feat: 批量生成文本embedding，优化兴趣匹配计算逻辑，支持消息兴趣值的批量更新

src/plugins/built_in/affinity_flow_chatter/core/affinity_interest_calculator.py

@@ -103,7 +103,7 @@ class AffinityInterestCalculator(BaseInterestCalculator):
 
             # 1. 计算兴趣匹配分
             keywords = self._extract_keywords_from_database(message)
-            interest_match_score = await self._calculate_interest_match_score(content, keywords)
+            interest_match_score = await self._calculate_interest_match_score(message, content, keywords)
             logger.debug(f"[Affinity兴趣计算] 兴趣匹配分: {interest_match_score}")
 
             # 2. 计算关系分
@@ -180,7 +180,9 @@ class AffinityInterestCalculator(BaseInterestCalculator):
                 success=False, message_id=getattr(message, "message_id", ""), interest_value=0.0, error_message=str(e)
             )
 
-    async def _calculate_interest_match_score(self, content: str, keywords: list[str] | None = None) -> float:
+    async def _calculate_interest_match_score(
+        self, message: "DatabaseMessages", content: str, keywords: list[str] | None = None
+    ) -> float:
         """计算兴趣匹配度（使用智能兴趣匹配系统，带超时保护）"""
 
         # 调试日志：检查各个条件
@@ -199,7 +201,9 @@ class AffinityInterestCalculator(BaseInterestCalculator):
         try:
             # 使用机器人的兴趣标签系统进行智能匹配（1.5秒超时保护）
             match_result = await asyncio.wait_for(
-                bot_interest_manager.calculate_interest_match(content, keywords or []),
+                bot_interest_manager.calculate_interest_match(
+                    content, keywords or [], getattr(message, "semantic_embedding", None)
+                ),
                 timeout=1.5
             )
             logger.debug(f"兴趣匹配结果: {match_result}")

src/plugins/built_in/affinity_flow_chatter/planner/planner.py

@@ -7,6 +7,9 @@ import asyncio
 from dataclasses import asdict
 from typing import TYPE_CHECKING, Any
 
+from src.chat.interest_system import bot_interest_manager
+from src.chat.interest_system.interest_manager import get_interest_manager
+from src.chat.message_receive.storage import MessageStorage
 from src.common.logger import get_logger
 from src.config.config import global_config
 from src.mood.mood_manager import mood_manager
@@ -19,6 +22,7 @@ if TYPE_CHECKING:
     from src.chat.planner_actions.action_manager import ChatterActionManager
     from src.common.data_models.info_data_model import Plan
     from src.common.data_models.message_manager_data_model import StreamContext
+    from src.common.data_models.database_data_model import DatabaseMessages
 
 # 导入提示词模块以确保其被初始化
 
@@ -115,6 +119,74 @@ class ChatterActionPlanner:
                 context.processing_message_id = None
             return [], None
 
+    async def _prepare_interest_scores(
+        self, context: "StreamContext | None", unread_messages: list["DatabaseMessages"]
+    ) -> None:
+        """在执行规划前，为未计算兴趣的消息批量补齐兴趣数据"""
+        if not context or not unread_messages:
+            return
+
+        pending_messages = [msg for msg in unread_messages if not getattr(msg, "interest_calculated", False)]
+        if not pending_messages:
+            return
+
+        logger.debug(f"批量兴趣值计算：待处理 {len(pending_messages)} 条消息")
+
+        if not bot_interest_manager.is_initialized:
+            logger.debug("bot_interest_manager 未初始化，跳过批量兴趣计算")
+            return
+
+        try:
+            interest_manager = get_interest_manager()
+        except Exception as exc:  # noqa: BLE001
+            logger.warning(f"获取兴趣管理器失败: {exc}")
+            return
+
+        if not interest_manager or not interest_manager.has_calculator():
+            logger.debug("当前无可用兴趣计算器，跳过批量兴趣计算")
+            return
+
+        text_map: dict[str, str] = {}
+        for message in pending_messages:
+            text = getattr(message, "processed_plain_text", None) or getattr(message, "display_message", "") or ""
+            text_map[str(message.message_id)] = text
+
+        try:
+            embeddings = await bot_interest_manager.generate_embeddings_for_texts(text_map)
+        except Exception as exc:  # noqa: BLE001
+            logger.error(f"批量获取消息embedding失败: {exc}")
+            embeddings = {}
+
+        interest_updates: dict[str, float] = {}
+        reply_updates: dict[str, bool] = {}
+
+        for message in pending_messages:
+            message_id = str(message.message_id)
+            if message_id in embeddings:
+                message.semantic_embedding = embeddings[message_id]
+
+            try:
+                result = await interest_manager.calculate_interest(message)
+            except Exception as exc:  # noqa: BLE001
+                logger.error(f"批量计算消息兴趣失败: {exc}")
+                continue
+
+            if result.success:
+                message.interest_value = result.interest_value
+                message.should_reply = result.should_reply
+                message.should_act = result.should_act
+                message.interest_calculated = True
+                interest_updates[message_id] = result.interest_value
+                reply_updates[message_id] = result.should_reply
+            else:
+                message.interest_calculated = False
+
+        if interest_updates:
+            try:
+                await MessageStorage.bulk_update_interest_values(interest_updates, reply_updates)
+            except Exception as exc:  # noqa: BLE001
+                logger.error(f"批量更新消息兴趣值失败: {exc}")
+
     async def _focus_mode_flow(self, context: "StreamContext | None") -> tuple[list[dict[str, Any]], Any | None]:
         """Focus模式下的完整plan流程
 
@@ -122,6 +194,7 @@ class ChatterActionPlanner:
         """
         try:
             unread_messages = context.get_unread_messages() if context else []
+            await self._prepare_interest_scores(context, unread_messages)
 
             # 1. 使用新的兴趣度管理系统进行评分
             max_message_interest = 0.0
@@ -303,6 +376,7 @@ class ChatterActionPlanner:
 
         try:
             unread_messages = context.get_unread_messages() if context else []
+            await self._prepare_interest_scores(context, unread_messages)
 
             # 1. 检查是否有未读消息
             if not unread_messages: