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https://github.com/xinnan-tech/xiaozhi-esp32-server.git
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add:增加千问收费视觉模型，速度更稳定一点
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hrz
@@ -195,7 +195,7 @@ Websocket接口地址: wss://2662r3426b.vicp.fun/xiaozhi/v1/
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| ASR(语音识别) | FunASR(本地) | ✅DoubaoASR(火山流式语音识别) |
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| LLM(大模型) | ChatGLMLLM(智谱glm-4-flash) | ✅DoubaoLLM(火山doubao-1-5-pro-32k-250115) |
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| VLLM(视觉大模型) | ChatGLMVLLM(智谱glm-4v-flash) | ✅ChatGLMVLLM(智谱glm-4v-flash) |
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| VLLM(视觉大模型) | ChatGLMVLLM(智谱glm-4v-flash) | ✅QwenVLVLLM(千问qwen2.5-vl-3b-instructh) |
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| TTS(语音合成) | EdgeTTS(微软语音) | ✅HuoshanDoubleStreamTTS(火山双流式语音合成) |
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| Intent(意图识别) | function_call(函数调用) | ✅function_call(函数调用) |
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| Memory(记忆功能) | mem_local_short(本地短期记忆） | ✅mem_local_short（本地短期记忆） |
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@@ -0,0 +1,14 @@
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-- VLLM模型配置
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delete from `ai_model_config` where id = 'VLLM_QwenVLVLLM';
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INSERT INTO `ai_model_config` VALUES ('VLLM_QwenVLVLLM', 'VLLM', 'QwenVLVLLM', '千问视觉模型', 0, 1, '{\"type\": \"openai\", \"model_name\": \"qwen2.5-vl-3b-instruct\", \"base_url\": \"https://dashscope.aliyuncs.com/compatible-mode/v1\", \"api_key\": \"你的api_key\"}', NULL, NULL, 2, NULL, NULL, NULL, NULL);
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-- 更新文档
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UPDATE `ai_model_config` SET
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`doc_link` = 'https://bailian.console.aliyun.com/?tab=api#/api/?type=model&url=https%3A%2F%2Fhelp.aliyun.com%2Fdocument_detail%2F2845564.html&renderType=iframe',
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`remark` = '千问视觉模型配置说明：
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1. 访问 https://bailian.console.aliyun.com/?tab=model#/api-key
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2. 注册并获取API密钥
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3. 填入配置文件中' WHERE `id` = 'VLLM_QwenVLVLLM';
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-- 删除参数，这两个参数已挪至python配置文件
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delete from `sys_params` where id in (113,114);
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@@ -183,4 +183,11 @@ databaseChangeLog:
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changes:
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- sqlFile:
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encoding: utf8
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path: classpath:db/changelog/202506031639.sql
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path: classpath:db/changelog/202506031639.sql
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- changeSet:
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id: 202506032232
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author: hrz
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changes:
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- sqlFile:
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encoding: utf8
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path: classpath:db/changelog/202506032232.sql
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@@ -467,6 +467,12 @@ VLLM:
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model_name: glm-4v-flash # 智谱AI的视觉模型
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url: https://open.bigmodel.cn/api/paas/v4/
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api_key: 你的api_key
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QwenVLVLLM:
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type: openai
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model_name: qwen2.5-vl-3b-instruct
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url: https://dashscope.aliyuncs.com/compatible-mode/v1
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# 可在这里找到你的api key https://bailian.console.aliyun.com/?apiKey=1#/api-key
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api_key: 你的api_key
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TTS:
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# 当前支持的type为edge、doubao，可自行适配
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EdgeTTS:
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@@ -46,7 +46,9 @@ class VLLMProvider(VLLMProviderBase):
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{"type": "text", "text": question},
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{
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"type": "image_url",
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"image_url": {"url": f"{base64_image}"},
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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},
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},
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],
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}
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@@ -0,0 +1,189 @@
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import time
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import asyncio
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import logging
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import statistics
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import base64
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from typing import Dict
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from tabulate import tabulate
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from config.settings import load_config
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from core.utils.vllm import create_instance
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# 设置全局日志级别为WARNING，抑制INFO级别日志
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logging.basicConfig(level=logging.WARNING)
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class AsyncVisionPerformanceTester:
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def __init__(self):
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self.config = load_config()
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self.test_images = [
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"../../docs/images/demo1.png",
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"../../docs/images/demo2.png",
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]
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self.test_questions = [
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"这张图片里有什么？",
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"请详细描述这张图片的内容",
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]
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# 加载测试图片
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self.results = {"vllm": {}}
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async def _test_vllm(self, vllm_name: str, config: Dict) -> Dict:
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"""异步测试单个视觉大模型性能"""
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try:
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# 检查API密钥配置
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if "api_key" in config and any(
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x in config["api_key"] for x in ["你的", "placeholder", "sk-xxx"]
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):
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print(f"⏭️ VLLM {vllm_name} 未配置api_key，已跳过")
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return {"name": vllm_name, "type": "vllm", "errors": 1}
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# 获取实际类型（兼容旧配置）
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module_type = config.get("type", vllm_name)
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vllm = create_instance(module_type, config)
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print(f"🖼️ 测试 VLLM: {vllm_name}")
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# 创建所有测试任务
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test_tasks = []
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for question in self.test_questions:
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for image in self.test_images:
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test_tasks.append(
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self._test_single_vision(vllm_name, vllm, question, image)
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)
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# 并发执行所有测试
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test_results = await asyncio.gather(*test_tasks)
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# 处理结果
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valid_results = [r for r in test_results if r is not None]
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if not valid_results:
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print(f"⚠️ {vllm_name} 无有效数据，可能配置错误")
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return {"name": vllm_name, "type": "vllm", "errors": 1}
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response_times = [r["response_time"] for r in valid_results]
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# 过滤异常数据
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mean = statistics.mean(response_times)
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stdev = statistics.stdev(response_times) if len(response_times) > 1 else 0
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filtered_times = [t for t in response_times if t <= mean + 3 * stdev]
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if len(filtered_times) < len(test_tasks) * 0.5:
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print(f"⚠️ {vllm_name} 有效数据不足，可能网络不稳定")
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return {"name": vllm_name, "type": "vllm", "errors": 1}
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return {
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"name": vllm_name,
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"type": "vllm",
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"avg_response": sum(response_times) / len(response_times),
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"std_response": (
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statistics.stdev(response_times) if len(response_times) > 1 else 0
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),
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"errors": 0,
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}
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except Exception as e:
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print(f"⚠️ VLLM {vllm_name} 测试失败: {str(e)}")
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return {"name": vllm_name, "type": "vllm", "errors": 1}
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async def _test_single_vision(
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self, vllm_name: str, vllm, question: str, image: str
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) -> Dict:
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"""测试单个视觉问题的性能"""
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try:
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print(f"📝 {vllm_name} 开始测试: {question[:20]}...")
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start_time = time.time()
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# 读取图片并转换为base64
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with open(image, "rb") as image_file:
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image_data = image_file.read()
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image_base64 = base64.b64encode(image_data).decode("utf-8")
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# 直接获取响应
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response = vllm.response(question, image_base64)
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response_time = time.time() - start_time
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print(f"✓ {vllm_name} 完成响应: {response_time:.3f}s")
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return {
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"name": vllm_name,
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"type": "vllm",
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"response_time": response_time,
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}
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except Exception as e:
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print(f"⚠️ {vllm_name} 测试失败: {str(e)}")
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return None
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def _print_results(self):
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"""打印测试结果"""
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vllm_table = []
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for name, data in self.results["vllm"].items():
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if data["errors"] == 0:
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stability = data["std_response"] / data["avg_response"]
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vllm_table.append(
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[
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name,
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f"{data['avg_response']:.3f}秒",
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f"{stability:.3f}",
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]
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)
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if vllm_table:
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print("\n视觉大模型性能排行:\n")
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print(
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tabulate(
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vllm_table,
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headers=["模型名称", "响应耗时", "稳定性"],
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tablefmt="github",
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colalign=("left", "right", "right"),
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disable_numparse=True,
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)
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)
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else:
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print("\n⚠️ 没有可用的视觉大模型进行测试。")
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async def run(self):
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"""执行全量异步测试"""
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print("🔍 开始筛选可用视觉大模型...")
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if not self.test_images:
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print(f"\n⚠️ {self.image_root} 路径下没有图片文件，无法进行测试")
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return
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# 创建所有测试任务
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all_tasks = []
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# VLLM测试任务
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if self.config.get("VLLM") is not None:
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for vllm_name, config in self.config.get("VLLM", {}).items():
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if "api_key" in config and any(
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x in config["api_key"] for x in ["你的", "placeholder", "sk-xxx"]
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):
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print(f"⏭️ VLLM {vllm_name} 未配置api_key，已跳过")
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continue
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print(f"🖼️ 添加VLLM测试任务: {vllm_name}")
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all_tasks.append(self._test_vllm(vllm_name, config))
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print(f"\n✅ 找到 {len(all_tasks)} 个可用视觉大模型")
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print(f"✅ 使用 {len(self.test_images)} 张测试图片")
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print(f"✅ 使用 {len(self.test_questions)} 个测试问题")
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print("\n⏳ 开始并发测试所有模型...\n")
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# 并发执行所有测试任务
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all_results = await asyncio.gather(*all_tasks, return_exceptions=True)
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# 处理结果
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for result in all_results:
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if isinstance(result, dict) and result["errors"] == 0:
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self.results["vllm"][result["name"]] = result
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# 打印结果
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print("\n📊 生成测试报告...")
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self._print_results()
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async def main():
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tester = AsyncVisionPerformanceTester()
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await tester.run()
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if __name__ == "__main__":
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asyncio.run(main())
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