我已经把群总结这块从“前 120 字硬砍”改成了“关键句提炼 + 更高上限”。

已改文件：

context_builder.py
具体优化：

原来：
group_memory_summary 超过 120 字就直接 summary[:117] + "..."，很容易把后面的关键结论截掉。
现在：
新增 _compact_group_summary(...)。
先按句拆分，再给句子打分，优先保留包含“结论/风险/报错/配置/策略/优化”等关键词的句子。
同时保留首句和末句，避免上下文断裂。
最终摘要上限提高到 420 字、最多 6 句，再做兜底截断。
prompt 字段名也改成 群长期摘要关键句，让模型更明确这是提炼后的重点。

plugins/ai_auto_response/context/context_builder.py

@@ -276,9 +276,7 @@ class ContextBuilder:
             return "当前群没有特殊知识域限制。"
         focus = ", ".join(group_profile.get("knowledge_focus", [])[:6])
         boundaries = ", ".join(group_profile.get("topic_boundaries", [])[:6])
-        summary = str(group_profile.get("group_memory_summary", "") or "").replace("\n", " ").strip()
-        if len(summary) > 120:
-            summary = summary[:117] + "..."
+        summary = ContextBuilder._compact_group_summary(str(group_profile.get("group_memory_summary", "") or ""))
         lines = [
             f"群模式：{group_profile.get('mode', 'social')}",
             f"知识域偏向：{group_profile.get('knowledge_domain', 'general')}（仅作理解倾向，不是每次都要显式提到）",
@@ -291,7 +289,7 @@ class ContextBuilder:
             f"表达松弛度：{group_profile.get('expressiveness_style', '克制')}",
             f"称呼强度：{group_profile.get('address_style', '低频称呼，默认直接接话')}",
             f"可能相关的话题背景：{focus}" if focus else "",
-            f"群长期摘要：{summary}" if summary else "",
+            f"群长期摘要关键句：{summary}" if summary else "",
             f"历史推断社交风格：{ContextBuilder._build_style_summary(group_profile.get('group_memory_style', {}))}"
             if group_profile.get("group_memory_style")
             else "",
@@ -313,6 +311,50 @@ class ContextBuilder:
             ]
         ).strip(" /")
 
+    @staticmethod
+    def _compact_group_summary(summary_text: str, max_chars: int = 420, max_sentences: int = 6) -> str:
+        text = str(summary_text or "").strip()
+        if not text:
+            return ""
+        text = re.sub(r"\s+", " ", text.replace("\n", " ").replace("\r", " ")).strip()
+        if len(text) <= max_chars:
+            return text
+
+        # 长摘要按句提炼，优先保留“结论/风险/动作/配置”等关键词句，避免简单截断丢重点。
+        sentences = [part.strip(" ，,；;。.!?！？:：") for part in re.split(r"[。！？!?；;\n]+", text) if part.strip()]
+        if not sentences:
+            return text[: max_chars - 3] + "..."
+
+        key_patterns = [
+            r"结论|核心|重点|关键|建议|方案|步骤|原因|影响|风险|注意|问题|异常|报错|故障|超时|阻塞",
+            r"配置|参数|阈值|策略|限制|回退|优化|修复|排查|上线|回滚|依赖|版本|兼容",
+        ]
+        scored: List[tuple[int, int, str]] = []
+        for idx, sentence in enumerate(sentences):
+            score = 0
+            for pattern in key_patterns:
+                if re.search(pattern, sentence, flags=re.IGNORECASE):
+                    score += 3
+            if re.search(r"\d", sentence):
+                score += 1
+            if 8 <= len(sentence) <= 80:
+                score += 1
+            if idx == 0 or idx == len(sentences) - 1:
+                score += 1
+            scored.append((score, idx, sentence))
+
+        chosen_indexes = {0, len(sentences) - 1}
+        for _, idx, _ in sorted(scored, key=lambda x: (-x[0], x[1])):
+            chosen_indexes.add(idx)
+            if len(chosen_indexes) >= max_sentences:
+                break
+
+        chosen = [sentences[idx] for idx in sorted(chosen_indexes)]
+        merged = "；".join([item for item in chosen if item]).strip("；")
+        if len(merged) <= max_chars:
+            return merged
+        return merged[: max_chars - 3].rstrip(" ，,；;。.!?！？:：") + "..."
+
     @staticmethod
     def _build_quote_prompt(quote_context: Dict) -> str:
         if not quote_context: