fix: fit language and log AI responses more clearly (#3864)

* fix: fit language and log AI responses more clearly

* chore: ruff format

packages/astrbot/long_term_memory.py

@@ -8,7 +8,7 @@ from astrbot.api import star
 from astrbot.api.event import AstrMessageEvent
 from astrbot.api.message_components import At, Image, Plain
 from astrbot.api.platform import MessageType
-from astrbot.api.provider import Provider, ProviderRequest
+from astrbot.api.provider import LLMResponse, Provider, ProviderRequest
 from astrbot.core.astrbot_config_mgr import AstrBotConfigManager
 
 """
@@ -158,8 +158,12 @@ class LongTermMemory:
         cfg = self.cfg(event)
         if cfg["enable_active_reply"]:
             prompt = req.prompt
-            req.prompt = f"You are now in a chatroom. The chat history is as follows:\n{chats_str}"
-            req.prompt += f"\nNow, a new message is coming: `{prompt}`. Please react to it. Only output your response and do not output any other information."
+            req.prompt = (
+                f"You are now in a chatroom. The chat history is as follows:\n{chats_str}"
+                f"\nNow, a new message is coming: `{prompt}`. "
+                "Please react to it. Only output your response and do not output any other information. "
+                "You MUST use the SAME language as the chatroom is using."
+            )
             req.contexts = []  # 清空上下文，当使用了主动回复，所有聊天记录都在一个prompt中。
         else:
             req.system_prompt += (
@@ -167,13 +171,15 @@ class LongTermMemory:
             )
             req.system_prompt += chats_str
 
-    async def after_req_llm(self, event: AstrMessageEvent):
+    async def after_req_llm(self, event: AstrMessageEvent, llm_resp: LLMResponse):
         if event.unified_msg_origin not in self.session_chats:
             return
 
-        if event.get_result() and event.get_result().is_llm_result():
-            final_message = f"[You/{datetime.datetime.now().strftime('%H:%M:%S')}]: {event.get_result().get_plain_text()}"
-            logger.debug(f"ltm | {event.unified_msg_origin} | {final_message}")
+        if llm_resp.completion_text:
+            final_message = f"[You/{datetime.datetime.now().strftime('%H:%M:%S')}]: {llm_resp.completion_text}"
+            logger.debug(
+                f"Recorded AI response: {event.unified_msg_origin} | {final_message}"
+            )
             self.session_chats[event.unified_msg_origin].append(final_message)
             cfg = self.cfg(event)
             if len(self.session_chats[event.unified_msg_origin]) > cfg["max_cnt"]:

packages/astrbot/main.py

@@ -322,7 +322,7 @@ class Main(star.Star):
 
     @filter.on_llm_response()
     async def inject_reasoning(self, event: AstrMessageEvent, resp: LLMResponse):
-        """在 LLM 响应后基于配置注入思考过程文本"""
+        """在 LLM 响应后基于配置注入思考过程文本 / 在 LLM 响应后记录对话"""
         umo = event.unified_msg_origin
         cfg = self.context.get_config(umo).get("provider_settings", {})
         show_reasoning = cfg.get("display_reasoning_text", False)
@@ -331,12 +331,9 @@ class Main(star.Star):
                 f"🤔 思考: {resp.reasoning_content}\n\n{resp.completion_text}"
             )
 
-    @filter.after_message_sent()
-    async def after_llm_req(self, event: AstrMessageEvent):
-        """在 LLM 请求后记录对话"""
         if self.ltm and self.ltm_enabled(event):
             try:
-                await self.ltm.after_req_llm(event)
+                await self.ltm.after_req_llm(event, resp)
             except Exception as e:
                 logger.error(f"ltm: {e}")