pref：优化大模型工具调用偷懒问题，调整基础提示词与新增临时工具记忆加深

main/xiaozhi-server/core/utils/dialogue.py

@@ -6,18 +6,20 @@ from datetime import datetime
 
 class Message:
     def __init__(
-        self,
-        role: str,
-        content: str = None,
-        uniq_id: str = None,
-        tool_calls=None,
-        tool_call_id=None,
+            self,
+            role: str,
+            content: str = None,
+            uniq_id: str = None,
+            tool_calls=None,
+            tool_call_id=None,
+            is_temporary=False,
     ):
         self.uniq_id = uniq_id if uniq_id is not None else str(uuid.uuid4())
         self.role = role
         self.content = content
         self.tool_calls = tool_calls
         self.tool_call_id = tool_call_id
+        self.is_temporary = is_temporary  # 标记临时消息（如工具调用提醒）
 
 
 class Dialogue:
@@ -59,8 +61,70 @@ class Dialogue:
         else:
             self.put(Message(role="system", content=new_content))
 
+    def trim_history(self, max_turns: int = 10) -> int:
+        """
+        智能截断对话历史，保留工具调用的完整性
+
+        Args:
+            max_turns: 保留的最大对话轮数（每轮 = user + assistant/tool 相关消息）
+
+        Returns:
+            int: 被移除的消息数量
+        """
+        if len(self.dialogue) <= max_turns * 2 + 1:  # +1 是系统消息
+            return 0
+
+        # 分离系统消息和对话消息
+        system_messages = [msg for msg in self.dialogue if msg.role == "system"]
+        conversation_messages = [msg for msg in self.dialogue if msg.role != "system"]
+
+        if len(conversation_messages) <= max_turns * 2:
+            return 0
+
+        # 智能截断：保留完整的工具调用链路
+        keep_messages = []
+        i = len(conversation_messages) - 1
+        turn_count = 0
+
+        while i >= 0 and turn_count < max_turns:
+            msg = conversation_messages[i]
+
+            # 从后向前收集消息
+            if msg.role == "user":
+                # 遇到 user 消息，说明一轮对话开始
+                keep_messages.insert(0, msg)
+                turn_count += 1
+                i -= 1
+            elif msg.role == "assistant":
+                # 收集 assistant 消息
+                keep_messages.insert(0, msg)
+
+                # 如果这个 assistant 有 tool_calls，需要收集对应的 tool 响应
+                if msg.tool_calls is not None:
+                    i -= 1
+                    # 继续向后收集所有相关的 tool 消息
+                    while i >= 0 and conversation_messages[i].role == "tool":
+                        keep_messages.insert(0, conversation_messages[i])
+                        i -= 1
+                else:
+                    i -= 1
+            elif msg.role == "tool":
+                # tool 消息应该已经被上面的逻辑收集了
+                # 如果单独遇到，也要保留（防止边界情况）
+                keep_messages.insert(0, msg)
+                i -= 1
+            else:
+                i -= 1
+
+        removed_count = len(conversation_messages) - len(keep_messages)
+
+        # 重建对话列表
+        self.dialogue = system_messages + keep_messages
+
+        return removed_count
+
     def get_llm_dialogue_with_memory(
-        self, memory_str: str = None, voiceprint_config: dict = None
+            self, memory_str: str = None, voiceprint_config: dict = None
     ) -> List[Dict[str, str]]:
         # 构建对话
         dialogue = []