From 3bc0b821a199da5054a149650f828f6e13d80911 Mon Sep 17 00:00:00 2001
From: Teery <96712976+Alexisxty@users.noreply.github.com>
Date: Sun, 23 Feb 2025 16:07:33 +0800
Subject: [PATCH] =?UTF-8?q?update:=20=E4=BF=AE=E6=94=B9=E4=BA=86=E6=9C=8D?=
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* update: optimize performance test files

* update:异步测试方法

---------

Co-authored-by: hrz <1710360675@qq.com>
---
 core/utils/llm.py     | 140 +++++++++---
 performance_tester.py | 507 +++++++++++++++++++++++++++---------------
 2 files changed, 440 insertions(+), 207 deletions(-)

diff --git a/core/utils/llm.py b/core/utils/llm.py
index f48c103d..8fdec09b 100644
--- a/core/utils/llm.py
+++ b/core/utils/llm.py
@@ -1,5 +1,13 @@
 import os
 import sys
+import asyncio
+from typing import List, Dict, Any
+
+# 添加项目根目录到Python路径
+current_dir = os.path.dirname(os.path.abspath(__file__))
+project_root = os.path.abspath(os.path.join(current_dir, "..", ".."))
+sys.path.insert(0, project_root)
+
 from config.logger import setup_logging
 import importlib
 from datetime import datetime
@@ -21,39 +29,115 @@ def create_instance(class_name, *args, **kwargs):
     raise ValueError(f"不支持的LLM类型: {class_name}，请检查该配置的type是否设置正确")
 
 
-if __name__ == "__main__":
+async def test_single_model(llm_name: str, llm_config: Dict[str, Any], test_prompt: str, config: Dict[str, Any]) -> Dict[str, Any]:
+    """异步测试单个模型"""
+    try:
+        # 获取实际的LLM类型
+        llm_type = llm_config["type"] if "type" in llm_config else llm_name
+        llm = create_instance(llm_type, llm_config)
+        
+        # 开始测试
+        dialogue = []
+        dialogue.append({"role": "system", "content": config.get("prompt")})
+        dialogue.append({"role": "user", "content": test_prompt})
+        
+        start_time = datetime.now()
+        llm_responses = llm.response("test", dialogue)
+        response_message = []
+        first_response_time = None
+        total_response_time = None
+        start = 0
+        full_response = ""
+
+        for content in llm_responses:
+            response_message.append(content)
+            full_response += content
+
+            if is_segment(response_message):
+                segment_text = "".join(response_message[start:])
+                segment_text = get_string_no_punctuation_or_emoji(segment_text)
+                if len(segment_text) > 0:
+                    if first_response_time is None:
+                        first_response_time = (datetime.now() - start_time).total_seconds()
+                    start = len(response_message)
+
+        total_response_time = (datetime.now() - start_time).total_seconds()
+        
+        return {
+            "name": llm_name,
+            "type": llm_type,
+            "first_response_time": first_response_time,
+            "total_response_time": total_response_time,
+            "response_length": len(full_response),
+            "status": "成功",
+            "response": full_response
+        }
+
+    except Exception as e:
+        print(f"测试 {llm_name} 时发生错误: {str(e)}")
+        return {
+            "name": llm_name,
+            "type": llm_config.get("type", llm_name),
+            "first_response_time": None,
+            "total_response_time": None,
+            "response_length": 0,
+            "status": f"失败 - {str(e)}",
+            "response": ""
+        }
+
+
+async def main():
     """
-      响应速度测试
+    LLM模型响应速度测试和排行（异步版本）
     """
     config = read_config(get_project_dir() + "config.yaml")
-    llm = create_instance(
-        config["selected_module"]["LLM"]
-        if not "type" in config["LLM"][config["selected_module"]["LLM"]]
-        else
-        config["LLM"][config["selected_module"]["LLM"]]["type"],
-        config["LLM"][config["selected_module"]["LLM"]]
-    )
+    test_prompt = "你好小智"
+    
+    print("开始并发测试所有模型...")
+    
+    # 创建所有模型的测试任务
+    tasks = []
+    for llm_name, llm_config in config["LLM"].items():
+        task = asyncio.create_task(test_single_model(llm_name, llm_config, test_prompt, config))
+        tasks.append(task)
+    
+    # 等待所有测试完成
+    test_results = await asyncio.gather(*tasks)
+    
+    # 打印测试结果排行榜
+    print("\n========= LLM模型性能测试排行榜 =========")
+    print("测试提示词:", test_prompt)
+    
+    # 过滤出成功的结果，并确保数值有效
+    successful_results = [r for r in test_results if r["status"] == "成功" and r["first_response_time"] is not None]
+    
+    if successful_results:
+        print("\n1. 首次响应时间排行:")
+        sorted_by_first = sorted(successful_results, key=lambda x: x["first_response_time"])
+        for i, result in enumerate(sorted_by_first, 1):
+            print(f"{i}. {result['name']}({result['type']}) - {result['first_response_time']:.2f}秒")
+            print(f"   响应内容: {result['response'][:50]}...")  # 只显示前50个字符
 
-    start_time = datetime.now()
+        print("\n2. 总响应时间排行:")
+        sorted_by_total = sorted(successful_results, key=lambda x: x["total_response_time"] or float('inf'))
+        for i, result in enumerate(sorted_by_total, 1):
+            if result["total_response_time"] is not None:
+                print(f"{i}. {result['name']}({result['type']}) - {result['total_response_time']:.2f}秒")
 
-    dialogue = []
-    dialogue.append({"role": "system", "content": config.get("prompt")})
-    dialogue.append({"role": "user", "content": "你好小智"})
-    llm_responses = llm.response("test", dialogue)
-    response_message = []
-    first_text = None
-    start = 0
+        print("\n3. 响应长度比较:")
+        sorted_by_length = sorted(successful_results, key=lambda x: x["response_length"], reverse=True)
+        for i, result in enumerate(sorted_by_length, 1):
+            print(f"{i}. {result['name']}({result['type']}) - {result['response_length']}字符")
+    else:
+        print("\n没有成功完成测试的模型。")
 
-    for content in llm_responses:
-        response_message.append(content)
+    if len(test_results) != len(successful_results):
+        print("\n测试失败的模型:")
+        failed_results = [r for r in test_results if r["status"] != "成功" or r["first_response_time"] is None]
+        for result in failed_results:
+            print(f"- {result['name']}({result['type']}): {result['status']}")
 
-        if is_segment(response_message):
-            segment_text = "".join(response_message[start:])
-            segment_text = get_string_no_punctuation_or_emoji(segment_text)
-            if len(segment_text) > 0:
-                if first_text is None:
-                    first_text = segment_text
-                    print("大模型首次返回耗时：" + str(datetime.now() - start_time))
-                start = len(response_message)
 
-    print("大模型返回总耗时：" + str(datetime.now() - start_time))
+if __name__ == "__main__":
+    # 运行异步主函数
+    asyncio.run(main())
diff --git a/performance_tester.py b/performance_tester.py
index 557cc1e5..8bae2df0 100644
--- a/performance_tester.py
+++ b/performance_tester.py
@@ -1,27 +1,27 @@
 import time
+import aiohttp
+import asyncio
 from tabulate import tabulate
-from typing import Dict
+from typing import Dict, List
 from core.utils.llm import create_instance as create_llm_instance
 from core.utils.tts import create_instance as create_tts_instance
 from core.utils.util import read_config
 import statistics
 from config.settings import get_config_file
-from concurrent.futures import ThreadPoolExecutor
 import inspect
 import os
-import requests
 import logging
 
 # 设置全局日志级别为WARNING，抑制INFO级别日志
 logging.basicConfig(level=logging.WARNING)
 
-class PerformanceTester:
+
+class AsyncPerformanceTester:
     def __init__(self):
         self.config = read_config(get_config_file())
-        # 从配置读取测试句子，如果不存在则使用默认
         self.test_sentences = self.config.get("module_test", {}).get(
-            "test_sentences", 
-            ["你好，请介绍一下你自己", "What's the weather like today?", 
+            "test_sentences",
+            ["你好，请介绍一下你自己", "What's the weather like today?",
              "请用100字概括量子计算的基本原理和应用前景"]
         )
         self.results = {
@@ -30,258 +30,407 @@ class PerformanceTester:
             "combinations": []
         }
 
-    def _test_llm(self, llm_name: str, config: Dict) -> Dict:
-        """测试单个LLM性能"""
+    async def _check_ollama_service(self, base_url: str, model_name: str) -> bool:
+        """异步检查Ollama服务状态"""
+        async with aiohttp.ClientSession() as session:
+            try:
+                # 检查服务是否可用
+                async with session.get(f"{base_url}/api/version") as response:
+                    if response.status != 200:
+                        print(f"🚫 Ollama服务未启动或无法访问: {base_url}")
+                        return False
+
+                # 检查模型是否存在
+                async with session.get(f"{base_url}/api/tags") as response:
+                    if response.status == 200:
+                        data = await response.json()
+                        models = data.get("models", [])
+                        if not any(model["name"] == model_name for model in models):
+                            print(f"🚫 Ollama模型 {model_name} 未找到，请先使用 ollama pull {model_name} 下载")
+                            return False
+                    else:
+                        print(f"🚫 无法获取Ollama模型列表")
+                        return False
+                return True
+            except Exception as e:
+                print(f"🚫 无法连接到Ollama服务: {str(e)}")
+                return False
+
+    async def _test_tts(self, tts_name: str, config: Dict) -> Dict:
+        """异步测试单个TTS性能"""
         try:
-            # 跳过未配置密钥的模块
-            if "api_key" in config and any(x in config["api_key"] for x in ["你的", "placeholder", "sk-xxx"]):
-                print(f"🚫 跳过未配置的LLM: {llm_name}")
-                return {"errors": 1}
-            
+            logging.getLogger("core.providers.tts.base").setLevel(logging.WARNING)
+
+            token_fields = ["access_token", "api_key", "token"]
+            if any(field in config and any(x in config[field] for x in ["你的", "placeholder"]) for field in
+                   token_fields):
+                print(f"⏭️  TTS {tts_name} 未配置access_token/api_key，已跳过")
+                return {"name": tts_name, "type": "tts", "errors": 1}
+
+            module_type = config.get('type', tts_name)
+            tts = create_tts_instance(
+                module_type,
+                config,
+                delete_audio_file=True
+            )
+
+            print(f"🎵 测试 TTS: {tts_name}")
+
+            tmp_file = tts.generate_filename()
+            await tts.text_to_speak("连接测试", tmp_file)
+
+            if not tmp_file or not os.path.exists(tmp_file):
+                print(f"❌ {tts_name} 连接失败")
+                return {"name": tts_name, "type": "tts", "errors": 1}
+
+            total_time = 0
+            test_count = len(self.test_sentences[:2])
+
+            for i, sentence in enumerate(self.test_sentences[:2], 1):
+                start = time.time()
+                tmp_file = tts.generate_filename()
+                await tts.text_to_speak(sentence, tmp_file)
+                duration = time.time() - start
+                total_time += duration
+
+                if tmp_file and os.path.exists(tmp_file):
+                    print(f"✓ {tts_name} [{i}/{test_count}]")
+                else:
+                    print(f"✗ {tts_name} [{i}/{test_count}]")
+                    return {"name": tts_name, "type": "tts", "errors": 1}
+
+            return {
+                "name": tts_name,
+                "type": "tts",
+                "avg_time": total_time / test_count,
+                "errors": 0
+            }
+
+        except Exception as e:
+            print(f"⚠️ {tts_name} 测试失败: {str(e)}")
+            return {"name": tts_name, "type": "tts", "errors": 1}
+
+    async def _test_llm(self, llm_name: str, config: Dict) -> Dict:
+        """异步测试单个LLM性能"""
+        try:
+            # 对于Ollama，跳过api_key检查并进行特殊处理
+            if llm_name == "Ollama":
+                base_url = config.get('base_url', 'http://localhost:11434')
+                model_name = config.get('model_name')
+                if not model_name:
+                    print(f"🚫 Ollama未配置model_name")
+                    return {"name": llm_name, "type": "llm", "errors": 1}
+
+                if not await self._check_ollama_service(base_url, model_name):
+                    return {"name": llm_name, "type": "llm", "errors": 1}
+            else:
+                if "api_key" in config and any(x in config["api_key"] for x in ["你的", "placeholder", "sk-xxx"]):
+                    print(f"🚫 跳过未配置的LLM: {llm_name}")
+                    return {"name": llm_name, "type": "llm", "errors": 1}
+
             # 获取实际类型（兼容旧配置）
             module_type = config.get('type', llm_name)
             llm = create_llm_instance(module_type, config)
-            
+
             # 统一使用UTF-8编码
             test_sentences = [s.encode('utf-8').decode('utf-8') for s in self.test_sentences]
-            
-            total_time = 0
-            first_token_times = []
-            valid_times = []
-            
+
+            # 创建所有句子的测试任务
+            sentence_tasks = []
             for sentence in test_sentences:
-                sentence_start = time.time()  # 记录整句开始时间
-                first_token_received = False
-                
-                # 遍历响应流
-                for chunk in llm.response("perf_test", [{"role": "user", "content": sentence}]):
-                    if not first_token_received and chunk.strip() != '':
-                        first_token_times.append(time.time() - sentence_start)
-                        first_token_received = True
-                
-                # 计算整句耗时
-                sentence_duration = time.time() - sentence_start
-                total_time += sentence_duration
-                valid_times.append(sentence_duration)
-            
-            # 新增有效性检查
-            if len(first_token_times) == 0 or len(valid_times) == 0:
+                sentence_tasks.append(self._test_single_sentence(llm_name, llm, sentence))
+
+            # 并发执行所有句子测试
+            sentence_results = await asyncio.gather(*sentence_tasks)
+
+            # 处理结果
+            valid_results = [r for r in sentence_results if r is not None]
+            if not valid_results:
                 print(f"⚠️  {llm_name} 无有效数据，可能配置错误")
-                return {"errors": 1}
-                
-            # 过滤异常数据（超过3倍标准差）
-            mean = statistics.mean(valid_times)
-            stdev = statistics.stdev(valid_times) if len(valid_times) > 1 else 0
-            filtered_times = [t for t in valid_times if t <= mean + 3*stdev]
-            
-            # 当有效数据不足时标记错误
+                return {"name": llm_name, "type": "llm", "errors": 1}
+
+            first_token_times = [r["first_token_time"] for r in valid_results]
+            response_times = [r["response_time"] for r in valid_results]
+
+            # 过滤异常数据
+            mean = statistics.mean(response_times)
+            stdev = statistics.stdev(response_times) if len(response_times) > 1 else 0
+            filtered_times = [t for t in response_times if t <= mean + 3 * stdev]
+
             if len(filtered_times) < len(test_sentences) * 0.5:
                 print(f"⚠️  {llm_name} 有效数据不足，可能网络不稳定")
-                return {"errors": 1}
+                return {"name": llm_name, "type": "llm", "errors": 1}
 
             return {
-                "avg_response": total_time / len(test_sentences),
-                "avg_first_token": sum(first_token_times)/len(first_token_times),
+                "name": llm_name,
+                "type": "llm",
+                "avg_response": sum(response_times) / len(response_times),
+                "avg_first_token": sum(first_token_times) / len(first_token_times),
                 "std_first_token": statistics.stdev(first_token_times) if len(first_token_times) > 1 else 0,
-                "std_response": statistics.stdev(valid_times) if len(valid_times) > 1 else 0,
+                "std_response": statistics.stdev(response_times) if len(response_times) > 1 else 0,
                 "errors": 0
             }
         except Exception as e:
             print(f"LLM {llm_name} 测试失败: {str(e)}")
-            return {"errors": 1}
+            return {"name": llm_name, "type": "llm", "errors": 1}
 
-    def _test_tts(self, tts_name: str, config: Dict) -> Dict:
-        """测试单个TTS性能"""
+    async def _test_single_sentence(self, llm_name: str, llm, sentence: str) -> Dict:
+        """测试单个句子的性能"""
         try:
-            # 关闭详细日志
-            logging.getLogger("core.providers.tts.base").setLevel(logging.WARNING)
-            
-            # 跳过未配置密钥的模块
-            token_fields = ["access_token", "api_key", "token"]
-            if any(field in config and any(x in config[field] for x in ["你的", "placeholder"]) for field in token_fields):
-                print(f"⏭️  TTS {tts_name} 未配置access_token/api_key，已跳过")
-                return {"errors": 1}
-            
-            # 获取实际类型（兼容旧配置）
-            module_type = config.get('type', tts_name)
-            tts = create_tts_instance(
-                module_type,
-                config, 
-                delete_audio_file=True  # 确保参数名称正确
-            )
-            
-            # 简化后的输出
-            print(f"\n🎵 正在测试 TTS: {tts_name}")
-            print(f"🔊 测试 {tts_name}：", end="", flush=True)
-            
-            # 连接测试
-            test_conn = tts.to_tts("连接测试")
-            if not os.path.exists(test_conn):
-                print("❌ 连接失败")
-                return {"errors": 1}
-            else:
-                print("✅")
-    
-            total_time = 0
-            test_count = len(self.test_sentences[:2])
-            
-            for i, sentence in enumerate(self.test_sentences[:2], 1):
-                start = time.time()
-                file_path = tts.to_tts(sentence)
-                duration = time.time() - start
-                total_time += duration
-                
-                # 显示简单的进度标识
-                if os.path.exists(file_path):
-                    print(f"✓[{i}/{test_count}]", end="", flush=True)
-                else:
-                    print(f"✗[{i}/{test_count}]", end="", flush=True)
-            
-            print()  # 换行
-            return {"avg_time": total_time / test_count, "errors": 0}
-    
-        except requests.exceptions.ConnectionError:
-            print(f"\n⛔ {tts_name} 无法连接服务端")
-            return {"errors": 1}
-        except Exception as e:
-            print(f"\n⚠️ {tts_name} 测试失败: {str(e)}")
-            return {"errors": 1}
+            print(f"📝 {llm_name} 开始测试: {sentence[:20]}...")
+            sentence_start = time.time()
+            first_token_received = False
+            first_token_time = None
 
-    def run(self):
-        """执行全量测试并自动跳过未配置的模块"""
-        print("🔍 开始自动检测已配置的模块...")
-        
-        # 测试所有LLM
-        for llm_name, config in self.config.get("LLM", {}).items():
-            # 特殊处理CozeLLM的配置检查
-            if llm_name == "CozeLLM":
-                if any(x in config.get("bot_id", "") for x in ["你的"]) \
-                or any(x in config.get("user_id", "") for x in ["你的"]):
-                    print(f"⏭️  LLM {llm_name} 未配置bot_id/user_id，已跳过")
-                    continue
-            # 通用的api_key配置检查
-            if "api_key" in config and any(x in config["api_key"] for x in ["你的", "placeholder"]):
-                print(f"⏭️  LLM {llm_name} 未配置api_key，已跳过")
-                continue
-                
-            print(f"🚀 正在测试 LLM: {llm_name}")
-            self.results["llm"][llm_name] = self._test_llm(llm_name, config)
-        
-        # 测试所有TTS
-        for tts_name, config in self.config.get("TTS", {}).items():
-            # 根据不同服务的token字段检测
-            token_fields = ["access_token", "api_key", "token"]
-            if any(field in config and any(x in config[field] for x in ["你的", "placeholder"]) for field in token_fields):
-                print(f"⏭️  TTS {tts_name} 未配置access_token/api_key，已跳过")
-                continue
-                
-            print(f"🎵 正在测试 TTS: {tts_name}")
-            self.results["tts"][tts_name] = self._test_tts(tts_name, config)
-        
-        # 生成组合建议
-        self._generate_combinations()
-        self._print_results()
+            async def process_response():
+                nonlocal first_token_received, first_token_time
+                for chunk in llm.response("perf_test", [{"role": "user", "content": sentence}]):
+                    if not first_token_received and chunk.strip() != '':
+                        first_token_time = time.time() - sentence_start
+                        first_token_received = True
+                        print(f"✓ {llm_name} 首个Token: {first_token_time:.3f}s")
+                    yield chunk
+
+            response_chunks = []
+            async for chunk in process_response():
+                response_chunks.append(chunk)
+
+            response_time = time.time() - sentence_start
+            print(f"✓ {llm_name} 完成响应: {response_time:.3f}s")
+
+            if first_token_time is None:
+                first_token_time = response_time  # 如果没有检测到first token，使用总响应时间
+
+            return {
+                "name": llm_name,
+                "type": "llm",
+                "first_token_time": first_token_time,
+                "response_time": response_time
+            }
+        except Exception as e:
+            print(f"⚠️ {llm_name} 句子测试失败: {str(e)}")
+            return None
 
     def _generate_combinations(self):
         """生成最佳组合建议"""
-        # 调整过滤条件，例如设为 >= 0.05
         valid_llms = [
-            k for k, v in self.results["llm"].items() 
+            k for k, v in self.results["llm"].items()
             if v["errors"] == 0 and v["avg_first_token"] >= 0.05
         ]
         valid_tts = [k for k, v in self.results["tts"].items() if v["errors"] == 0]
 
+        # 找出基准值
+        min_first_token = min([self.results["llm"][llm]["avg_first_token"] for llm in valid_llms]) if valid_llms else 1
+        min_tts_time = min([self.results["tts"][tts]["avg_time"] for tts in valid_tts]) if valid_tts else 1
+
         for llm in valid_llms:
             for tts in valid_tts:
-                llm_weight = 0.8 if self.results["llm"][llm]["avg_first_token"] < 1.0 else 0.6
-                tts_weight = 1 - llm_weight
-                score = (
-                    self.results["llm"][llm]["avg_first_token"] * llm_weight +
-                    self.results["tts"][tts]["avg_time"] * tts_weight
-                )
+                # 计算相对性能分数（越小越好）
+                llm_score = self.results["llm"][llm]["avg_first_token"] / min_first_token
+                tts_score = self.results["tts"][tts]["avg_time"] / min_tts_time
+
+                # 计算稳定性分数（标准差/平均值，越小越稳定）
+                llm_stability = self.results["llm"][llm]["std_first_token"] / self.results["llm"][llm][
+                    "avg_first_token"]
+
+                # 综合得分（考虑性能和稳定性）
+                # 性能权重0.7，稳定性权重0.3
+                llm_final_score = llm_score * 0.7 + llm_stability * 0.3
+
+                # 总分 = LLM得分(70%) + TTS得分(30%)
+                total_score = llm_final_score * 0.7 + tts_score * 0.3
+
                 self.results["combinations"].append({
                     "llm": llm,
                     "tts": tts,
-                    "score": score,
+                    "score": total_score,
                     "details": {
                         "llm_first_token": self.results["llm"][llm]["avg_first_token"],
+                        "llm_stability": llm_stability,
                         "tts_time": self.results["tts"][tts]["avg_time"]
                     }
                 })
 
-        # 按综合得分排序
+        # 分数越小越好
         self.results["combinations"].sort(key=lambda x: x["score"])
 
     def _print_results(self):
-        """控制台输出结果"""
-        # LLM结果表格
+        """打印测试结果"""
         llm_table = []
         for name, data in self.results["llm"].items():
             if data["errors"] == 0:
+                stability = data["std_first_token"] / data["avg_first_token"]
                 llm_table.append([
-                    name,
-                    f"{data['avg_first_token']:.3f}s",
-                    f"{data['avg_response']:.3f}s"
+                    name,  # 不需要固定宽度，让tabulate自己处理对齐
+                    f"{data['avg_first_token']:.3f}秒",
+                    f"{data['avg_response']:.3f}秒",
+                    f"{stability:.3f}"
                 ])
 
         if llm_table:
             print("\nLLM 性能排行:")
             print(tabulate(
                 llm_table,
-                headers=["模块名称", "平均首Token时间", "平均总响应时间"],
-                tablefmt="github"
+                headers=["模型名称", "首字耗时", "总耗时", "稳定性"],
+                tablefmt="github",
+                colalign=("left", "right", "right", "right"),
+                disable_numparse=True
             ))
         else:
             print("\n⚠️ 没有可用的LLM模块进行测试。")
 
-        # TTS结果表格
         tts_table = []
         for name, data in self.results["tts"].items():
             if data["errors"] == 0:
                 tts_table.append([
-                    name,
-                    f"{data['avg_time']:.3f}s"
+                    name,  # 不需要固定宽度
+                    f"{data['avg_time']:.3f}秒"
                 ])
 
         if tts_table:
             print("\nTTS 性能排行:")
             print(tabulate(
                 tts_table,
-                headers=["模块名称", "平均合成时间"],
-                tablefmt="github"
+                headers=["模型名称", "合成耗时"],
+                tablefmt="github",
+                colalign=("left", "right"),
+                disable_numparse=True
             ))
         else:
             print("\n⚠️ 没有可用的TTS模块进行测试。")
 
-        # 最佳组合建议
         if self.results["combinations"]:
-            print("\n推荐配置组合 (综合响应速度):")
+            print("\n推荐配置组合 (得分越小越好):")
             combo_table = []
-            for combo in self.results["combinations"][:5]:  # 显示前5名
+            for combo in self.results["combinations"][:5]:
                 combo_table.append([
-                    f"{combo['llm']} + {combo['tts']}",
+                    f"{combo['llm']} + {combo['tts']}",  # 不需要固定宽度
                     f"{combo['score']:.3f}",
-                    f"{combo['details']['llm_first_token']:.3f}s",
-                    f"{combo['details']['tts_time']:.3f}s"
+                    f"{combo['details']['llm_first_token']:.3f}秒",
+                    f"{combo['details']['llm_stability']:.3f}",
+                    f"{combo['details']['tts_time']:.3f}秒"
                 ])
-            
+
             print(tabulate(
                 combo_table,
-                headers=["组合方案", "综合得分", "LLM首Token", "TTS合成"],
-                tablefmt="github"
+                headers=["组合方案", "综合得分", "LLM首字耗时", "稳定性", "TTS合成耗时"],
+                tablefmt="github",
+                colalign=("left", "right", "right", "right", "right"),
+                disable_numparse=True
             ))
         else:
             print("\n⚠️ 没有可用的模块组合建议。")
 
-    def _execute_with_timeout(self, func, args=(), kwargs={}, timeout=None):
-        with ThreadPoolExecutor(max_workers=1) as executor:
-            future = executor.submit(func, *args, **kwargs)
-            try:
-                result = future.result(timeout)
-                return list(result) if inspect.isgenerator(result) else result
-            except TimeoutError:
-                raise Exception("操作超时")
+    def _process_results(self, all_results):
+        """处理测试结果"""
+        for result in all_results:
+            if result["errors"] == 0:
+                if result["type"] == "llm":
+                    self.results["llm"][result["name"]] = result
+                else:
+                    self.results["tts"][result["name"]] = result
+
+    async def run(self):
+        """执行全量异步测试"""
+        print("🔍 开始筛选可用模块...")
+
+        # 创建所有测试任务
+        all_tasks = []
+
+        # LLM测试任务
+        for llm_name, config in self.config.get("LLM", {}).items():
+            # 检查配置有效性
+            if llm_name == "CozeLLM":
+                if any(x in config.get("bot_id", "") for x in ["你的"]) \
+                        or any(x in config.get("user_id", "") for x in ["你的"]):
+                    print(f"⏭️  LLM {llm_name} 未配置bot_id/user_id，已跳过")
+                    continue
+            elif "api_key" in config and any(x in config["api_key"] for x in ["你的", "placeholder", "sk-xxx"]):
+                print(f"⏭️  LLM {llm_name} 未配置api_key，已跳过")
+                continue
+
+            # 对于Ollama，先检查服务状态
+            if llm_name == "Ollama":
+                base_url = config.get('base_url', 'http://localhost:11434')
+                model_name = config.get('model_name')
+                if not model_name:
+                    print(f"🚫 Ollama未配置model_name")
+                    continue
+
+                if not await self._check_ollama_service(base_url, model_name):
+                    continue
+
+            print(f"📋 添加LLM测试任务: {llm_name}")
+            module_type = config.get('type', llm_name)
+            llm = create_llm_instance(module_type, config)
+
+            # 为每个句子创建独立任务
+            for sentence in self.test_sentences:
+                sentence = sentence.encode('utf-8').decode('utf-8')
+                all_tasks.append(self._test_single_sentence(llm_name, llm, sentence))
+
+        # TTS测试任务
+        for tts_name, config in self.config.get("TTS", {}).items():
+            token_fields = ["access_token", "api_key", "token"]
+            if any(field in config and any(x in config[field] for x in ["你的", "placeholder"]) for field in
+                   token_fields):
+                print(f"⏭️  TTS {tts_name} 未配置access_token/api_key，已跳过")
+                continue
+            print(f"🎵 添加TTS测试任务: {tts_name}")
+            all_tasks.append(self._test_tts(tts_name, config))
+
+        print(
+            f"\n✅ 找到 {len([t for t in all_tasks if 'test_single_sentence' in str(t)]) / len(self.test_sentences):.0f} 个可用LLM模块")
+        print(f"✅ 找到 {len([t for t in all_tasks if '_test_tts' in str(t)])} 个可用TTS模块")
+        print("\n⏳ 开始并发测试所有模块...\n")
+
+        # 并发执行所有测试任务
+        all_results = await asyncio.gather(*all_tasks, return_exceptions=True)
+
+        # 处理LLM结果
+        llm_results = {}
+        for result in [r for r in all_results if r and isinstance(r, dict) and r.get("type") == "llm"]:
+            llm_name = result["name"]
+            if llm_name not in llm_results:
+                llm_results[llm_name] = {
+                    "name": llm_name,
+                    "type": "llm",
+                    "first_token_times": [],
+                    "response_times": [],
+                    "errors": 0
+                }
+            llm_results[llm_name]["first_token_times"].append(result["first_token_time"])
+            llm_results[llm_name]["response_times"].append(result["response_time"])
+
+        # 计算LLM平均值和标准差
+        for llm_name, data in llm_results.items():
+            if len(data["first_token_times"]) >= len(self.test_sentences) * 0.5:
+                self.results["llm"][llm_name] = {
+                    "name": llm_name,
+                    "type": "llm",
+                    "avg_response": sum(data["response_times"]) / len(data["response_times"]),
+                    "avg_first_token": sum(data["first_token_times"]) / len(data["first_token_times"]),
+                    "std_first_token": statistics.stdev(data["first_token_times"]) if len(
+                        data["first_token_times"]) > 1 else 0,
+                    "std_response": statistics.stdev(data["response_times"]) if len(data["response_times"]) > 1 else 0,
+                    "errors": 0
+                }
+
+        # 处理TTS结果
+        for result in [r for r in all_results if r and isinstance(r, dict) and r.get("type") == "tts"]:
+            if result["errors"] == 0:
+                self.results["tts"][result["name"]] = result
+
+        # 生成组合建议并打印结果
+        print("\n📊 生成测试报告...")
+        self._generate_combinations()
+        self._print_results()
+
+
+async def main():
+    tester = AsyncPerformanceTester()
+    await tester.run()
+
 
 if __name__ == "__main__":
-    tester = PerformanceTester()
-    tester.run()
\ No newline at end of file
+    asyncio.run(main())
\ No newline at end of file