feat：优化planner的运作模式（同时保留旧切换选项（未完善））

src/chat/focus_chat/heartFC_chat.py

@@ -24,10 +24,11 @@ from src.chat.heart_flow.observation.structure_observation import StructureObser
 from src.chat.heart_flow.observation.actions_observation import ActionObservation
 from src.chat.focus_chat.info_processors.tool_processor import ToolProcessor
 from src.chat.focus_chat.expressors.default_expressor import DefaultExpressor
+from src.chat.focus_chat.replyer.default_replyer import DefaultReplyer
 from src.chat.focus_chat.memory_activator import MemoryActivator
 from src.chat.focus_chat.info_processors.base_processor import BaseProcessor
 from src.chat.focus_chat.info_processors.self_processor import SelfProcessor
-from src.chat.focus_chat.planners.planner import ActionPlanner
+from src.chat.focus_chat.planners.planner_factory import PlannerFactory
 from src.chat.focus_chat.planners.modify_actions import ActionModifier
 from src.chat.focus_chat.planners.action_manager import ActionManager
 from src.chat.focus_chat.working_memory.working_memory import WorkingMemory
@@ -119,8 +120,9 @@ class HeartFChatting:
         self._register_default_processors()
 
         self.expressor = DefaultExpressor(chat_id=self.stream_id)
+        self.replyer = DefaultReplyer(chat_id=self.stream_id)
         self.action_manager = ActionManager()
-        self.action_planner = ActionPlanner(log_prefix=self.log_prefix, action_manager=self.action_manager)
+        self.action_planner = PlannerFactory.create_planner(log_prefix=self.log_prefix, action_manager=self.action_manager)
         self.action_modifier = ActionModifier(action_manager=self.action_manager)
         self.action_observation = ActionObservation(observe_id=self.stream_id)
 
@@ -167,8 +169,10 @@ class HeartFChatting:
 
         try:
             await self.expressor.initialize()
+            await self.replyer.initialize()
             self.chat_stream = await asyncio.to_thread(chat_manager.get_stream, self.stream_id)
             self.expressor.chat_stream = self.chat_stream
+            self.replyer.chat_stream = self.chat_stream
             self.log_prefix = f"[{chat_manager.get_stream_name(self.stream_id) or self.stream_id}]"
         except Exception as e:
             logger.error(f"[HFC:{self.stream_id}] 初始化HFC时发生错误: {e}")
@@ -583,6 +587,7 @@ class HeartFChatting:
                     thinking_id=thinking_id,
                     observations=self.all_observations,
                     expressor=self.expressor,
+                    replyer=self.replyer,
                     chat_stream=self.chat_stream,
                     log_prefix=self.log_prefix,
                     shutting_down=self._shutting_down,

src/chat/focus_chat/memory_activator.py

@@ -118,6 +118,7 @@ class MemoryActivator:
 
         # 只取response的第一个元素（字符串）
         response_str = response[0]
+        print(f"response_str: {response_str[1]}")
         keywords = list(get_keywords_from_json(response_str))
 
         # 更新关键词缓存

src/chat/focus_chat/planners/base_planner.py

@@ -0,0 +1,25 @@
+from abc import ABC, abstractmethod
+from typing import List, Dict, Any
+from src.chat.focus_chat.planners.action_manager import ActionManager
+from src.chat.focus_chat.info.info_base import InfoBase
+
+class BasePlanner(ABC):
+    """规划器基类"""
+    
+    def __init__(self, log_prefix: str, action_manager: ActionManager):
+        self.log_prefix = log_prefix
+        self.action_manager = action_manager
+    
+    @abstractmethod
+    async def plan(self, all_plan_info: List[InfoBase], running_memorys: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """
+        规划下一步行动
+        
+        Args:
+            all_plan_info: 所有计划信息
+            running_memorys: 回忆信息
+            
+        Returns:
+            Dict[str, Any]: 规划结果
+        """
+        pass 

src/chat/focus_chat/planners/planner_complex.py

@@ -16,6 +16,7 @@ from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
 from src.individuality.individuality import individuality
 from src.chat.focus_chat.planners.action_manager import ActionManager
 from json_repair import repair_json
+from src.chat.focus_chat.planners.base_planner import BasePlanner
 
 logger = get_logger("planner")
 
@@ -73,9 +74,9 @@ action_name: {action_name}
     )
 
 
-class ActionPlanner:
+class ActionPlanner(BasePlanner):
     def __init__(self, log_prefix: str, action_manager: ActionManager):
-        self.log_prefix = log_prefix
+        super().__init__(log_prefix, action_manager)
         # LLM规划器配置
         self.planner_llm = LLMRequest(
             model=global_config.model.focus_planner,
@@ -83,8 +84,6 @@ class ActionPlanner:
             request_type="focus.planner",  # 用于动作规划
         )
 
-        self.action_manager = action_manager
-
     async def plan(self, all_plan_info: List[InfoBase], running_memorys: List[Dict[str, Any]]) -> Dict[str, Any]:
         """
         规划器 (Planner): 使用LLM根据上下文决定做出什么动作。

src/chat/focus_chat/planners/planner_factory.py

@@ -0,0 +1,52 @@
+from typing import Dict, Type
+from src.chat.focus_chat.planners.base_planner import BasePlanner
+from src.chat.focus_chat.planners.planner_complex import ActionPlanner as ComplexActionPlanner
+from src.chat.focus_chat.planners.planner_simple import ActionPlanner as SimpleActionPlanner
+from src.chat.focus_chat.planners.action_manager import ActionManager
+from src.config.config import global_config
+from src.common.logger_manager import get_logger
+
+logger = get_logger("planner_factory")
+
+class PlannerFactory:
+    """规划器工厂类，用于创建不同类型的规划器实例"""
+    
+    # 注册所有可用的规划器类型
+    _planner_types: Dict[str, Type[BasePlanner]] = {
+        "complex": ComplexActionPlanner,
+        "simple": SimpleActionPlanner,
+    }
+    
+    @classmethod
+    def register_planner(cls, name: str, planner_class: Type[BasePlanner]) -> None:
+        """
+        注册新的规划器类型
+        
+        Args:
+            name: 规划器类型名称
+            planner_class: 规划器类
+        """
+        cls._planner_types[name] = planner_class
+        logger.info(f"注册新的规划器类型: {name}")
+    
+    @classmethod
+    def create_planner(cls, log_prefix: str, action_manager: ActionManager) -> BasePlanner:
+        """
+        创建规划器实例
+        
+        Args:
+            log_prefix: 日志前缀
+            action_manager: 动作管理器实例
+            
+        Returns:
+            BasePlanner: 规划器实例
+        """
+        planner_type = global_config.focus_chat.planner_type
+        
+        if planner_type not in cls._planner_types:
+            logger.warning(f"{log_prefix} 未知的规划器类型: {planner_type}，使用默认规划器")
+            planner_type = "complex"
+            
+        planner_class = cls._planner_types[planner_type]
+        logger.info(f"{log_prefix} 使用{planner_type}规划器")
+        return planner_class(log_prefix=log_prefix, action_manager=action_manager) 

src/chat/focus_chat/planners/planner_simple.py

@@ -0,0 +1,404 @@
+import json  # <--- 确保导入 json
+import traceback
+from typing import List, Dict, Any, Optional
+from rich.traceback import install
+from src.llm_models.utils_model import LLMRequest
+from src.config.config import global_config
+from src.chat.focus_chat.info.info_base import InfoBase
+from src.chat.focus_chat.info.obs_info import ObsInfo
+from src.chat.focus_chat.info.cycle_info import CycleInfo
+from src.chat.focus_chat.info.mind_info import MindInfo
+from src.chat.focus_chat.info.action_info import ActionInfo
+from src.chat.focus_chat.info.structured_info import StructuredInfo
+from src.chat.focus_chat.info.self_info import SelfInfo
+from src.common.logger_manager import get_logger
+from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
+from src.individuality.individuality import individuality
+from src.chat.focus_chat.planners.action_manager import ActionManager
+from json_repair import repair_json
+from src.chat.focus_chat.planners.base_planner import BasePlanner
+
+logger = get_logger("planner")
+
+install(extra_lines=3)
+
+
+def init_prompt():
+    Prompt(
+        """
+你的自我认知是：
+{self_info_block}
+{extra_info_block}
+{memory_str}
+
+你是群内的一员，你现在正在参与群内的闲聊，以下是群内的聊天内容：
+
+{chat_content_block}
+
+{mind_info_block}
+
+{cycle_info_block}
+注意，除了下面动作选项之外，你在群聊里不能做其他任何事情，这是你能力的边界，现在请你选择合适的action:
+{moderation_prompt}
+
+{action_options_text}
+
+以严格的 JSON 格式输出，且仅包含 JSON 内容，不要有任何其他文字或解释。
+请你以下面格式输出：
+{{
+    "action": "action_name"
+    "reasoning": "理由",
+    "参数": "参数的值"(可能有多个参数),
+}}
+
+请输出你提取的JSON，不要有任何其他文字或解释：
+
+""",
+        "planner_prompt",
+    )
+    
+    Prompt("""
+{raw_output}
+请从上面这段内容中提取出JSON内容，不要有任何其他文字或解释。
+以严格的 JSON 格式输出，且仅包含 JSON 内容，不要有任何其他文字或解释。
+请你以下面格式输出：
+{{
+    "action": "action_name"
+    "参数": "参数的值"(可能有多个参数),
+}}
+
+请输出你提取的JSON，不要有任何其他文字或解释：""",
+    "planner_prompt_json",
+    )
+    
+    
+    
+
+    Prompt(
+        """
+动作名称：{action_name}
+    描述：{action_description}
+    {action_parameters}
+    使用该动作的场景：{action_require}""",
+        "action_prompt",
+    )
+
+
+class ActionPlanner(BasePlanner):
+    def __init__(self, log_prefix: str, action_manager: ActionManager):
+        super().__init__(log_prefix, action_manager)
+        # LLM规划器配置
+        self.planner_llm = LLMRequest(
+            model=global_config.model.focus_planner,
+            max_tokens=1000,
+            request_type="focus.planner",  # 用于动作规划
+        )
+        
+        self.utils_llm = LLMRequest(
+            model=global_config.model.utils_small,
+            max_tokens=1000,
+            request_type="focus.planner",  # 用于动作规划
+        )
+
+    async def plan(self, all_plan_info: List[InfoBase], running_memorys: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """
+        规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
+
+        参数:
+            all_plan_info: 所有计划信息
+            running_memorys: 回忆信息
+        """
+
+        action = "no_reply"  # 默认动作
+        reasoning = "规划器初始化默认"
+        action_data = {}
+
+        try:
+            # 获取观察信息
+            extra_info: list[str] = []
+
+            # 设置默认值
+            nickname_str = ""
+            for nicknames in global_config.bot.alias_names:
+                nickname_str += f"{nicknames},"
+            name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。"
+
+            personality_block = individuality.get_personality_prompt(x_person=2, level=2)
+            identity_block = individuality.get_identity_prompt(x_person=2, level=2)
+
+            self_info = name_block + personality_block + identity_block
+            current_mind = "你思考了很久，没有想清晰要做什么"
+
+            cycle_info = ""
+            structured_info = ""
+            extra_info = []
+            observed_messages = []
+            observed_messages_str = ""
+            chat_type = "group"
+            is_group_chat = True
+            for info in all_plan_info:
+                if isinstance(info, ObsInfo):
+                    observed_messages = info.get_talking_message()
+                    observed_messages_str = info.get_talking_message_str_truncate()
+                    chat_type = info.get_chat_type()
+                    is_group_chat = chat_type == "group"
+                elif isinstance(info, MindInfo):
+                    current_mind = info.get_current_mind()
+                elif isinstance(info, CycleInfo):
+                    cycle_info = info.get_observe_info()
+                elif isinstance(info, SelfInfo):
+                    self_info = info.get_processed_info()
+                elif isinstance(info, StructuredInfo):
+                    structured_info = info.get_processed_info()
+                    # print(f"structured_info: {structured_info}")
+                # elif not isinstance(info, ActionInfo):  # 跳过已处理的ActionInfo
+                # extra_info.append(info.get_processed_info())
+
+            # 获取当前可用的动作
+            current_available_actions = self.action_manager.get_using_actions()
+
+            # 如果没有可用动作或只有no_reply动作，直接返回no_reply
+            if not current_available_actions or (
+                len(current_available_actions) == 1 and "no_reply" in current_available_actions
+            ):
+                action = "no_reply"
+                reasoning = "没有可用的动作" if not current_available_actions else "只有no_reply动作可用，跳过规划"
+                logger.info(f"{self.log_prefix}{reasoning}")
+                self.action_manager.restore_actions()
+                logger.debug(
+                    f"{self.log_prefix}沉默后恢复到默认动作集, 当前可用: {list(self.action_manager.get_using_actions().keys())}"
+                )
+                return {
+                    "action_result": {"action_type": action, "action_data": action_data, "reasoning": reasoning},
+                    "current_mind": current_mind,
+                    "observed_messages": observed_messages,
+                }
+
+            # --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
+            prompt = await self.build_planner_prompt(
+                self_info_block=self_info,
+                is_group_chat=is_group_chat,  # <-- Pass HFC state
+                chat_target_info=None,
+                observed_messages_str=observed_messages_str,  # <-- Pass local variable
+                current_mind=current_mind,  # <-- Pass argument
+                structured_info=structured_info,  # <-- Pass SubMind info
+                current_available_actions=current_available_actions,  # <-- Pass determined actions
+                cycle_info=cycle_info,  # <-- Pass cycle info
+                extra_info=extra_info,
+                running_memorys=running_memorys,
+            )
+
+            # --- 调用 LLM (普通文本生成) ---
+            llm_content = None
+            try:
+                prompt = f"{prompt}"
+                llm_content, (reasoning_content, _) = await self.planner_llm.generate_response_async(prompt=prompt)
+                
+                logger.debug(
+                    f"{self.log_prefix}规划器Prompt:\n{prompt}\n\n决策动作:{action},\n动作信息: '{action_data}'\n理由: {reasoning}"
+                )
+                
+                logger.debug(f"{self.log_prefix}LLM 原始响应: {llm_content}")
+                logger.debug(f"{self.log_prefix}LLM 原始理由响应: {reasoning_content}")
+            except Exception as req_e:
+                logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")
+                reasoning = f"LLM 请求失败，你的模型出现问题: {req_e}"
+                action = "no_reply"
+            
+            # try:
+            #     prompt_json = await global_prompt_manager.get_prompt_async("planner_prompt_json") 
+            #     prompt_json = prompt_json.format(raw_output=llm_content)
+            #     llm_content_json, (reasoning_content_json, _) = await self.utils_llm.generate_response_async(prompt=prompt_json)
+            #     logger.debug(f"{self.log_prefix}LLM格式化JSON: {llm_content_json}")
+            #     logger.debug(f"{self.log_prefix}LLM格式化理由: {reasoning_content_json}")
+            # except Exception as json_e:
+            #     logger.error(f"{self.log_prefix}解析LLM响应JSON失败，模型返回不标准: {json_e}. LLM原始输出: '{llm_content}'")
+            #     reasoning = f"解析LLM响应JSON失败: {json_e}. 将使用默认动作 'no_reply'."
+            #     action = "no_reply"
+                
+            
+
+            if llm_content:
+                try:
+                    fixed_json_string = repair_json(llm_content)
+                    if isinstance(fixed_json_string, str):
+                        try:
+                            parsed_json = json.loads(fixed_json_string)
+                        except json.JSONDecodeError as decode_error:
+                            logger.error(f"JSON解析错误: {str(decode_error)}")
+                            parsed_json = {}
+                    else:
+                        # 如果repair_json直接返回了字典对象，直接使用
+                        parsed_json = fixed_json_string
+
+                    # 提取决策，提供默认值
+                    extracted_action = parsed_json.get("action", "no_reply")
+                    # extracted_reasoning = parsed_json.get("reasoning", "LLM未提供理由")
+                    extracted_reasoning = ""
+
+                    # 将所有其他属性添加到action_data
+                    action_data = {}
+                    for key, value in parsed_json.items():
+                        if key not in ["action", "reasoning"]:
+                            action_data[key] = value
+                            
+                    action_data["identity"] = self_info
+
+                    # 对于reply动作不需要额外处理，因为相关字段已经在上面的循环中添加到action_data
+
+                    if extracted_action not in current_available_actions:
+                        logger.warning(
+                            f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{extracted_action}' (可用: {list(current_available_actions.keys())})，将强制使用 'no_reply'"
+                        )
+                        action = "no_reply"
+                        reasoning = f"LLM 返回了当前不可用的动作 '{extracted_action}' (可用: {list(current_available_actions.keys())})。原始理由: {extracted_reasoning}"
+                    else:
+                        # 动作有效且可用
+                        action = extracted_action
+                        reasoning = extracted_reasoning
+
+                except Exception as json_e:
+                    logger.warning(
+                        f"{self.log_prefix}解析LLM响应JSON失败，模型返回不标准: {json_e}. LLM原始输出: '{llm_content}'"
+                    )
+                    reasoning = f"解析LLM响应JSON失败: {json_e}. 将使用默认动作 'no_reply'."
+                    action = "no_reply"
+
+        except Exception as outer_e:
+            logger.error(f"{self.log_prefix}Planner 处理过程中发生意外错误，规划失败，将执行 no_reply: {outer_e}")
+            traceback.print_exc()
+            action = "no_reply"
+            reasoning = f"Planner 内部处理错误: {outer_e}"
+
+        # logger.debug(
+        #     f"{self.log_prefix}规划器Prompt:\n{prompt}\n\n决策动作:{action},\n动作信息: '{action_data}'\n理由: {reasoning}"
+        # )
+
+        # 恢复到默认动作集
+        self.action_manager.restore_actions()
+        logger.debug(
+            f"{self.log_prefix}规划后恢复到默认动作集, 当前可用: {list(self.action_manager.get_using_actions().keys())}"
+        )
+
+        action_result = {"action_type": action, "action_data": action_data, "reasoning": reasoning}
+
+        plan_result = {
+            "action_result": action_result,
+            "current_mind": current_mind,
+            "observed_messages": observed_messages,
+            "action_prompt": prompt,
+        }
+
+        return plan_result
+
+    async def build_planner_prompt(
+        self,
+        self_info_block: str,
+        is_group_chat: bool,  # Now passed as argument
+        chat_target_info: Optional[dict],  # Now passed as argument
+        observed_messages_str: str,
+        current_mind: Optional[str],
+        structured_info: Optional[str],
+        current_available_actions: Dict[str, ActionInfo],
+        cycle_info: Optional[str],
+        extra_info: list[str],
+        running_memorys: List[Dict[str, Any]],
+    ) -> str:
+        """构建 Planner LLM 的提示词 (获取模板并填充数据)"""
+        try:
+            memory_str = ""
+            if global_config.focus_chat.parallel_processing:
+                memory_str = ""
+                if running_memorys:
+                    memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
+                    for running_memory in running_memorys:
+                        memory_str += f"{running_memory['topic']}: {running_memory['content']}\n"
+
+            chat_context_description = "你现在正在一个群聊中"
+            chat_target_name = None  # Only relevant for private
+            if not is_group_chat and chat_target_info:
+                chat_target_name = (
+                    chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or "对方"
+                )
+                chat_context_description = f"你正在和 {chat_target_name} 私聊"
+
+            chat_content_block = ""
+            if observed_messages_str:
+                chat_content_block = f"聊天记录：\n{observed_messages_str}"
+            else:
+                chat_content_block = "你还未开始聊天"
+
+            mind_info_block = ""
+            if current_mind:
+                mind_info_block = f"对聊天的规划：{current_mind}"
+            else:
+                mind_info_block = "你刚参与聊天"
+
+            personality_block = individuality.get_prompt(x_person=2, level=2)
+
+            action_options_block = ""
+            for using_actions_name, using_actions_info in current_available_actions.items():
+                # print(using_actions_name)
+                # print(using_actions_info)
+                # print(using_actions_info["parameters"])
+                # print(using_actions_info["require"])
+                # print(using_actions_info["description"])
+
+                using_action_prompt = await global_prompt_manager.get_prompt_async("action_prompt")
+
+                param_text = ""
+                for param_name, param_description in using_actions_info["parameters"].items():
+                    param_text += f"    {param_name}: {param_description}\n"
+
+                require_text = ""
+                for require_item in using_actions_info["require"]:
+                    require_text += f"  - {require_item}\n"
+
+                if param_text:
+                    param_text = f"参数：\n{param_text}"
+                else:
+                    param_text = "无需参数"
+                
+                using_action_prompt = using_action_prompt.format(
+                    action_name=using_actions_name,
+                    action_description=using_actions_info["description"],
+                    action_parameters=param_text,
+                    action_require=require_text,
+                )
+
+                action_options_block += using_action_prompt
+
+            extra_info_block = "\n".join(extra_info)
+            extra_info_block += f"\n{structured_info}"
+            if extra_info or structured_info:
+                extra_info_block = f"以下是一些额外的信息，现在请你阅读以下内容，进行决策\n{extra_info_block}\n以上是一些额外的信息，现在请你阅读以下内容，进行决策"
+            else:
+                extra_info_block = ""
+
+            # 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。"
+            moderation_prompt_block = ""
+
+            planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
+            prompt = planner_prompt_template.format(
+                self_info_block=self_info_block,
+                memory_str=memory_str,
+                # bot_name=global_config.bot.nickname,
+                prompt_personality=personality_block,
+                chat_context_description=chat_context_description,
+                chat_content_block=chat_content_block,
+                mind_info_block=mind_info_block,
+                cycle_info_block=cycle_info,
+                action_options_text=action_options_block,
+                # action_available_block=action_available_block,
+                extra_info_block=extra_info_block,
+                moderation_prompt=moderation_prompt_block,
+            )
+            return prompt
+
+        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"
+
+
+init_prompt()

src/config/official_configs.py

@@ -153,6 +153,9 @@ class FocusChatConfig(ConfigBase):
     processor_max_time: int = 25
     """处理器最大时间，单位秒，如果超过这个时间，处理器会自动停止"""
 
+    planner_type: str = "default"
+    """规划器类型，可选值：default（默认规划器）, simple（简单规划器）"""
+
 
 @dataclass
 class FocusChatProcessorConfig(ConfigBase):

template/bot_config_template.toml

@@ -102,6 +102,8 @@ observation_context_size = 16 # 观察到的最长上下文大小
 compressed_length = 8 # 不能大于observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5
 compress_length_limit = 4 #最多压缩份数，超过该数值的压缩上下文会被删除
 
+planner_type = "complex" # 规划器类型，可选值：complex（复杂规划器）, simple（简单规划器）
+
 [focus_chat_processor] # 专注聊天处理器，打开可以实现更多功能，但是会增加token消耗
 mind_processor = false # 是否启用思维处理器
 self_identify_processor = true # 是否启用自我识别处理器