm_content, (reasoning_content, _, _) = await self.planner_llm.generate_response_async(prompt=prompt)
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llm_duration_ms = (time.perf_counter() - llm_start) * 1000
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llm_reasoning = reasoning_content
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|
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logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
|
||
logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
|
||
|
||
if global_config.debug.show_planner_prompt:
|
||
logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
|
||
logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
|
||
if reasoning_content:
|
||
logger.info(f"{self.log_prefix}规划器推理: {reasoning_content}")
|
||
else:
|
||
logger.debug(f"{self.log_prefix}规划器原始提示词: {prompt}")
|
||
logger.debug(f"{self.log_prefix}规划器原始响应: {llm_content}")
|
||
if reasoning_content:
|
||
logger.debug(f"{self.log_prefix}规划器推理: {reasoning_content}")
|
||
|
||
except Exception as req_e:
|
||
logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")
|
||
extracted_reasoning = f"LLM 请求失败，模型出现问题: {req_e}"
|
||
return extracted_reasoning, [
|
||
ActionPlannerInfo(
|
||
action_type="complete_talk",
|
||
reasoning=extracted_reasoning,
|
||
action_data={},
|
||
action_message=None,
|
||
available_actions=available_actions,
|
||
)
|
||
], llm_content, llm_reasoning, llm_duration_ms
|
||
|
||
# 解析LLM响应
|
||
if llm_content:
|
||
try:
|
||
json_objects, extracted_reasoning = self._extract_json_from_markdown(llm_content)
|
||
if json_objects:
|
||
logger.info(f"{self.log_prefix}从响应中提取到{len(json_objects)}个JSON对象")
|
||
for i, json_obj in enumerate(json_objects):
|
||
logger.info(f"{self.log_prefix}解析第{i + 1}个JSON对象: {json_obj}")
|
||
filtered_actions_list = list(filtered_actions.items())
|
||
for json_obj in json_objects:
|
||
parsed_actions = self._parse_single_action(json_obj, message_id_list, filtered_actions_list)
|
||
logger.info(f"{self.log_prefix}解析后的动作: {[a.action_type for a in parsed_actions]}")
|
||
actions.extend(parsed_actions)
|
||
else:
|
||
# 尝试解析为直接的JSON
|
||
logger.warning(f"{self.log_prefix}LLM没有返回可用动作: {llm_content}")
|
||
extracted_reasoning = extracted_reasoning or "LLM没有返回可用动作"
|
||
actions = self._create_complete_talk(extracted_reasoning, available_actions)
|
||
|
||
except Exception as json_e:
|
||
logger.warning(f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'")
|
||
extracted_reasoning = f"解析LLM响应JSON失败: {json_e}"
|
||
actions = self._create_complete_talk(extracted_reasoning, available_actions)
|
||
traceback.print_exc()
|
||
else:
|
||
extracted_reasoning = "规划器没有获得LLM响应"
|
||
actions = self._create_complete_talk(extracted_reasoning, available_actions)
|
||
|
||
# 添加循环开始时间到所有动作
|
||
for action in actions:
|
||
action.action_data = action.action_data or {}
|
||
action.action_data["loop_start_time"] = loop_start_time
|
||
|
||
logger.debug(
|
||
f"{self.log_prefix}规划器决定执行{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
|
||
)
|
||
|
||
return extracted_reasoning, actions, llm_content, llm_reasoning, llm_duration_ms
|
||
|
||
def _create_complete_talk(
|
||
self, reasoning: str, available_actions: Dict[str, ActionInfo]
|
||
) -> List[ActionPlannerInfo]:
|
||
"""创建complete_talk"""
|
||
return [
|
||
ActionPlannerInfo(
|
||
action_type="complete_talk",
|
||
reasoning=reasoning,
|
||
action_data={},
|
||
action_message=None,
|
||
available_actions=available_actions,
|
||
)
|
||
]
|
||
|
||
def add_plan_log(self, reasoning: str, actions: List[ActionPlannerInfo]):
|
||
"""添加计划日志"""
|
||
self.plan_log.append((reasoning, time.time(), actions))
|
||
if len(self.plan_log) > 20:
|
||
self.plan_log.pop(0)
|
||
|
||
def _extract_json_from_markdown(self, content: str) -> Tuple[List[dict], str]:
|
||
# sourcery skip: for-append-to-extend
|
||
"""从Markdown格式的内容中提取JSON对象和推理内容"""
|
||
json_objects = []
|
||
reasoning_content = ""
|
||
|
||
# 使用正则表达式查找```json包裹的JSON内容
|
||
json_pattern = r"```json\s*(.*?)\s*```"
|
||
markdown_matches = re.findall(json_pattern, content, re.DOTALL)
|
||
|
||
# 提取JSON之前的内容作为推理文本
|
||
first_json_pos = len(content)
|
||
if markdown_matches:
|
||
# 找到第一个```json的位置
|
||
first_json_pos = content.find("```json")
|
||
if first_json_pos > 0:
|
||
reasoning_content = content[:first_json_pos].strip()
|
||
# 清理推理内容中的注释标记
|
||
reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE)
|
||
reasoning_content = reasoning_content.strip()
|
||
|
||
# 处理```json包裹的JSON
|
||
for match in markdown_matches:
|
||
try:
|
||
# 清理可能的注释和格式问题
|
||
json_str = re.sub(r"//.*?\n", "\n", match) # 移除单行注释
|
||
json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL) # 移除多行注释
|
||
if json_str := json_str.strip():
|
||
# 先尝试将整个块作为一个JSON对象或数组（适用于多行JSON）
|
||
try:
|
||
json_obj = json.loads(repair_json(json_str))
|
||
if isinstance(json_obj, dict):
|
||
json_objects.append(json_obj)
|
||
elif isinstance(json_obj, list):
|
||
for item in json_obj:
|
||
if isinstance(item, dict):
|
||
json_objects.append(item)
|
||
except json.JSONDecodeError:
|
||
# 如果整个块解析失败，尝试按行分割（适用于多个单行JSON对象）
|
||
lines = [line.strip() for line in json_str.split("\n") if line.strip()]
|
||
for line in lines:
|
||
try:
|
||
# 尝试解析每一行作为独立的JSON对象
|
||
json_obj = json.loads(repair_json(line))
|
||
if isinstance(json_obj, dict):
|
||
json_objects.append(json_obj)
|
||
elif isinstance(json_obj, list):
|
||
for item in json_obj:
|
||
if isinstance(item, dict):
|
||
json_objects.append(item)
|
||
except json.JSONDecodeError:
|
||
# 单行解析失败，继续下一行
|
||
continue
|
||
except Exception as e:
|
||
logger.warning(f"{self.log_prefix}解析JSON块失败: {e}, 块内容: {match[:100]}...")
|
||
continue
|
||
|
||
# 如果没有找到完整的```json```块，尝试查找不完整的代码块（缺少结尾```）
|
||
if not json_objects:
|
||
json_start_pos = content.find("```json")
|
||
if json_start_pos != -1:
|
||
# 找到```json之后的内容
|
||
json_content_start = json_start_pos + 7 # ```json的长度
|
||
# 提取从```json之后到内容结尾的所有内容
|
||
incomplete_json_str = content[json_content_start:].strip()
|
||
|
||
# 提取JSON之前的内容作为推理文本
|
||
if json_start_pos > 0:
|
||
reasoning_content = content[:json_start_pos].strip()
|
||
reasoning_content = re.sub(r"^//\s*", "", reasoning_content, flags=re.MULTILINE)
|
||
reasoning_content = reasoning_content.strip()
|
||
|
||
if incomplete_json_str:
|
||
try:
|
||
# 清理可能的注释和格式问题
|
||
json_str = re.sub(r"//.*?\n", "\n", incomplete_json_str)
|
||
json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL)
|
||
json_str = json_str.strip()
|
||
|
||
if json_str:
|
||
# 尝试按行分割，每行可能是一个JSON对象
|
||
lines = [line.strip() for line in json_str.split("\n") if line.strip()]
|
||
for line in lines:
|
||
try:
|
||
json_obj = json.loads(repair_json(line))
|
||
if isinstance(json_obj, dict):
|
||
json_objects.append(json_obj)
|
||
elif isinstance(json_obj, list):
|
||
for item in json_obj:
|
||
if isinstance(item, dict):
|
||
json_objects.append(item)
|
||
except json.JSONDecodeError:
|
||
pass
|
||
|
||
# 如果按行解析没有成功，尝试将整个块作为一个JSON对象或数组
|
||
if not json_objects:
|
||
try:
|
||
json_obj = json.loads(repair_json(json_str))
|
||
if isinstance(json_obj, dict):
|
||
json_objects.append(json_obj)
|
||
elif isinstance(json_obj, list):
|
||
for item in json_obj:
|
||
if isinstance(item, dict):
|
||
json_objects.append(item)
|
||
except Exception as e:
|
||
logger.debug(f"尝试解析不完整的JSON代码块失败: {e}")
|
||
except Exception as e:
|
||
logger.debug(f"处理不完整的JSON代码块时出错: {e}")
|
||
|
||
return json_objects, reasoning_content
|