放宽最近上下文到30条并取消中途截断

plugins/ai_auto_response/context/context_builder.py

@@ -94,38 +94,14 @@ class ContextBuilder:
     ) -> List[Dict]:
         if not recent_messages:
             return []
+        # 这里直接把“最近 N 条”原样交给后续提示词层，而不是再做一次相关性裁剪：
+        # 1. 用户明确要求给模型 30 条最近消息，方便推断群里正在讨论的上下文；
+        # 2. 之前的“相关性筛选 + 尾部保留”虽然更省 token，但会打断对话连续性；
+        # 3. 对群聊场景来说，连续现场通常比少量高分片段更有利于模型判断谁在接谁的话。
+        #
+        # 这里仍保留签名参数不动，是为了兼容上层调用，避免后续改动牵连太多。
         window = recent_messages[-self.recent_context_size:]
-        if len(window) <= 8:
-            return window
-
-        current_tokens = self._extract_topic_tokens(current_content)
-        quote_tokens = self._extract_topic_tokens(
-            f"{quote_context.get('title', '')} {quote_context.get('quote_body', '')}"
-        )
-        focus_tokens = current_tokens | quote_tokens
-        quote_sender_name = str(quote_context.get("quote_sender_name", "") or "").strip().lower()
-
-        scored: List[tuple[int, int, Dict]] = []
-        for idx, item in enumerate(window):
-            score = self._message_relevance(
-                item,
-                current_sender=current_sender,
-                focus_tokens=focus_tokens,
-                quote_sender_name=quote_sender_name,
-            )
-            if score > 0:
-                scored.append((score, idx, item))
-
-        # 总是保留尾部几条，维持现场感；再拼上与当前话题最相关的消息。
-        tail_indexes = set(range(max(len(window) - 4, 0), len(window)))
-        keep_indexes = set(tail_indexes)
-        for _, idx, _ in sorted(scored, key=lambda x: (-x[0], -x[1]))[:10]:
-            keep_indexes.add(idx)
-
-        selected = [window[idx] for idx in sorted(keep_indexes)]
-        if len(selected) < 6:
-            return window[-6:]
-        return selected[-12:]
+        return window
 
     @classmethod
     def _message_relevance(