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

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- 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）
This commit is contained in:
lacerate551
2026-06-21 20:25:12 +08:00
parent c6a17ad1e4
commit 6d5a4b5b5e
4 changed files with 76 additions and 12 deletions
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28
api/chat_routes.py
View File

@@ -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

45
api/sync_routes.py
View File

@@ -315,3 +315,48 @@ def stop_sync_monitor() -> Tuple[Any, int]:
message="操作失败",
http_status=500
)
# ==================== 缓存管理 API ====================
@sync_bp.route('/cache/clear', methods=['POST'])
@require_gateway_auth
def clear_all_caches() -> Tuple[Any, int]:
"""
清除所有缓存层（Query / Embedding / Rerank / Semantic / Intent 精确缓存）
用于测试和调试，不影响向量库数据。
Returns:
{"success": true, "message": "缓存已清除", "cleared": [...]}
"""
cleared = []
# 1. 精确匹配缓存（Query / Embedding / Rerank）
try:
from core.cache import get_cache_manager
cache = get_cache_manager()
if cache:
cache.clear_all()
cleared.append("query_cache")
cleared.append("embedding_cache")
cleared.append("rerank_cache")
except Exception as e:
logger.warning(f"清除精确缓存失败: {e}")
# 2. 语义缓存（FAISS）
try:
from core.semantic_cache import get_semantic_cache
sc = get_semantic_cache()
if sc:
sc.clear()
cleared.append("semantic_cache")
except Exception as e:
logger.warning(f"清除语义缓存失败: {e}")
logger.info(f"缓存清除完成: {cleared}")
return jsonify({
"success": True,
"message": f"已清除 {len(cleared)} 层缓存",
"cleared": cleared
})