feat: 添加了中英文动作插件模板，更新了摘要和智能思维导图插件，并简化了异步上下文压缩插件的模型阈值配置。

plugins/actions/smart-mind-map/smart_mind_map.py

@@ -362,8 +362,8 @@ class Action:
             description="Whether to show action status updates in the chat interface.",
         )
         LLM_MODEL_ID: str = Field(
-            default="gemini-2.5-flash",
-            description="Built-in LLM model ID for text analysis.",
+            default="",
+            description="Built-in LLM model ID for text analysis. If empty, uses the current conversation's model.",
         )
         MIN_TEXT_LENGTH: int = Field(
             default=100,
@@ -514,8 +514,13 @@ class Action:
                 long_text_content=long_text_content,
             )
 
+            # Determine model to use
+            target_model = self.valves.LLM_MODEL_ID
+            if not target_model:
+                target_model = body.get("model")
+
             llm_payload = {
-                "model": self.valves.LLM_MODEL_ID,
+                "model": target_model,
                 "messages": [
                     {"role": "system", "content": SYSTEM_PROMPT_MINDMAP_ASSISTANT},
                     {"role": "user", "content": formatted_user_prompt},

plugins/actions/smart-mind-map/思维导图.py

@@ -363,8 +363,8 @@ class Action:
             default=True, description="是否在聊天界面显示操作状态更新。"
         )
         LLM_MODEL_ID: str = Field(
-            default="gemini-2.5-flash",
-            description="用于文本分析的内置LLM模型ID。",
+            default="",
+            description="用于文本分析的内置LLM模型ID。如果为空，则使用当前对话的模型。",
         )
         MIN_TEXT_LENGTH: int = Field(
             default=100, description="进行思维导图分析所需的最小文本长度(字符数)。"
@@ -514,13 +514,17 @@ class Action:
                 long_text_content=long_text_content,
             )
 
+            # 确定使用的模型
+            target_model = self.valves.LLM_MODEL_ID
+            if not target_model:
+                target_model = body.get("model")
+
             llm_payload = {
-                "model": self.valves.LLM_MODEL_ID,
+                "model": target_model,
                 "messages": [
                     {"role": "system", "content": SYSTEM_PROMPT_MINDMAP_ASSISTANT},
                     {"role": "user", "content": formatted_user_prompt},
                 ],
-                "temperature": 0.5,
                 "stream": False,
             }
             user_obj = Users.get_user_by_id(user_id)