mirror of
https://github.com/xinnan-tech/xiaozhi-esp32-server.git
synced 2026-07-22 07:03:53 +08:00
欣南科技
@@ -215,13 +215,15 @@ Websocket接口地址: wss://2662r3426b.vicp.fun/xiaozhi/v1/
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| 模块名称 | 入门全免费设置 | 流式配置 |
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|:---:|:---:|:---:|
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| ASR(语音识别) | FunASR(本地) | 👍FunASRServer 或 👍DoubaoStreamASR |
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| LLM(大模型) | ChatGLMLLM(智谱glm-4-flash) | 👍DoubaoLLM(doubao-1-5-pro-32k-250115) 或 AliLLM(qwen3-235b-a22b-instruct-2507) |
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| ASR(语音识别) | FunASR(本地) | 👍FunASR(本地GPU模式) |
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| LLM(大模型) | ChatGLMLLM(智谱glm-4-flash) | 👍AliLLM(qwen3-235b-a22b-instruct-2507) 或 👍DoubaoLLM(doubao-1-5-pro-32k-250115) |
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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) | ✅LinkeraiTTS(灵犀流式) | 👍HuoshanDoubleStreamTTS(火山双流式语音合成) 或 👍AliyunStreamTTS(阿里云流式语音合成) |
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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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如果您关心各组件的耗时，请查阅[小智各组件性能测试报告](https://github.com/xinnan-tech/xiaozhi-performance-research)，可按报告中的测试方法在您的环境中实际测试。
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#### 🔧 测试工具
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本项目提供以下测试工具，帮助您验证系统和选择合适的模型：
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+4
-2
@@ -38,13 +38,15 @@ conda install conda-forge::ffmpeg
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| 模块名称 | 入门全免费设置 | 流式配置 |
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|:---:|:---:|:---:|
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| ASR(语音识别) | FunASR(本地) | 👍FunASRServer 或 👍DoubaoStreamASR |
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| LLM(大模型) | ChatGLMLLM(智谱glm-4-flash) | 👍DoubaoLLM(doubao-1-5-pro-32k-250115) 或 AliLLM(qwen3-235b-a22b-instruct-2507) |
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| ASR(语音识别) | FunASR(本地) | 👍FunASR(本地GPU模式) |
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| LLM(大模型) | ChatGLMLLM(智谱glm-4-flash) | 👍AliLLM(qwen3-235b-a22b-instruct-2507) 或 👍DoubaoLLM(doubao-1-5-pro-32k-250115) |
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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) | ✅LinkeraiTTS(灵犀流式) | 👍HuoshanDoubleStreamTTS(火山双流式语音合成) 或 👍AliyunStreamTTS(阿里云流式语音合成) |
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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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如果您关心各组件的耗时，请查阅[小智各组件性能测试报告](https://github.com/xinnan-tech/xiaozhi-performance-research)，可按报告中的测试方法在您的环境中实际测试。
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### 6、我说话很慢，停顿时小智老是抢话 🗣️
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建议：在配置文件中找到如下部分，将 `min_silence_duration_ms` 的值调大（例如改为 `1000`）：
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@@ -85,7 +85,13 @@ class LLMProvider(LLMProviderBase):
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log.bind(tag=TAG).info(
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f"Gemini 代理设置成功 - HTTP: {http_proxy}, HTTPS: {https_proxy}"
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)
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# 配置API密钥
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genai.configure(api_key=self.api_key)
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# 设置请求超时（秒）
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self.timeout = cfg.get("timeout", 120) # 默认120秒
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# 创建模型实例
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self.model = genai.GenerativeModel(self.model_name)
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self.gen_cfg = GenerationConfig(
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@@ -164,6 +170,7 @@ class LLMProvider(LLMProviderBase):
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generation_config=self.gen_cfg,
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tools=tools,
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stream=True,
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timeout=self.timeout,
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)
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try:
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@@ -3,6 +3,7 @@ import logging
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import os
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import statistics
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import time
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import concurrent.futures
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from typing import Dict, Optional
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import yaml
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import aiohttp
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@@ -19,16 +20,104 @@ description = "大语言模型性能测试"
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class LLMPerformanceTester:
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def __init__(self):
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self.config = load_config()
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# 使用更符合智能体场景的测试内容，包含系统提示词
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self.system_prompt = self._load_system_prompt()
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self.test_sentences = self.config.get("module_test", {}).get(
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"test_sentences",
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[
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"你好，请介绍一下你自己",
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"What's the weather like today?",
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"请用100字概括量子计算的基本原理和应用前景",
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"你好，我今天心情不太好，能安慰一下我吗？",
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"帮我查一下明天的天气如何？",
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"我想听一个有趣的故事，你能给我讲一个吗？",
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"现在几点了？今天是星期几？",
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"我想设置一个明天早上8点的闹钟提醒我开会",
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],
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)
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self.results = {}
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def _load_system_prompt(self) -> str:
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"""加载系统提示词"""
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try:
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prompt_file = os.path.join(
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os.path.dirname(os.path.dirname(__file__)), "agent-base-prompt.txt"
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)
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with open(prompt_file, "r", encoding="utf-8") as f:
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content = f.read()
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# 替换模板变量为测试值
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content = content.replace(
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"{{base_prompt}}", "你是小智，一个聪明可爱的AI助手"
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)
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content = content.replace(
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"{{emojiList}}", "😀,😃,😄,😁,😊,😍,🤔,😮,😱,😢,😭,😴,😵,🤗,🙄"
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)
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content = content.replace("{{current_time}}", "2024年8月17日 12:30:45")
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content = content.replace("{{today_date}}", "2024年8月17日")
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content = content.replace("{{today_weekday}}", "星期六")
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content = content.replace("{{lunar_date}}", "甲辰年七月十四")
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content = content.replace("{{local_address}}", "北京市")
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content = content.replace("{{weather_info}}", "今天晴，25-32℃")
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return content
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except Exception as e:
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print(f"无法加载系统提示词文件: {e}")
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return "你是小智，一个聪明可爱的AI助手。请用温暖友善的语气回复用户。"
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def _collect_response_sync(self, llm, messages, llm_name, sentence_start):
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"""同步收集响应数据的辅助方法"""
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chunks = []
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first_token_received = False
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first_token_time = None
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try:
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response_generator = llm.response("perf_test", messages)
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chunk_count = 0
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for chunk in response_generator:
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chunk_count += 1
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# 每处理一定数量的chunk就检查一下是否应该中断
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if chunk_count % 10 == 0:
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# 通过检查当前线程是否被标记为中断来提前退出
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import threading
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if (
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threading.current_thread().ident
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!= threading.main_thread().ident
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):
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# 如果不是主线程，检查是否应该停止
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pass
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# 检查chunk是否包含错误信息
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chunk_str = str(chunk)
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if (
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"异常" in chunk_str
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or "错误" in chunk_str
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or "502" in chunk_str.lower()
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):
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error_msg = chunk_str.lower()
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print(f"{llm_name} 响应包含错误信息: {error_msg}")
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# 抛出一个包含错误信息的异常
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raise Exception(chunk_str)
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if not first_token_received and chunk.strip() != "":
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first_token_time = time.time() - sentence_start
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first_token_received = True
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print(f"{llm_name} 首个 Token: {first_token_time:.3f}s")
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chunks.append(chunk)
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except Exception as e:
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# 更详细的错误信息
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error_msg = str(e).lower()
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print(f"{llm_name} 响应收集异常: {error_msg}")
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# 对于502错误或网络错误，直接抛出异常让上层处理
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if (
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"502" in error_msg
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or "bad gateway" in error_msg
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or "error code: 502" in error_msg
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or "异常" in str(e)
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or "错误" in str(e)
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):
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raise e
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# 对于其他错误，可以返回部分结果
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return chunks, first_token_time
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return chunks, first_token_time
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async def _check_ollama_service(self, base_url: str, model_name: str) -> bool:
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"""异步检查 Ollama 服务状态"""
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async with aiohttp.ClientSession() as session:
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@@ -64,20 +153,51 @@ class LLMPerformanceTester:
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first_token_received = False
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first_token_time = None
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async def process_response():
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nonlocal first_token_received, first_token_time
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for chunk in llm.response(
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"perf_test", [{"role": "user", "content": sentence}]
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):
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if not first_token_received and chunk.strip() != "":
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first_token_time = time.time() - sentence_start
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first_token_received = True
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print(f"{llm_name} 首个 Token: {first_token_time:.3f}s")
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yield chunk
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# 构建包含系统提示词的消息
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messages = [
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{"role": "system", "content": self.system_prompt},
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{"role": "user", "content": sentence},
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]
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response_chunks = []
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async for chunk in process_response():
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response_chunks.append(chunk)
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# 使用asyncio.wait_for进行超时控制
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try:
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loop = asyncio.get_event_loop()
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with concurrent.futures.ThreadPoolExecutor() as executor:
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# 创建响应收集任务
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future = executor.submit(
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self._collect_response_sync,
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llm,
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messages,
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llm_name,
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sentence_start,
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)
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# 使用asyncio.wait_for实现超时控制
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try:
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response_chunks, first_token_time = await asyncio.wait_for(
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asyncio.wrap_future(future), timeout=10.0
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)
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except asyncio.TimeoutError:
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print(f"{llm_name} 测试超时（10秒），跳过")
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# 强制取消future
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future.cancel()
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# 等待一小段时间确保线程池任务能够响应取消
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try:
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await asyncio.wait_for(
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asyncio.wrap_future(future), timeout=1.0
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)
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except (
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asyncio.TimeoutError,
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concurrent.futures.CancelledError,
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Exception,
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):
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# 忽略所有异常，确保程序继续执行
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pass
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return None
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except Exception as timeout_error:
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print(f"{llm_name} 处理异常: {timeout_error}")
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return None
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response_time = time.time() - sentence_start
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print(f"{llm_name} 完成响应: {response_time:.3f}s")
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@@ -89,6 +209,20 @@ class LLMPerformanceTester:
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"response_time": response_time,
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}
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except Exception as e:
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error_msg = str(e).lower()
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# 检查是否为502错误或网络错误
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if (
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"502" in error_msg
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or "bad gateway" in error_msg
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or "error code: 502" in error_msg
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):
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print(f"{llm_name} 遇到502错误，跳过测试")
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return {
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"name": llm_name,
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"type": "llm",
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"errors": 1,
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"error_type": "502网络错误",
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}
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print(f"{llm_name} 句子测试失败: {str(e)}")
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return None
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@@ -101,16 +235,31 @@ class LLMPerformanceTester:
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model_name = config.get("model_name")
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if not model_name:
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print("Ollama 未配置 model_name")
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return {"name": llm_name, "type": "llm", "errors": 1}
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return {
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"name": llm_name,
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"type": "llm",
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"errors": 1,
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"error_type": "网络错误",
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}
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if not await self._check_ollama_service(base_url, model_name):
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return {"name": llm_name, "type": "llm", "errors": 1}
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return {
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"name": llm_name,
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"type": "llm",
|
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"errors": 1,
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"error_type": "网络错误",
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}
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else:
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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"跳过未配置的 LLM: {llm_name}")
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return {"name": llm_name, "type": "llm", "errors": 1}
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return {
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"name": llm_name,
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"type": "llm",
|
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"errors": 1,
|
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"error_type": "配置错误",
|
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}
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# 获取实际类型（兼容旧配置）
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module_type = config.get("type", llm_name)
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@@ -128,64 +277,178 @@ class LLMPerformanceTester:
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self._test_single_sentence(llm_name, llm, sentence)
|
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)
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|
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# 并发执行所有句子测试
|
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sentence_results = await asyncio.gather(*sentence_tasks)
|
||||
# 并发执行所有句子测试，并处理可能的异常
|
||||
sentence_results = await asyncio.gather(
|
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*sentence_tasks, return_exceptions=True
|
||||
)
|
||||
|
||||
# 处理结果，过滤掉异常和None值
|
||||
valid_results = []
|
||||
for result in sentence_results:
|
||||
if isinstance(result, dict) and result is not None:
|
||||
valid_results.append(result)
|
||||
elif isinstance(result, Exception):
|
||||
error_msg = str(result).lower()
|
||||
if "502" in error_msg or "bad gateway" in error_msg:
|
||||
print(f"{llm_name} 遇到502错误，跳过该句子测试")
|
||||
return {
|
||||
"name": llm_name,
|
||||
"type": "llm",
|
||||
"errors": 1,
|
||||
"error_type": "502网络错误",
|
||||
}
|
||||
else:
|
||||
print(f"{llm_name} 句子测试异常: {result}")
|
||||
|
||||
# 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 {"name": llm_name, "type": "llm", "errors": 1}
|
||||
print(f"{llm_name} 无有效数据，可能遇到网络问题或配置错误")
|
||||
return {
|
||||
"name": llm_name,
|
||||
"type": "llm",
|
||||
"errors": 1,
|
||||
"error_type": "网络错误",
|
||||
}
|
||||
|
||||
first_token_times = [r["first_token_time"] for r in valid_results]
|
||||
# 检查有效结果数量，如果太少则认为测试失败
|
||||
if len(valid_results) < len(test_sentences) * 0.3: # 至少要有30%的成功率
|
||||
print(
|
||||
f"{llm_name} 成功测试句子过少({len(valid_results)}/{len(test_sentences)})，可能网络不稳定或接口有问题"
|
||||
)
|
||||
return {
|
||||
"name": llm_name,
|
||||
"type": "llm",
|
||||
"errors": 1,
|
||||
"error_type": "网络错误",
|
||||
}
|
||||
|
||||
first_token_times = [
|
||||
r["first_token_time"]
|
||||
for r in valid_results
|
||||
if r.get("first_token_time")
|
||||
]
|
||||
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 {"name": llm_name, "type": "llm", "errors": 1}
|
||||
# 过滤异常数据（超出3个标准差的数据）
|
||||
if len(response_times) > 1:
|
||||
mean = statistics.mean(response_times)
|
||||
stdev = statistics.stdev(response_times)
|
||||
filtered_times = [t for t in response_times if t <= mean + 3 * stdev]
|
||||
else:
|
||||
filtered_times = response_times
|
||||
|
||||
return {
|
||||
"name": llm_name,
|
||||
"type": "llm",
|
||||
"avg_response": sum(response_times) / len(response_times),
|
||||
"avg_first_token": sum(first_token_times) / len(first_token_times),
|
||||
"avg_first_token": (
|
||||
sum(first_token_times) / len(first_token_times)
|
||||
if first_token_times
|
||||
else 0
|
||||
),
|
||||
"success_rate": f"{len(valid_results)}/{len(test_sentences)}",
|
||||
"errors": 0,
|
||||
}
|
||||
except Exception as e:
|
||||
print(f"LLM {llm_name} 测试失败: {str(e)}")
|
||||
return {"name": llm_name, "type": "llm", "errors": 1}
|
||||
error_msg = str(e).lower()
|
||||
if "502" in error_msg or "bad gateway" in error_msg:
|
||||
print(f"LLM {llm_name} 遇到502错误，跳过测试")
|
||||
else:
|
||||
print(f"LLM {llm_name} 测试失败: {str(e)}")
|
||||
error_type = "网络错误"
|
||||
if "timeout" in str(e).lower():
|
||||
error_type = "超时连接"
|
||||
return {
|
||||
"name": llm_name,
|
||||
"type": "llm",
|
||||
"errors": 1,
|
||||
"error_type": error_type,
|
||||
}
|
||||
|
||||
def _print_results(self):
|
||||
"""打印测试结果"""
|
||||
llm_table = []
|
||||
print("\n" + "=" * 50)
|
||||
print("LLM 性能测试结果")
|
||||
print("=" * 50)
|
||||
|
||||
if not self.results:
|
||||
print("没有可用的测试结果")
|
||||
return
|
||||
|
||||
headers = ["模型名称", "平均响应时间(s)", "首Token时间(s)", "成功率", "状态"]
|
||||
table_data = []
|
||||
|
||||
# 收集所有数据并分类
|
||||
valid_results = []
|
||||
error_results = []
|
||||
|
||||
for name, data in self.results.items():
|
||||
if data["errors"] == 0:
|
||||
llm_table.append(
|
||||
[
|
||||
name,
|
||||
f"{data['avg_first_token']:.3f}秒",
|
||||
f"{data['avg_response']:.3f}秒",
|
||||
]
|
||||
# 正常结果
|
||||
avg_response = f"{data['avg_response']:.3f}"
|
||||
avg_first_token = (
|
||||
f"{data['avg_first_token']:.3f}"
|
||||
if data["avg_first_token"] > 0
|
||||
else "-"
|
||||
)
|
||||
success_rate = data.get("success_rate", "N/A")
|
||||
status = "✅ 正常"
|
||||
|
||||
# 保存用于排序的值
|
||||
first_token_value = (
|
||||
data["avg_first_token"]
|
||||
if data["avg_first_token"] > 0
|
||||
else float("inf")
|
||||
)
|
||||
|
||||
if llm_table:
|
||||
print("\nLLM 性能排行:\n")
|
||||
print(
|
||||
tabulate(
|
||||
llm_table,
|
||||
headers=["模型名称", "首字耗时", "总耗时"],
|
||||
tablefmt="github",
|
||||
colalign=("left", "right", "right"),
|
||||
disable_numparse=True,
|
||||
valid_results.append(
|
||||
{
|
||||
"name": name,
|
||||
"avg_response": avg_response,
|
||||
"avg_first_token": avg_first_token,
|
||||
"success_rate": success_rate,
|
||||
"status": status,
|
||||
"sort_key": first_token_value,
|
||||
}
|
||||
)
|
||||
else:
|
||||
# 错误结果
|
||||
avg_response = "-"
|
||||
avg_first_token = "-"
|
||||
success_rate = "0/5"
|
||||
|
||||
# 获取具体错误类型
|
||||
error_type = data.get("error_type", "网络错误")
|
||||
status = f"❌ {error_type}"
|
||||
|
||||
error_results.append(
|
||||
[name, avg_response, avg_first_token, success_rate, status]
|
||||
)
|
||||
|
||||
# 按首Token时间升序排序
|
||||
valid_results.sort(key=lambda x: x["sort_key"])
|
||||
|
||||
# 将排序后的有效结果转换为表格数据
|
||||
for result in valid_results:
|
||||
table_data.append(
|
||||
[
|
||||
result["name"],
|
||||
result["avg_response"],
|
||||
result["avg_first_token"],
|
||||
result["success_rate"],
|
||||
result["status"],
|
||||
]
|
||||
)
|
||||
else:
|
||||
print("\n没有可用的 LLM 模块进行测试。")
|
||||
|
||||
# 将错误结果添加到表格数据末尾
|
||||
table_data.extend(error_results)
|
||||
|
||||
print(tabulate(table_data, headers=headers, tablefmt="grid"))
|
||||
print("\n测试说明:")
|
||||
print("- 测试内容：包含完整系统提示词的智能体对话场景")
|
||||
print("- 超时控制：单个请求最大等待时间为10秒")
|
||||
print("- 错误处理：自动跳过502错误和网络异常的模型")
|
||||
print("- 成功率：成功响应的句子数量/总测试句子数量")
|
||||
print("\n测试完成！")
|
||||
|
||||
async def run(self):
|
||||
"""执行全量异步测试"""
|
||||
@@ -227,13 +490,45 @@ class LLMPerformanceTester:
|
||||
print(f"\n找到 {len(all_tasks)} 个可用 LLM 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，但为每个任务设置独立超时
|
||||
async def test_with_timeout(task, timeout=30):
|
||||
"""为每个测试任务添加超时保护"""
|
||||
try:
|
||||
return await asyncio.wait_for(task, timeout=timeout)
|
||||
except asyncio.TimeoutError:
|
||||
print(f"测试任务超时（{timeout}秒），跳过")
|
||||
return {
|
||||
"name": "Unknown",
|
||||
"type": "llm",
|
||||
"errors": 1,
|
||||
"error_type": "超时连接",
|
||||
}
|
||||
except Exception as e:
|
||||
print(f"测试任务异常: {str(e)}")
|
||||
return {
|
||||
"name": "Unknown",
|
||||
"type": "llm",
|
||||
"errors": 1,
|
||||
"error_type": "网络错误",
|
||||
}
|
||||
|
||||
# 为每个任务包装超时保护
|
||||
protected_tasks = [test_with_timeout(task) for task in all_tasks]
|
||||
|
||||
# 并发执行所有测试任务
|
||||
all_results = await asyncio.gather(*all_tasks, return_exceptions=True)
|
||||
all_results = await asyncio.gather(*protected_tasks, return_exceptions=True)
|
||||
|
||||
# 处理结果
|
||||
for result in all_results:
|
||||
if isinstance(result, dict) and result.get("errors") == 0:
|
||||
self.results[result["name"]] = result
|
||||
if isinstance(result, dict):
|
||||
if result.get("errors") == 0:
|
||||
self.results[result["name"]] = result
|
||||
else:
|
||||
# 即使有错误也记录，用于显示失败状态
|
||||
if result.get("name") != "Unknown":
|
||||
self.results[result["name"]] = result
|
||||
elif isinstance(result, Exception):
|
||||
print(f"测试结果处理异常: {str(result)}")
|
||||
|
||||
# 打印结果
|
||||
print("\n生成测试报告...")
|
||||
|
||||
Reference in New Issue
Block a user