creat

Signed-off-by: TIANHE <TIANHE@GMAIL.COM>

backend_api_python/app/services/agents/base_agent.py

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+"""
+智能体基类
+"""
+from abc import ABC, abstractmethod
+from typing import Dict, Any, Optional, List
+from app.utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class BaseAgent(ABC):
+    """智能体基类，所有分析智能体都继承此类"""
+    
+    def __init__(self, name: str, memory: Optional[Any] = None):
+        """
+        初始化智能体
+        
+        Args:
+            name: 智能体名称
+            memory: 记忆系统实例（可选）
+        """
+        self.name = name
+        self.memory = memory
+        self.logger = get_logger(f"{__name__}.{name}")
+    
+    @abstractmethod
+    def analyze(self, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        执行分析任务
+        
+        Args:
+            context: 分析上下文，包含市场、代码、基础数据等
+            
+        Returns:
+            分析结果字典
+        """
+        pass
+    
+    def get_memories(self, situation: str, n_matches: int = 2) -> List[Dict[str, Any]]:
+        """
+        从记忆中检索相似情况
+        
+        Args:
+            situation: 当前情况描述
+            n_matches: 返回的匹配数量
+            
+        Returns:
+            匹配的历史记录列表
+        """
+        if self.memory:
+            return self.memory.get_memories(situation, n_matches=n_matches)
+        return []
+    
+    def format_memories_for_prompt(self, memories: List[Dict[str, Any]]) -> str:
+        """
+        格式化记忆为提示词
+        
+        Args:
+            memories: 记忆列表
+            
+        Returns:
+            格式化的字符串
+        """
+        if not memories:
+            return "无历史经验可参考。"
+        
+        formatted = "历史经验参考：\n"
+        for i, mem in enumerate(memories, 1):
+            formatted += f"{i}. {mem.get('recommendation', 'N/A')}\n"
+        
+        return formatted