feat：重构插件api

src/plugin_system/base/base_action.py

@@ -0,0 +1,120 @@
+from abc import ABC, abstractmethod
+from typing import Tuple, Dict, Any, Optional
+from src.common.logger_manager import get_logger
+from src.plugin_system.apis.plugin_api import PluginAPI
+from src.plugin_system.base.component_types import ActionActivationType, ChatMode, ActionInfo, ComponentType
+
+logger = get_logger("base_action")
+
+class BaseAction(ABC):
+    """Action组件基类
+    
+    Action是插件的一种组件类型，用于处理聊天中的动作逻辑
+    
+    子类可以通过类属性定义激活条件：
+    - focus_activation_type: 专注模式激活类型
+    - normal_activation_type: 普通模式激活类型
+    - activation_keywords: 激活关键词列表
+    - keyword_case_sensitive: 关键词是否区分大小写
+    - mode_enable: 启用的聊天模式
+    - parallel_action: 是否允许并行执行
+    - random_activation_probability: 随机激活概率
+    - llm_judge_prompt: LLM判断提示词
+    """
+    
+    # 默认激活设置（子类可以覆盖）
+    focus_activation_type: ActionActivationType = ActionActivationType.NEVER
+    normal_activation_type: ActionActivationType = ActionActivationType.NEVER
+    activation_keywords: list = []
+    keyword_case_sensitive: bool = False
+    mode_enable: ChatMode = ChatMode.ALL
+    parallel_action: bool = True
+    random_activation_probability: float = 0.0
+    llm_judge_prompt: str = ""
+    
+    def __init__(self, 
+                 action_data: dict, 
+                 reasoning: str, 
+                 cycle_timers: dict, 
+                 thinking_id: str,
+                 **kwargs):
+        """初始化Action组件
+        
+        Args:
+            action_data: 动作数据
+            reasoning: 执行该动作的理由
+            cycle_timers: 计时器字典
+            thinking_id: 思考ID
+            **kwargs: 其他参数（包含服务对象）
+        """
+        self.action_data = action_data
+        self.reasoning = reasoning
+        self.cycle_timers = cycle_timers
+        self.thinking_id = thinking_id
+        
+        # 创建API实例
+        self.api = PluginAPI(
+            chat_stream=kwargs.get("chat_stream"),
+            expressor=kwargs.get("expressor"), 
+            replyer=kwargs.get("replyer"),
+            observations=kwargs.get("observations"),
+            log_prefix=kwargs.get("log_prefix", "")
+        )
+        
+        self.log_prefix = kwargs.get("log_prefix", "")
+        
+        logger.debug(f"{self.log_prefix} Action组件初始化完成")
+    
+    async def send_reply(self, content: str) -> bool:
+        """发送回复消息
+        
+        Args:
+            content: 回复内容
+            
+        Returns:
+            bool: 是否发送成功
+        """
+        return await self.api.send_message("text", content)
+    
+    @classmethod
+    def get_action_info(cls, name: str = None, description: str = None) -> 'ActionInfo':
+        """从类属性生成ActionInfo
+        
+        Args:
+            name: Action名称，如果不提供则使用类名
+            description: Action描述，如果不提供则使用类文档字符串
+            
+        Returns:
+            ActionInfo: 生成的Action信息对象
+        """
+
+        
+        # 自动生成名称和描述
+        if name is None:
+            name = cls.__name__.lower().replace('action', '')
+        if description is None:
+            description = cls.__doc__ or f"{cls.__name__} Action组件"
+            description = description.strip().split('\n')[0]  # 取第一行作为描述
+        
+        return ActionInfo(
+            name=name,
+            component_type=ComponentType.ACTION,
+            description=description,
+            focus_activation_type=cls.focus_activation_type,
+            normal_activation_type=cls.normal_activation_type,
+            activation_keywords=cls.activation_keywords.copy() if cls.activation_keywords else [],
+            keyword_case_sensitive=cls.keyword_case_sensitive,
+            mode_enable=cls.mode_enable,
+            parallel_action=cls.parallel_action,
+            random_activation_probability=cls.random_activation_probability,
+            llm_judge_prompt=cls.llm_judge_prompt
+        )
+    
+    @abstractmethod
+    async def execute(self) -> Tuple[bool, str]:
+        """执行Action的抽象方法，子类必须实现
+        
+        Returns:
+            Tuple[bool, str]: (是否执行成功, 回复文本)
+        """
+        pass