feat: 性能插桩、缓存清除端点、LLM top_p 与 JSON 强制输出

- chat_routes.py: 新增 8 阶段性能计时（stages_ms）+ SSE 输出
- sync_routes.py: 新增 POST /cache/clear 缓存清除端点
- engine.py: LLM 调用增加 top_p 参数
- intent_analyzer.py: response_format 加配置开关（INTENT_RESPONSE_FORMAT）

api/chat_routes.py

@@ -2196,6 +2196,9 @@ def rag():
         import re
         start_time = _time.time()
         full_answer = []
+        # === 性能插桩：各阶段耗时 ===
+        _t = {'intent': 0, 'cache': 0, 'search': 0, 'vlm': 0, 'image': 0, 'rescue': 0, 'llm': 0, 'post': 0}
+        _t_prev = start_time
 
         try:
             # 0. 意图分析（改写 + 双层判断）
@@ -2227,7 +2230,6 @@ def rag():
                     # 构建上下文
                     context_text = ""
                     if history:
-                        # 提取最近的助手回答
                         for msg in reversed(history):
                             if msg.get("role") == "assistant":
                                 context_text = msg.get("content", "")
@@ -2260,7 +2262,6 @@ def rag():
 
 请直接回答用户问题。"""
 
-                    # 流式生成回答
                     for content in call_llm_stream(
                         client,
                         prompt=user_prompt,
@@ -2274,14 +2275,15 @@ def rag():
                         full_answer.append(content)
                         yield f"data: {json.dumps({'type': 'chunk', 'content': content}, ensure_ascii=False)}\n\n"
 
-                    # 发送完成事件
                     yield f"data: {json.dumps({'type': 'finish', 'answer': ''.join(full_answer), 'sources': []}, ensure_ascii=False)}\n\n"
-                    return  # 直接返回，不执行后续检索
+                    return
 
             except Exception as e:
                 logger.warning(f"意图分析失败: {e}，继续执行检索流程")
 
             retrieval_query = intent.rewritten_query if (intent and intent.rewritten_query) else message
+            _t['intent'] = round((_time.time() - _t_prev) * 1000, 1)
+            _t_prev = _time.time()
 
             # 1.5 语义缓存检查（跳过检索+生成全流程）
             _semantic_cache_emb = None
@@ -2343,6 +2345,8 @@ def rag():
 
             # 1. 发送开始事件
             yield f"data: {json.dumps({'type': 'start', 'message': '正在检索知识库...'}, ensure_ascii=False)}\n\n"
+            _t['cache'] = round((_time.time() - _t_prev) * 1000, 1)
+            _t_prev = _time.time()
 
             # 2. 执行混合检索（扩大召回数量，确保图片切片有机会被召回）
             # 如果意图分析生成了子查询（对比类），传给搜索引擎并行检索
@@ -2619,6 +2623,8 @@ def rag():
             # 2.5. 懒加载增强（Phase 4）
             # 暂时禁用：VLM 调用耗时过长，可能导致请求超时
             # TODO: 后续可改为异步后台任务
+            _t['search'] = round((_time.time() - _t_prev) * 1000, 1)
+            _t_prev = _time.time()
             try:
                 import asyncio
                 from knowledge.lazy_enhance import enhance_retrieved_chunks
@@ -2626,9 +2632,13 @@ def rag():
                 asyncio.run(enhance_retrieved_chunks(contexts, message, kb_name))
             except Exception as e:
                 logger.warning(f"懒加载增强失败: {e}")
+            _t['vlm'] = round((_time.time() - _t_prev) * 1000, 1)
+            _t_prev = _time.time()
 
             # 3. 选择要展示的图片（Phase 5）
             selected_images = select_images(contexts, message)
+            _t['image'] = round((_time.time() - _t_prev) * 1000, 1)
+            _t_prev = _time.time()
 
             # 调试事件：图片选择详情
             if IS_DEV:
@@ -2754,12 +2764,16 @@ def rag():
                 yield f"data: {json.dumps({'type': 'context_built', 'data': {'chunk_count': len(text_used), 'context_length': len(enhanced_context), 'budget_max_chars': CONTEXT_MAX_CHARS, 'min_score_filter': RERANK_CONTEXT_MIN_SCORE, 'confidence_top3': _confidence_score, 'score_stats': {'max': max(_scores) if _scores else 0, 'min': min(_scores) if _scores else 0, 'avg': round(sum(_scores)/len(_scores), 4) if _scores else 0}, 'context_preview': enhanced_context[:500], 'chunks_used': [{'source': ctx.get('meta',{}).get('source',''), 'page': ctx.get('meta',{}).get('page',0), 'score': ctx.get('score',0), 'preview': (ctx.get('doc','') or '')[:100]} for ctx in text_used]}}, ensure_ascii=False)}\n\n"
 
             # 5. 流式生成回答
+            _t['rescue'] = round((_time.time() - _t_prev) * 1000, 1)
+            _t_prev = _time.time()
             from core.engine import get_engine
             engine = get_engine()
 
             for token in engine.generate_answer_stream(message, enhanced_context, history):
                 full_answer.append(token)
                 yield f"data: {json.dumps({'type': 'chunk', 'content': token}, ensure_ascii=False)}\n\n"
+            _t['llm'] = round((_time.time() - _t_prev) * 1000, 1)
+            _t_prev = _time.time()
 
             # 6. P0：答案对齐过滤器
             # 从 LLM 回答中提取图号引用，过滤图片选择结果
@@ -2873,12 +2887,16 @@ def rag():
                     if step.get('name') == 'rerank' and step.get('applied'):
                         rerank_time = step.get('time_ms', 0)
                         rerank_cached = step.get('cached', False)
+                _t['post'] = round((_time.time() - _t_prev) * 1000, 1)
                 finish_event["timing"] = {
                     "total_search_ms": timing_info.get('total_ms', 0),
                     "rerank_ms": rerank_time,
                     "rerank_cached": rerank_cached,
-                    "total_ms": duration_ms
+                    "total_ms": duration_ms,
+                    "stages_ms": _t
                 }
+                # 记录到日志
+                logger.info(f"[性能] 总{duration_ms}ms | 意图{_t['intent']:.0f} 缓存{_t['cache']:.0f} 检索{_t['search']:.0f} VLM{_t['vlm']:.0f} 图片{_t['image']:.0f} 救援{_t['rescue']:.0f} LLM{_t['llm']:.0f} 后处理{_t['post']:.0f}")
             # 10.5 写入语义缓存
             try:
                 from core.semantic_cache import get_semantic_cache