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MeetSpot/app/agent/base.py

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+"""Agent 基类 - 参考 OpenManus BaseAgent 设计"""
+
+from abc import ABC, abstractmethod
+from contextlib import asynccontextmanager
+from typing import List, Optional, Dict, Any
+
+from pydantic import BaseModel, Field, model_validator
+
+from app.llm import LLM
+from app.logger import logger
+from app.schema import AgentState, Memory, Message, ROLE_TYPE
+
+
+class BaseAgent(BaseModel, ABC):
+    """Agent 基类
+
+    提供基础的状态管理、记忆管理和执行循环。
+    子类需要实现 step() 方法来定义具体行为。
+    """
+
+    # 核心属性
+    name: str = Field(default="BaseAgent", description="Agent 名称")
+    description: Optional[str] = Field(default=None, description="Agent 描述")
+
+    # 提示词
+    system_prompt: Optional[str] = Field(default=None, description="系统提示词")
+    next_step_prompt: Optional[str] = Field(default=None, description="下一步提示词")
+
+    # 依赖
+    llm: Optional[LLM] = Field(default=None, description="LLM 实例")
+    memory: Memory = Field(default_factory=Memory, description="Agent 记忆")
+    state: AgentState = Field(default=AgentState.IDLE, description="当前状态")
+
+    # 执行控制
+    max_steps: int = Field(default=10, description="最大执行步数")
+    current_step: int = Field(default=0, description="当前步数")
+
+    # 重复检测阈值
+    duplicate_threshold: int = 2
+
+    class Config:
+        arbitrary_types_allowed = True
+        extra = "allow"
+
+    @model_validator(mode="after")
+    def initialize_agent(self) -> "BaseAgent":
+        """初始化 Agent"""
+        if self.llm is None:
+            try:
+                self.llm = LLM()
+            except Exception as e:
+                logger.warning(f"无法初始化 LLM: {e}")
+        if not isinstance(self.memory, Memory):
+            self.memory = Memory()
+        return self
+
+    @asynccontextmanager
+    async def state_context(self, new_state: AgentState):
+        """状态上下文管理器，用于安全的状态转换"""
+        if not isinstance(new_state, AgentState):
+            raise ValueError(f"无效状态: {new_state}")
+
+        previous_state = self.state
+        self.state = new_state
+        try:
+            yield
+        except Exception as e:
+            self.state = AgentState.ERROR
+            raise e
+        finally:
+            self.state = previous_state
+
+    def update_memory(
+        self,
+        role: ROLE_TYPE,
+        content: str,
+        base64_image: Optional[str] = None,
+        **kwargs,
+    ) -> None:
+        """添加消息到记忆"""
+        message_map = {
+            "user": Message.user_message,
+            "system": Message.system_message,
+            "assistant": Message.assistant_message,
+            "tool": lambda content, **kw: Message.tool_message(content, **kw),
+        }
+
+        if role not in message_map:
+            raise ValueError(f"不支持的消息角色: {role}")
+
+        if role == "tool":
+            self.memory.add_message(message_map[role](content, **kwargs))
+        else:
+            self.memory.add_message(message_map[role](content, base64_image=base64_image))
+
+    async def run(self, request: Optional[str] = None) -> str:
+        """执行 Agent 主循环
+
+        Args:
+            request: 可选的初始用户请求
+
+        Returns:
+            执行结果摘要
+        """
+        if self.state != AgentState.IDLE:
+            raise RuntimeError(f"无法从状态 {self.state} 启动 Agent")
+
+        if request:
+            self.update_memory("user", request)
+
+        results: List[str] = []
+        async with self.state_context(AgentState.RUNNING):
+            while (
+                self.current_step < self.max_steps
+                and self.state != AgentState.FINISHED
+            ):
+                self.current_step += 1
+                logger.info(f"执行步骤 {self.current_step}/{self.max_steps}")
+                step_result = await self.step()
+
+                # 检测卡住状态
+                if self.is_stuck():
+                    self.handle_stuck_state()
+
+                results.append(f"Step {self.current_step}: {step_result}")
+
+            if self.current_step >= self.max_steps:
+                self.current_step = 0
+                self.state = AgentState.IDLE
+                results.append(f"已终止: 达到最大步数 ({self.max_steps})")
+
+        return "\n".join(results) if results else "未执行任何步骤"
+
+    @abstractmethod
+    async def step(self) -> str:
+        """执行单步操作 - 子类必须实现"""
+        pass
+
+    def handle_stuck_state(self):
+        """处理卡住状态"""
+        stuck_prompt = "检测到重复响应。请考虑新策略，避免重复已尝试过的无效路径。"
+        self.next_step_prompt = f"{stuck_prompt}\n{self.next_step_prompt or ''}"
+        logger.warning(f"Agent 检测到卡住状态，已添加提示")
+
+    def is_stuck(self) -> bool:
+        """检测是否陷入循环"""
+        if len(self.memory.messages) < 2:
+            return False
+
+        last_message = self.memory.messages[-1]
+        if not last_message.content:
+            return False
+
+        # 统计相同内容出现次数
+        duplicate_count = sum(
+            1
+            for msg in reversed(self.memory.messages[:-1])
+            if msg.role == "assistant" and msg.content == last_message.content
+        )
+
+        return duplicate_count >= self.duplicate_threshold
+
+    @property
+    def messages(self) -> List[Message]:
+        """获取记忆中的消息列表"""
+        return self.memory.messages
+
+    @messages.setter
+    def messages(self, value: List[Message]):
+        """设置记忆中的消息列表"""
+        self.memory.messages = value