feat：优化maisaka

src/maisaka/replyer.py

@@ -1,76 +1,94 @@
 """
-MaiSaka - Reply 回复生成器
-根据想法和上下文生成口语化回复。
+MaiSaka reply helper.
 """
 
-from typing import Optional
+from datetime import datetime
+from typing import Any, Optional
+
+from src.config.config import global_config
+
 from .llm_service import MaiSakaLLMService
 
+VISIBLE_REPLY_PREFIX = "\u3010\u9ea6\u9ea6\u7684\u53d1\u8a00\u3011"
 
-def format_chat_history(messages: list) -> str:
-    """将聊天消息列表格式化为可读文本。"""
+
+def _normalize_content(content: str, limit: int = 500) -> str:
+    normalized = " ".join((content or "").split())
+    if len(normalized) > limit:
+        return normalized[:limit] + "..."
+    return normalized
+
+
+def _format_message_time(_: dict[str, Any]) -> str:
+    return datetime.now().strftime("%H:%M:%S")
+
+
+def _extract_visible_assistant_reply(message: dict[str, Any]) -> str:
+    if message.get("_type") == "perception":
+        return ""
+
+    content = (message.get("content", "") or "").strip()
+    if not content:
+        return ""
+
+    marker = "[generated_reply]"
+    if marker in content:
+        _, visible_reply = content.rsplit(marker, 1)
+        return _normalize_content(visible_reply)
+
+    return ""
+
+
+def _extract_guided_bot_reply(message: dict[str, Any]) -> str:
+    content = (message.get("content", "") or "").strip()
+    if content.startswith(VISIBLE_REPLY_PREFIX):
+        return _normalize_content(content[len(VISIBLE_REPLY_PREFIX) :].strip())
+    return ""
+
+
+def format_chat_history(messages: list[dict[str, Any]]) -> str:
+    """Format visible chat history for reply generation."""
+    bot_nickname = global_config.bot.nickname.strip() or "Bot"
     parts: list[str] = []
-    for msg in messages:
-        role = msg.get("role", "?")
-        content = msg.get("content", "") or ""
-        if role == "system":
-            parts.append(f"[系统] {content[:500]}")
-        elif role == "user":
-            parts.append(f"[用户] {content[:500]}")
-        elif role == "assistant":
+
+    for message in messages:
+        role = message.get("role", "")
+        timestamp = _format_message_time(message)
+
+        if role == "user":
+            guided_reply = _extract_guided_bot_reply(message)
+            if guided_reply:
+                parts.append(f"{timestamp} {bot_nickname}（分析器指导的麦麦发言）：{guided_reply}")
+                continue
+
+            content = _normalize_content(message.get("content", "") or "")
             if content:
-                parts.append(f"[助手思考] {content[:500]}")
-            for tc in msg.get("tool_calls", []):
-                func = tc.get("function", {})
-                name = func.get("name", "?")
-                args = func.get("arguments", "")
-                if isinstance(args, str) and len(args) > 200:
-                    args = args[:200] + "..."
-                parts.append(f"[助手调用 {name}] {args}")
-        elif role == "tool":
-            parts.append(f"[工具结果] {content[:300]}")
+                parts.append(f"{timestamp} 用户：{content}")
+            continue
+
+        if role == "assistant":
+            visible_reply = _extract_visible_assistant_reply(message)
+            if visible_reply:
+                parts.append(f"{timestamp} {bot_nickname}（你）：{visible_reply}")
+
     return "\n".join(parts)
 
 
 class Replyer:
-    """
-    回复生成器。
-
-    根据给定的想法（reason）和对话上下文，生成符合人设的口语化回复。
-    """
+    """Generate visible replies from thoughts and context."""
 
     def __init__(self, llm_service: Optional[MaiSakaLLMService] = None):
-        """
-        初始化回复器。
-
-        Args:
-            llm_service: LLM 服务实例，如果为 None 则需要在调用前设置
-        """
         self._llm_service = llm_service
         self._enabled = True
 
     def set_llm_service(self, llm_service: MaiSakaLLMService) -> None:
-        """设置 LLM 服务"""
         self._llm_service = llm_service
 
     def set_enabled(self, enabled: bool) -> None:
-        """启用/禁用回复功能"""
         self._enabled = enabled
 
-    async def reply(self, reason: str, chat_history: list) -> str:
-        """
-        根据想法和上下文生成回复。
-
-        Args:
-            reason: 想要回复的方式、想法、内容（不包含具体回复内容）
-            chat_history: 对话历史上下文
-
-        Returns:
-            生成的回复内容，失败时返回默认回复
-        """
+    async def reply(self, reason: str, chat_history: list[dict[str, Any]]) -> str:
         if not self._enabled or not reason or self._llm_service is None:
             return "..."
 
-        # 直接使用 LLM 服务的 generate_reply 方法
-        # 该方法使用主项目的 replyer 模型配置
         return await self._llm_service.generate_reply(reason, chat_history)