action的reply_message设置为数据模型，维护typing以及增强稳定性

src/chat/planner_actions/action_manager.py

@@ -84,7 +84,7 @@ class ActionManager:
                 log_prefix=log_prefix,
                 shutting_down=shutting_down,
                 plugin_config=plugin_config,
-                action_message=action_message.flatten() if action_message else None,
+                action_message=action_message,
             )
 
             logger.debug(f"创建Action实例成功: {action_name}")

src/chat/utils/utils_image.py

@@ -4,7 +4,6 @@ import time
 import hashlib
 import uuid
 import io
-import asyncio
 import numpy as np
 
 from typing import Optional, Tuple
@@ -177,7 +176,7 @@ class ImageManager:
                 emotion_prompt, temperature=0.3, max_tokens=50
             )
 
-            if emotion_result is None:
+            if not emotion_result:
                 logger.warning("LLM未能生成情感标签，使用详细描述的前几个词")
                 # 降级处理：从详细描述中提取关键词
                 import jieba

src/llm_models/utils_model.py

@@ -156,19 +156,19 @@ class LLMRequest:
         """
         # 请求体构建
         start_time = time.time()
-        
+
         message_builder = MessageBuilder()
         message_builder.add_text_content(prompt)
         messages = [message_builder.build()]
-        
+
         tool_built = self._build_tool_options(tools)
-        
+
         # 模型选择
         model_info, api_provider, client = self._select_model()
-        
+
         # 请求并处理返回值
         logger.debug(f"LLM选择耗时: {model_info.name} {time.time() - start_time}")
-        
+
         response = await self._execute_request(
             api_provider=api_provider,
             client=client,
@@ -179,8 +179,7 @@ class LLMRequest:
             max_tokens=max_tokens,
             tool_options=tool_built,
         )
-        
-        
+
         content = response.content
         reasoning_content = response.reasoning_content or ""
         tool_calls = response.tool_calls
@@ -188,7 +187,7 @@ class LLMRequest:
         if not reasoning_content and content:
             content, extracted_reasoning = self._extract_reasoning(content)
             reasoning_content = extracted_reasoning
-            
+
         if usage := response.usage:
             llm_usage_recorder.record_usage_to_database(
                 model_info=model_info,
@@ -199,7 +198,7 @@ class LLMRequest:
                 time_cost=time.time() - start_time,
             )
 
-        return content, (reasoning_content, model_info.name, tool_calls)
+        return content or "", (reasoning_content, model_info.name, tool_calls)
 
     async def get_embedding(self, embedding_input: str) -> Tuple[List[float], str]:
         """获取嵌入向量
@@ -248,11 +247,11 @@ class LLMRequest:
         )
         model_info = model_config.get_model_info(least_used_model_name)
         api_provider = model_config.get_provider(model_info.api_provider)
-        
+
         # 对于嵌入任务，强制创建新的客户端实例以避免事件循环问题
-        force_new_client = (self.request_type == "embedding")
+        force_new_client = self.request_type == "embedding"
         client = client_registry.get_client_class_instance(api_provider, force_new=force_new_client)
-        
+
         logger.debug(f"选择请求模型: {model_info.name}")
         total_tokens, penalty, usage_penalty = self.model_usage[model_info.name]
         self.model_usage[model_info.name] = (total_tokens, penalty, usage_penalty + 1)  # 增加使用惩罚值防止连续使用

src/person_info/person_info.py

@@ -241,7 +241,7 @@ class Person:
         self.name_reason: Optional[str] = None
         self.know_times = 0
         self.know_since = None
-        self.last_know = None
+        self.last_know: Optional[float] = None
         self.memory_points = []
 
         # 初始化性格特征相关字段

src/plugin_system/__init__.py

@@ -53,6 +53,15 @@ from .apis import (
     get_logger,
 )
 
+from src.common.data_models.database_data_model import (
+    DatabaseMessages,
+    DatabaseUserInfo,
+    DatabaseGroupInfo,
+    DatabaseChatInfo,
+)
+from src.common.data_models.info_data_model import TargetPersonInfo, ActionPlannerInfo
+from src.common.data_models.llm_data_model import LLMGenerationDataModel
+
 
 __version__ = "2.0.0"
 
@@ -103,4 +112,12 @@ __all__ = [
     # "ManifestGenerator",
     # "validate_plugin_manifest",
     # "generate_plugin_manifest",
+    # 数据模型
+    "DatabaseMessages",
+    "DatabaseUserInfo",
+    "DatabaseGroupInfo",
+    "DatabaseChatInfo",
+    "TargetPersonInfo",
+    "ActionPlannerInfo",
+    "LLMGenerationDataModel"
 ]

src/plugin_system/base/base_action.py

@@ -39,7 +39,7 @@ class BaseAction(ABC):
         chat_stream: ChatStream,
         log_prefix: str = "",
         plugin_config: Optional[dict] = None,
-        action_message: Optional[dict] = None,
+        action_message: Optional["DatabaseMessages"] = None,
         **kwargs,
     ):
         # sourcery skip: hoist-similar-statement-from-if, merge-else-if-into-elif, move-assign-in-block, swap-if-else-branches, swap-nested-ifs
@@ -114,16 +114,13 @@ class BaseAction(ABC):
 
         if self.action_message:
             self.has_action_message = True
-        else:
-            self.action_message = {}
 
-        if self.has_action_message:
             if self.action_name != "no_action":
-                self.group_id = str(self.action_message.get("chat_info_group_id", None))
-                self.group_name = self.action_message.get("chat_info_group_name", None)
+                self.group_id = str(self.action_message.chat_info.group_info.group_id if self.action_message.chat_info.group_info else None)
+                self.group_name = self.action_message.chat_info.group_info.group_name if self.action_message.chat_info.group_info else None
 
-                self.user_id = str(self.action_message.get("user_id", None))
-                self.user_nickname = self.action_message.get("user_nickname", None)
+                self.user_id = str(self.action_message.user_info.user_id)
+                self.user_nickname = self.action_message.user_info.user_nickname
                 if self.group_id:
                     self.is_group = True
                     self.target_id = self.group_id