MaiBot/src/chat/planner_actions/planner.py

import json
import time
import traceback
import random
import re
from typing import Dict, Optional, Tuple, List, TYPE_CHECKING, Union
from rich.traceback import install
from datetime import datetime
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 src.common.data_models.info_data_model import ActionPlannerInfo
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
from src.chat.utils.chat_message_builder import (
    build_readable_messages_with_id,
    get_raw_msg_before_timestamp_with_chat,
    replace_user_references,
)
from src.chat.utils.utils import get_chat_type_and_target_info
from src.chat.planner_actions.action_manager import ActionManager
from src.chat.message_receive.chat_stream import get_chat_manager
from src.plugin_system.base.component_types import ActionInfo, ComponentType, ActionActivationType
from src.plugin_system.core.component_registry import component_registry
from src.plugin_system.apis.message_api import translate_pid_to_description
from src.person_info.person_info import Person

if TYPE_CHECKING:
    from src.common.data_models.info_data_model import TargetPersonInfo
    from src.common.data_models.database_data_model import DatabaseMessages

logger = get_logger("planner")

install(extra_lines=3)


def init_prompt():
    Prompt(
        """
{time_block}
{name_block}
{chat_context_description}，以下是具体的聊天内容
**聊天内容**
{chat_content_block}

**可选的action**
reply
动作描述：
1.你可以选择呼叫了你的名字，但是你没有做出回应的消息进行回复
2.你可以自然的顺着正在进行的聊天内容进行回复或自然的提出一个问题
3.最好一次对一个话题进行回复，免得啰嗦或者回复内容太乱。
4.不要选择回复你自己发送的消息
5.不要单独对表情包进行回复
{reply_action_example}

no_reply
动作描述：
保持沉默，不回复直到有新消息
控制聊天频率，不要太过频繁的发言
{{"action":"no_reply"}}

{action_options_text}

**你之前的action执行和思考记录**
{actions_before_now_block}

请选择**可选的**且符合使用条件的action，并说明触发action的消息id(消息id格式:m+数字)
先输出你的简短的选择思考理由，再输出你选择的action，理由不要分点，精简。
**动作选择要求**
请你根据聊天内容,用户的最新消息和以下标准选择合适的动作:
{plan_style}
{moderation_prompt}

target_message_id为必填，表示触发消息的id
请选择所有符合使用要求的action，每个动作最多选择一次，但是可以选择多个动作；
动作用json格式输出，用```json包裹，如果输出多个json，每个json都要单独一行放在同一个```json代码块内:
**示例**
// 理由文本（简短）
```json
{{"action":"动作名", "target_message_id":"m123", .....}}
{{"action":"动作名", "target_message_id":"m456", .....}}
```""",
        "planner_prompt",
    )

    Prompt(
        """
{action_name}
动作描述：{action_description}
使用条件{parallel_text}：
{action_require}
{{"action":"{action_name}",{action_parameters}, "target_message_id":"消息id(m+数字)"}}
""",
        "action_prompt",
    )


class ActionPlanner:
    def __init__(self, chat_id: str, action_manager: ActionManager):
        self.chat_id = chat_id
        self.log_prefix = f"[{get_chat_manager().get_stream_name(chat_id) or chat_id}]"
        self.action_manager = action_manager
        # LLM规划器配置
        self.planner_llm = LLMRequest(
            model_set=model_config.model_task_config.planner, request_type="planner"
        )  # 用于动作规划

        self.last_obs_time_mark = 0.0

        self.plan_log: List[Tuple[str, float, Union[List[ActionPlannerInfo], str]]] = []

    def find_message_by_id(
        self, message_id: str, message_id_list: List[Tuple[str, "DatabaseMessages"]]
    ) -> Optional["DatabaseMessages"]:
        # sourcery skip: use-next
        """
        根据message_id从message_id_list中查找对应的原始消息

        Args:
            message_id: 要查找的消息ID
            message_id_list: 消息ID列表，格式为[{'id': str, 'message': dict}, ...]

        Returns:
            找到的原始消息字典，如果未找到则返回None
        """
        for item in message_id_list:
            if item[0] == message_id:
                return item[1]
        return None

    def _replace_message_ids_with_text(
        self, text: Optional[str], message_id_list: List[Tuple[str, "DatabaseMessages"]]
    ) -> Optional[str]:
        """将文本中的 m+数字 消息ID替换为原消息内容，并添加双引号"""
        if not text:
            return text

        id_to_message = {msg_id: msg for msg_id, msg in message_id_list}

        # 匹配m后带2-4位数字，前后不是字母数字下划线
        pattern = r"(?<![A-Za-z0-9_])m\d{2,4}(?![A-Za-z0-9_])"

        matches = re.findall(pattern, text)
        if matches:
            available_ids = set(id_to_message.keys())
            found_ids = set(matches)
            missing_ids = found_ids - available_ids
            if missing_ids:
                logger.info(
                    f"{self.log_prefix}planner理由中引用的消息ID不在当前上下文中: {missing_ids}, 可用ID: {list(available_ids)[:10]}..."
                )
            logger.info(
                f"{self.log_prefix}planner理由替换: 找到{len(matches)}个消息ID引用，其中{len(found_ids & available_ids)}个在上下文中"
            )

        def _replace(match: re.Match[str]) -> str:
            msg_id = match.group(0)
            message = id_to_message.get(msg_id)
            if not message:
                logger.warning(f"{self.log_prefix}planner理由引用 {msg_id} 未找到对应消息，保持原样")
                return msg_id

            msg_text = (message.processed_plain_text or "").strip()
            if not msg_text:
                logger.warning(f"{self.log_prefix}planner理由引用 {msg_id} 的消息内容为空，保持原样")
                return msg_id

            # 替换 [picid:xxx] 为 [图片：描述]
            pic_pattern = r"\[picid:([^\]]+)\]"
            def replace_pic_id(pic_match: re.Match) -> str:
                pic_id = pic_match.group(1)
                description = translate_pid_to_description(pic_id)
                return f"[图片：{description}]"
            msg_text = re.sub(pic_pattern, replace_pic_id, msg_text)

            # 替换用户引用格式：回复<aaa:bbb> 和 @<aaa:bbb>
            platform = getattr(message, "user_info", None) and message.user_info.platform or getattr(message, "chat_info", None) and message.chat_info.platform or "qq"
            msg_text = replace_user_references(msg_text, platform, replace_bot_name=True)

            # 替换单独的 <用户名:用户ID> 格式（replace_user_references 已处理回复<和@<格式）
            # 匹配所有 <aaa:bbb> 格式，由于 replace_user_references 已经替换了回复<和@<格式，
            # 这里匹配到的应该都是单独的格式
            user_ref_pattern = r"<([^:<>]+):([^:<>]+)>"
            def replace_user_ref(user_match: re.Match) -> str:
                user_name = user_match.group(1)
                user_id = user_match.group(2)
                try:
                    # 检查是否是机器人自己
                    if user_id == global_config.bot.qq_account:
                        return f"{global_config.bot.nickname}(你)"
                    person = Person(platform=platform, user_id=user_id)
                    return person.person_name or user_name
                except Exception:
                    # 如果解析失败，使用原始昵称
                    return user_name
            msg_text = re.sub(user_ref_pattern, replace_user_ref, msg_text)

            preview = msg_text if len(msg_text) <= 100 else f"{msg_text[:97]}..."
            logger.info(f"{self.log_prefix}planner理由引用 {msg_id} -> 消息（{preview}）")
            return f"消息（{msg_text}）"

        return re.sub(pattern, _replace, text)

    def _parse_single_action(
        self,
        action_json: dict,
        message_id_list: List[Tuple[str, "DatabaseMessages"]],
        current_available_actions: List[Tuple[str, ActionInfo]],
        extracted_reasoning: str = "",
    ) -> List[ActionPlannerInfo]:
        """解析单个action JSON并返回ActionPlannerInfo列表"""
        action_planner_infos = []

        try:
            action = action_json.get("action", "no_reply")
            # 使用 extracted_reasoning（整体推理文本）作为 reasoning
            if extracted_reasoning:
                reasoning = self._replace_message_ids_with_text(extracted_reasoning, message_id_list)
                if reasoning is None:
                    reasoning = extracted_reasoning
            else:
                reasoning = "未提供原因"
            action_data = {key: value for key, value in action_json.items() if key not in ["action"]}
            # 非no_reply动作需要target_message_id
            target_message = None

            target_message_id = action_json.get("target_message_id")
            if target_message_id:
                # 根据target_message_id查找原始消息
                target_message = self.find_message_by_id(target_message_id, message_id_list)
                if target_message is None:
                    logger.warning(f"{self.log_prefix}无法找到target_message_id '{target_message_id}' 对应的消息")
                    # 选择最新消息作为target_message
                    target_message = message_id_list[-1][1]
            else:
                target_message = message_id_list[-1][1]
                logger.debug(f"{self.log_prefix}动作'{action}'缺少target_message_id，使用最新消息作为target_message")

            if action != "no_reply" and target_message is not None and self._is_message_from_self(target_message):
                logger.info(
                    f"{self.log_prefix}Planner选择了自己的消息 {target_message_id or target_message.message_id} 作为目标，强制使用 no_reply"
                )
                reasoning = f"目标消息 {target_message_id or target_message.message_id} 来自机器人自身，违反不回复自身消息规则。原始理由: {reasoning}"
                action = "no_reply"
                target_message = None

            # 验证action是否可用
            available_action_names = [action_name for action_name, _ in current_available_actions]
            internal_action_names = ["no_reply", "reply", "wait_time"]

            if action not in internal_action_names and action not in available_action_names:
                logger.warning(
                    f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (可用: {available_action_names})，将强制使用 'no_reply'"
                )
                reasoning = (
                    f"LLM 返回了当前不可用的动作 '{action}' (可用: {available_action_names})。原始理由: {reasoning}"
                )
                action = "no_reply"

            # 创建ActionPlannerInfo对象
            # 将列表转换为字典格式
            available_actions_dict = dict(current_available_actions)
            action_planner_infos.append(
                ActionPlannerInfo(
                    action_type=action,
                    reasoning=reasoning,
                    action_data=action_data,
                    action_message=target_message,
                    available_actions=available_actions_dict,
                    action_reasoning=extracted_reasoning if extracted_reasoning else None,
                )
            )

        except Exception as e:
            logger.error(f"{self.log_prefix}解析单个action时出错: {e}")
            # 将列表转换为字典格式
            available_actions_dict = dict(current_available_actions)
            action_planner_infos.append(
                ActionPlannerInfo(
                    action_type="no_reply",
                    reasoning=f"解析单个action时出错: {e}",
                    action_data={},
                    action_message=None,
                    available_actions=available_actions_dict,
                    action_reasoning=extracted_reasoning if extracted_reasoning else None,
                )
            )

        return action_planner_infos

    def _is_message_from_self(self, message: "DatabaseMessages") -> bool:
        """判断消息是否由机器人自身发送"""
        try:
            return str(message.user_info.user_id) == str(global_config.bot.qq_account) and (
                message.user_info.platform or ""
            ) == (global_config.bot.platform or "")
        except AttributeError:
            logger.warning(f"{self.log_prefix}检测消息发送者失败，缺少必要字段")
            return False

    async def plan(
        self,
        available_actions: Dict[str, ActionInfo],
        loop_start_time: float = 0.0,
        force_reply_message: Optional["DatabaseMessages"] = None,
    ) -> List[ActionPlannerInfo]:
        # sourcery skip: use-named-expression
        """
        规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
        """

        # 获取聊天上下文
        message_list_before_now = get_raw_msg_before_timestamp_with_chat(
            chat_id=self.chat_id,
            timestamp=time.time(),
            limit=int(global_config.chat.max_context_size * 0.6),
            filter_intercept_message_level=1,
        )
        message_id_list: list[Tuple[str, "DatabaseMessages"]] = []
        chat_content_block, message_id_list = build_readable_messages_with_id(
            messages=message_list_before_now,
            timestamp_mode="normal_no_YMD",
            read_mark=self.last_obs_time_mark,
            truncate=True,
            show_actions=True,
        )

        message_list_before_now_short = message_list_before_now[-int(global_config.chat.max_context_size * 0.3) :]
        chat_content_block_short, message_id_list_short = build_readable_messages_with_id(
            messages=message_list_before_now_short,
            timestamp_mode="normal_no_YMD",
            truncate=False,
            show_actions=False,
        )

        self.last_obs_time_mark = time.time()

        # 获取必要信息
        is_group_chat, chat_target_info, current_available_actions = self.get_necessary_info()

        # 应用激活类型过滤
        filtered_actions = self._filter_actions_by_activation_type(available_actions, chat_content_block_short)

        logger.debug(f"{self.log_prefix}过滤后有{len(filtered_actions)}个可用动作")

        # 构建包含所有动作的提示词
        prompt, message_id_list = await self.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,
        )

        # 调用LLM获取决策
        reasoning, actions = await self._execute_main_planner(
            prompt=prompt,
            message_id_list=message_id_list,
            filtered_actions=filtered_actions,
            available_actions=available_actions,
            loop_start_time=loop_start_time,
        )

        # 如果有强制回复消息，确保回复该消息
        if force_reply_message:
            # 检查是否已经有回复该消息的 action
            has_reply_to_force_message = False
            for action in actions:
                if action.action_type == "reply" and action.action_message and action.action_message.message_id == force_reply_message.message_id:
                    has_reply_to_force_message = True
                    break
            
            # 如果没有回复该消息，强制添加回复 action
            if not has_reply_to_force_message:
                # 移除所有 no_reply action（如果有）
                actions = [a for a in actions if a.action_type != "no_reply"]
                
                # 创建强制回复 action
                available_actions_dict = dict(current_available_actions)
                force_reply_action = ActionPlannerInfo(
                    action_type="reply",
                    reasoning="用户提及了我，必须回复该消息",
                    action_data={"loop_start_time": loop_start_time},
                    action_message=force_reply_message,
                    available_actions=available_actions_dict,
                    action_reasoning=None,
                )
                # 将强制回复 action 放在最前面
                actions.insert(0, force_reply_action)
                logger.info(f"{self.log_prefix} 检测到强制回复消息，已添加回复动作")

        logger.info(
            f"{self.log_prefix}Planner:{reasoning}。选择了{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
        )

        self.add_plan_log(reasoning, actions)

        return actions

    def add_plan_log(self, reasoning: str, actions: List[ActionPlannerInfo]):
        self.plan_log.append((reasoning, time.time(), actions))
        if len(self.plan_log) > 20:
            self.plan_log.pop(0)

    def add_plan_excute_log(self, result: str):
        self.plan_log.append(("", time.time(), result))
        if len(self.plan_log) > 20:
            self.plan_log.pop(0)

    def get_plan_log_str(self, max_action_records: int = 2, max_execution_records: int = 5) -> str:
        """
        获取计划日志字符串

        Args:
            max_action_records: 显示多少条最新的action记录，默认2
            max_execution_records: 显示多少条最新执行结果记录，默认8

        Returns:
            格式化的日志字符串
        """
        action_records = []
        execution_records = []

        # 从后往前遍历，收集最新的记录
        for reasoning, timestamp, content in reversed(self.plan_log):
            if isinstance(content, list) and all(isinstance(action, ActionPlannerInfo) for action in content):
                # 这是action记录
                if len(action_records) < max_action_records:
                    action_records.append((reasoning, timestamp, content, "action"))
            else:
                # 这是执行结果记录
                if len(execution_records) < max_execution_records:
                    execution_records.append((reasoning, timestamp, content, "execution"))

        # 合并所有记录并按时间戳排序
        all_records = action_records + execution_records
        all_records.sort(key=lambda x: x[1])  # 按时间戳排序

        plan_log_str = ""

        # 按时间顺序添加所有记录
        for reasoning, timestamp, content, record_type in all_records:
            time_str = datetime.fromtimestamp(timestamp).strftime("%H:%M:%S")
            if record_type == "action":
                # plan_log_str += f"{time_str}:{reasoning}|你使用了{','.join([action.action_type for action in content])}\n"
                plan_log_str += f"{time_str}:{reasoning}\n"
            else:
                plan_log_str += f"{time_str}:你执行了action:{content}\n"

        return plan_log_str

    async def build_planner_prompt(
        self,
        is_group_chat: bool,
        chat_target_info: Optional["TargetPersonInfo"],
        current_available_actions: Dict[str, ActionInfo],
        message_id_list: List[Tuple[str, "DatabaseMessages"]],
        chat_content_block: str = "",
        interest: str = "",
    ) -> tuple[str, List[Tuple[str, "DatabaseMessages"]]]:
        """构建 Planner LLM 的提示词 (获取模板并填充数据)"""
        try:
            actions_before_now_block = self.get_plan_log_str()

            # 构建聊天上下文描述
            chat_context_description = "你现在正在一个群聊中"

            # 构建动作选项块
            action_options_block = await self._build_action_options_block(current_available_actions)

            # 其他信息
            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。"
            time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
            bot_name = global_config.bot.nickname
            bot_nickname = (
                f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
            )
            name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。"

            # 根据 think_mode 配置决定 reply action 的示例 JSON
            if global_config.chat.think_mode == "classic":
                reply_action_example = '{{"action":"reply", "target_messamge_id":"消息id(m+数字)"}}'
            else:
                reply_action_example = '5.think_level表示思考深度，0表示该回复不需要思考和回忆，1表示该回复需要进行回忆和思考\n{{"action":"reply", "think_level":数值等级(0或1), "target_messamge_id":"消息id(m+数字)"}}'

            planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
            prompt = planner_prompt_template.format(
                time_block=time_block,
                chat_context_description=chat_context_description,
                chat_content_block=chat_content_block,
                actions_before_now_block=actions_before_now_block,
                action_options_text=action_options_block,
                moderation_prompt=moderation_prompt_block,
                name_block=name_block,
                interest=interest,
                plan_style=global_config.personality.plan_style,
                reply_action_example=reply_action_example,
            )

            return prompt, message_id_list
        except Exception as e:
            logger.error(f"构建 Planner 提示词时出错: {e}")
            logger.error(traceback.format_exc())
            return "构建 Planner Prompt 时出错", []

    def get_necessary_info(self) -> Tuple[bool, Optional["TargetPersonInfo"], Dict[str, ActionInfo]]:
        """
        获取 Planner 需要的必要信息
        """
        is_group_chat = True
        is_group_chat, chat_target_info = get_chat_type_and_target_info(self.chat_id)
        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}")

        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
            ComponentType.ACTION
        )
        current_available_actions = {}
        for action_name in current_available_actions_dict:
            if action_name in all_registered_actions:
                current_available_actions[action_name] = all_registered_actions[action_name]
            else:
                logger.warning(f"{self.log_prefix}使用中的动作 {action_name} 未在已注册动作中找到")

        return is_group_chat, chat_target_info, current_available_actions

    def _filter_actions_by_activation_type(
        self, available_actions: Dict[str, ActionInfo], chat_content_block: str
    ) -> Dict[str, ActionInfo]:
        """根据激活类型过滤动作"""
        filtered_actions = {}

        for action_name, action_info in available_actions.items():
            if action_info.activation_type == ActionActivationType.NEVER:
                logger.debug(f"{self.log_prefix}动作 {action_name} 设置为 NEVER 激活类型，跳过")
                continue
            elif action_info.activation_type in [ActionActivationType.LLM_JUDGE, ActionActivationType.ALWAYS]:
                filtered_actions[action_name] = action_info
            elif action_info.activation_type == ActionActivationType.RANDOM:
                if random.random() < action_info.random_activation_probability:
                    filtered_actions[action_name] = action_info
            elif action_info.activation_type == ActionActivationType.KEYWORD:
                if action_info.activation_keywords:
                    for keyword in action_info.activation_keywords:
                        if keyword in chat_content_block:
                            filtered_actions[action_name] = action_info
                            break
            else:
                logger.warning(f"{self.log_prefix}未知的激活类型: {action_info.activation_type}，跳过处理")

        return filtered_actions

    async def _build_action_options_block(self, current_available_actions: Dict[str, ActionInfo]) -> str:
        """构建动作选项块"""
        if not current_available_actions:
            return ""

        action_options_block = ""
        for action_name, action_info in current_available_actions.items():
            # 构建参数文本
            param_text = ""
            if action_info.action_parameters:
                param_text = "\n"
                for param_name, param_description in action_info.action_parameters.items():
                    param_text += f'    "{param_name}":"{param_description}"\n'
                param_text = param_text.rstrip("\n")

            # 构建要求文本
            require_text = ""
            for require_item in action_info.action_require:
                require_text += f"- {require_item}\n"
            require_text = require_text.rstrip("\n")

            if not action_info.parallel_action:
                parallel_text = "(当选择这个动作时，请不要选择其他动作)"
            else:
                parallel_text = ""

            # 获取动作提示模板并填充
            using_action_prompt = await global_prompt_manager.get_prompt_async("action_prompt")
            using_action_prompt = using_action_prompt.format(
                action_name=action_name,
                action_description=action_info.description,
                action_parameters=param_text,
                action_require=require_text,
                parallel_text=parallel_text,
            )

            action_options_block += using_action_prompt

        return action_options_block

    async def _execute_main_planner(
        self,
        prompt: str,
        message_id_list: List[Tuple[str, "DatabaseMessages"]],
        filtered_actions: Dict[str, ActionInfo],
        available_actions: Dict[str, ActionInfo],
        loop_start_time: float,
    ) -> Tuple[str, List[ActionPlannerInfo]]:
        """执行主规划器"""
        llm_content = None
        actions: List[ActionPlannerInfo] = []

        try:
            # 调用LLM
            llm_content, (reasoning_content, _, _) = await self.planner_llm.generate_response_async(prompt=prompt)

            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}")
            return f"LLM 请求失败，模型出现问题: {req_e}", [
                ActionPlannerInfo(
                    action_type="no_reply",
                    reasoning=f"LLM 请求失败，模型出现问题: {req_e}",
                    action_data={},
                    action_message=None,
                    available_actions=available_actions,
                )
            ]

        # 解析LLM响应
        extracted_reasoning = ""
        if llm_content:
            try:
                json_objects, extracted_reasoning = self._extract_json_from_markdown(llm_content)
                extracted_reasoning = self._replace_message_ids_with_text(extracted_reasoning, message_id_list) or ""
                if json_objects:
                    logger.debug(f"{self.log_prefix}从响应中提取到{len(json_objects)}个JSON对象")
                    filtered_actions_list = list(filtered_actions.items())
                    for json_obj in json_objects:
                        actions.extend(
                            self._parse_single_action(
                                json_obj, message_id_list, filtered_actions_list, extracted_reasoning
                            )
                        )
                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 = "LLM没有返回可用动作"
                    actions = self._create_no_reply("LLM没有返回可用动作", 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_no_reply(f"解析LLM响应JSON失败: {json_e}", available_actions)
                traceback.print_exc()
        else:
            extracted_reasoning = "规划器没有获得LLM响应"
            actions = self._create_no_reply("规划器没有获得LLM响应", available_actions)

        # 添加循环开始时间到所有非no_reply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}规划器选择了{len(actions)}个动作: {' '.join([a.action_type for a in actions])}")

        return extracted_reasoning, actions

    def _create_no_reply(self, reasoning: str, available_actions: Dict[str, ActionInfo]) -> List[ActionPlannerInfo]:
        """创建no_reply"""
        return [
            ActionPlannerInfo(
                action_type="no_reply",
                reasoning=reasoning,
                action_data={},
                action_message=None,
                available_actions=available_actions,
            )
        ]

    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():
                    # 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:
                            # 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:
                            # 如果单行解析失败，尝试将整个块作为一个JSON对象或数组
                            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:
                        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.warning(f"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


init_prompt()