应要求提交上未完成的HFC, expression部分

src/bw_learner/expression_reflect_tracker.py

@@ -0,0 +1,60 @@
+from typing import TYPE_CHECKING, Optional
+
+import time
+
+from src.common.logger import get_logger
+from src.common.database.database import get_db_session
+from src.llm_models.utils_model import LLMRequest
+from src.config.config import model_config
+
+if TYPE_CHECKING:
+    from src.common.data_models.expression_data_model import MaiExpression
+
+# TODO: 这个LLMRequest实例被更优雅的方式替换掉
+judge_model = LLMRequest(model_set=model_config.model_task_config.tool_use, request_type="reflect.tracker")
+
+logger = get_logger("reflect_tracker")
+
+class ReflectTracker:
+    def __init__(self, session_id: str):
+        self.session_id = session_id
+        self.last_check_msg_count = 0
+        self.max_msg_count = 30
+        self.max_duration = 15 * 60  # 15 分钟
+        self.expression: Optional["MaiExpression"] = None  # 当前正在追踪的表达，由外部设置
+
+        # 运行状态
+        self.tracking = False
+        self.tracking_start_time: float = 0.0
+
+    def register_expression_and_track(self, expression: "MaiExpression"):
+        """注册需要追踪的表达"""
+        if self.tracking:
+            raise RuntimeError("ReflectTracker is already tracking an expression.")
+        self.expression = expression
+        self.tracking = True
+        self.tracking_start_time = time.time()
+    
+    def _reset_tracker(self):
+        """重置追踪状态"""
+        self.expression = None
+        self.tracking = False
+        self.last_check_msg_count = 0
+
+    async def trigger_tracker(self) -> bool:
+        """
+        触发追踪检查
+
+        Returns:
+            return (bool): 如果返回True，表示追踪完成，Tracker运行结束（运行状态置为`False`）；如果返回False，表示继续追踪
+        """
+        # 对于没有正在追踪的表达，直接返回False
+        if not self.tracking:
+            return False
+
+        # 检查是否超时（无论是消息数量还是时间）
+        if time.time() - self.tracking_start_time > self.max_duration:
+            self._reset_tracker()
+            return True
+        
+        # TODO: 完成追踪检查逻辑