update readme

Author: LuckyYC

README.md

@@ -1,5 +1,12 @@
 <div align="center">
   <img src="docs/images/logo.png" alt="LMeterX Logo" width="400"/>
+  <p>
+    <a href="https://github.com/MigoXLab/LMeterX/blob/main/LICENSE"><img src="https://img.shields.io/github/license/MigoXLab/LMeterX" alt="License"></a>
+    <a href="https://github.com/MigoXLab/LMeterX/stargazers"><img src="https://img.shields.io/github/stars/MigoXLab/LMeterX" alt="GitHub stars"></a>
+    <a href="https://github.com/MigoXLab/LMeterX/network/members"><img src="https://img.shields.io/github/forks/MigoXLab/LMeterX" alt="GitHub forks"></a>
+    <a href="https://github.com/MigoXLab/LMeterX/issues"><img src="https://img.shields.io/github/issues/MigoXLab/LMeterX" alt="GitHub issues"></a>
+    <a href="https://deepwiki.com/MigoXLab/LMeterX"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a>
+  </p>
   <p>
     <a href="README_CN.md">简体中文</a> |
     <strong>English</strong>
@@ -10,14 +17,15 @@
 
 ## 📋 Project Overview
 
-LMeterX is a professional large language model performance testing platform that supports comprehensive load testing for any LLM service. Through an intuitive Web interface, users can easily create and manage test tasks, monitor testing processes in real-time, and obtain detailed performance analysis reports, providing reliable data support for model deployment and performance optimization.
+LMeterX is a professional large language model performance testing platform that can be applied to model inference services based on large model inference frameworks (such as LiteLLM, vLLM, TensorRT-LLM, LMDeploy, and others), and also supports performance testing for cloud services like Azure OpenAI, AWS Bedrock, Google Vertex AI, and other major cloud providers. Through an intuitive Web interface, users can easily create and manage test tasks, monitor testing processes in real-time, and obtain detailed performance analysis reports, providing reliable data support for model deployment and performance optimization.
 
 <div align="center">
   <img src="docs/images/images.gif" alt="LMeterX Demo" width="800"/>
 </div>
 
 ## ✨ Core Features
 
+- **Universal Framework Support** - Compatible with mainstream inference frameworks (vLLM, LiteLLM, TensorRT-LLM) and cloud services (Azure, AWS, Google Cloud)
 - **Full Model Compatibility** - Supports mainstream LLMs like GPT, Claude, and Llama with one-click stress testing
 - **High-Load Stress Testing** - Simulates high-concurrency requests to accurately detect model performance limits
 - **Multi-Scenario Coverage** - Supports streaming/non-streaming, supports text/multimodal/custom datasets<sup>![NEW](https://img.shields.io/badge/NEW-00C851?style=flat&labelColor=transparent)</sup>
@@ -59,38 +67,52 @@ curl -fsSL https://raw.githubusercontent.com/MigoXLab/LMeterX/main/quick-start.s
 ```
 ### Usage Guide
 
-1. **Access Web Interface**: http://localhost:8080
-2. **Create Test Task**:
-   - Configure target API address and model parameters
-   - Select test type (text conversation/image-text conversation)
-   - Set concurrent user count and test duration
-   - Configure other advanced parameters (optional)
-3. **Monitor Test Process**: Real-time view of test logs and performance metrics
-4. **View and Export Test Results**: View detailed performance results and export reports.
-5. **AI Summary**: After configuring the AI service on the System Configuration page, you can perform AI-powered evaluation and summary of performance results on the Task Results page.
+1. **Access Web Interface**: Open http://localhost:8080
+2. **Create Test Task**: Navigate to Test Tasks → Create Task, configure LLM API request information, test data, and request-response field mapping
+   **2.1 Basic Information**: For `/chat/completions` API, you only need to configure API path, model, and response mode. You can also supplement the complete payload in request parameters.
+   **2.2 Data Payload**: Select built-in text datasets/multimodal datasets as needed, or upload custom JSONL data files.
+   **2.3 Field Mapping**: Configure the prompt field path in payload, and response data paths for model output content, reasoning_content fields, usage fields, etc. This field mapping is crucial for updating request parameters with datasets and correctly parsing streaming/non-streaming responses.
+3. **API Testing**: In Test Tasks → Create Task, click the "Test" button in the Basic Information panel to quickly test API connectivity
+   **Note**: For quick API response, it's recommended to use simple prompts when testing API connectivity.
+4. **Real-time Monitoring**: Navigate to Test Tasks → Logs/Monitoring Center to view full-chain test logs and troubleshoot exceptions
+5. **Result Analysis**: Navigate to Test Tasks → Results to view detailed performance results and export reports
+6. **Result Comparison**: Navigate to Model Arena to select multiple models or versions for multi-dimensional performance comparison
+7. **AI Analysis**: In Test Tasks → Results/Model Arena, after configuring AI analysis service, support intelligent performance evaluation for single/multiple tasks
 
 ## 🔧 Configuration
 
 ### Environment Variable Configuration
 
 #### General Configuration
 ```bash
-SECRET_KEY=your_secret_key_here        # Application security key
-FLASK_DEBUG=false                      # Debug mode switch
-```
+# ================= Database Configuration =================
+DB_HOST=mysql           # Database host (container name or IP)
+DB_PORT=3306            # Database port
+DB_USER=lmeterx         # Database username
+DB_PASSWORD=lmeterx_password  # Database password (use secrets management in production)
+DB_NAME=lmeterx         # Database name
+
+# ================= Frontend Configuration =================
+VITE_API_BASE_URL=/api  # Base API URL for frontend requests (supports reverse proxy)
+
+# ================= High-Concurrency Load Testing Deployment Requirements =================
+# When concurrent users exceed this threshold, the system will automatically enable multi-process mode (requires multi-core CPU support)
+MULTIPROCESS_THRESHOLD=1000
+
+# Minimum number of concurrent users each child process should handle (prevents excessive processes and resource waste)
+MIN_USERS_PER_PROCESS=600
+
+# ⚠️ IMPORTANT NOTES:
+#   - When concurrency ≥ 1000, enabling multi-process mode is strongly recommended for performance.
+#   - Multi-process mode requires multi-core CPU resources — ensure your deployment environment meets these requirements.
+
+# ================= Deployment Resource Limits =================
+deploy:
+  resources:
+    limits:
+      cpus: '2.0'       # Recommended minimum: 2 CPU cores (4+ cores recommended for high-concurrency scenarios)
+      memory: 2G        # Memory limit — adjust based on actual load (minimum recommended: 2G)
 
-#### Database Configuration
-```bash
-DB_HOST=mysql                          # Database host address
-DB_PORT=3306                           # Database port
-DB_USER=lmeterx                        # Database username
-DB_PASSWORD=lmeterx_password           # Database password
-DB_NAME=lmeterx                        # Database name
-```
-
-#### Frontend Configuration
-```bash
-VITE_API_BASE_URL=/api                # API base path
 ```
 
 ## 🤝 Development Guide
@@ -135,6 +157,7 @@ LMeterX/
 
 ### Planned
 - [ ] CLI command-line tool
+- [ ] Support for `/v1/embedding` and `/v1/rerank` API stress testing
 
 ## 📚 Related Documentation
 

README_CN.md

@@ -1,5 +1,12 @@
 <div align="center">
   <img src="docs/images/logo.png" alt="LMeterX Logo" width="400"/>
+  <p>
+    <a href="https://github.com/MigoXLab/LMeterX/blob/main/LICENSE"><img src="https://img.shields.io/github/license/MigoXLab/LMeterX" alt="License"></a>
+    <a href="https://github.com/MigoXLab/LMeterX/stargazers"><img src="https://img.shields.io/github/stars/MigoXLab/LMeterX" alt="GitHub stars"></a>
+  <a href="https://github.com/MigoXLab/LMeterX/network/members"><img src="https://img.shields.io/github/forks/MigoXLab/LMeterX" alt="GitHub forks"></a>
+  <a href="https://github.com/MigoXLab/LMeterX/issues"><img src="https://img.shields.io/github/issues/MigoXLab/LMeterX" alt="GitHub issues"></a>
+    <a href="https://deepwiki.com/MigoXLab/LMeterX"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a>
+  </p>
   <p>
     <strong>简体中文</strong> |
     <a href="README.md">English</a>
@@ -19,6 +26,7 @@ LMeterX 是一个专业的大语言模型性能测试平台，支持基于大模
 
 ## ✨ 核心特性
 
+- **通用框架支持** - 兼容主流推理框架（vLLM、LiteLLM、TensorRT-LLM）和云服务（Azure、AWS、Google Cloud）
 - **全模型兼容** - 支持 GPT、Claude、Llama 等主流大模型，一键发起压测
 - **高负载压测** - 模拟高并发请求，精准探测模型性能极限
 - **多场景覆盖** - 支持流式/非流式、文本/多模态/自定义数据集<sup>![NEW](https://img.shields.io/badge/NEW-00C851?style=flat&labelColor=transparent)</sup>
@@ -61,30 +69,46 @@ curl -fsSL https://raw.githubusercontent.com/MigoXLab/LMeterX/main/quick-start.s
 
 ### 使用指南
 
-1. **访问界面** - 打开 http://localhost:8080
-2. **创建任务** - 配置API地址、模型参数、测试类型
-3. **实时监控** - 查看测试日志和性能指标
-4. **结果分析** - 查看详细性能结果，导出报告
-5. **AI分析** - 配置AI服务后，获得智能性能评估
+1. **访问界面**: 打开 http://localhost:8080
+2. **创建任务**: 导航至 测试任务 → 创建任务，配置LLM API请求信息、测试数据以及请求响应字段映射
+   **2.1 基础信息**: 对于 `/chat/completions` API只需要配置API路径、模型以及响应模式即可，也支持在请求参数中补充完整payload
+   **2.2 数据负载**: 根据需要选择内置的纯文本数据集/多模态数据集，也支持自定义JSONL数据或文件等
+   **2.3 字段映射**: 配置payload中prompt对应字段路径，以及响应数据中对应模型输出的content、reasoning_content字段路径、usage字段路径等，此字段映射对于使用数据集更新请求参数和正确解析流式/非流式响应至关重要，请注意仔细填写
+1. **API测试**: 在 测试任务 → 创建任务，点击基础信息面板的"测试"按钮，快速测试API连通性
+   **注意**: 为快速得到API响应，建议使用简单的prompt测试API连通性
+1. **实时监控**: 导航至 测试任务 → 日志/监控中心，查看全链路测试日志，排查异常
+2. **结果分析**: 导航至 测试任务 → 结果，查看详细性能结果，导出报告
+3. **结果对比**: 导航至 模型擂台，选择多个模型或版本进行多维度性能对比
+4. **AI分析**: 在 测试任务 → 结果/模型擂台，配置AI分析服务后，支持对单个/多个任务进行智能性能评估
 
 ## 🔧 配置说明
 
 ### 环境变量
 
 ```bash
-# 通用配置
-SECRET_KEY=your_secret_key_here
-FLASK_DEBUG=false
-
-# 数据库配置
+===  数据库配置 ===
 DB_HOST=mysql
 DB_PORT=3306
 DB_USER=lmeterx
 DB_PASSWORD=lmeterx_password
 DB_NAME=lmeterx
 
-# 前端配置
+===  前端配置 ===
 VITE_API_BASE_URL=/api
+
+=== 高并发压测 部署要求 ===
+# 当并发用户数超过此阈值，系统将自动启用多进程模式（需多核 CPU 支持）
+MULTIPROCESS_THRESHOLD: 1000
+# 每个子进程至少承载的并发用户数（避免进程过多导致资源浪费）
+MIN_USERS_PER_PROCESS: 600
+# ⚠️ 重要提示：
+#   - 当并发量 ≥ 1000 时，强烈建议启用多进程以提升性能。
+#   - 多进程模式依赖多核 CPU 资源，请确保部署环境满足资源要求
+deploy:
+  resources:
+    limits:
+      cpus: '2.0'    # 建议至少分配 2 核 CPU（高并发场景建议 4 核或以上）                     #
+      memory: 2G     # 内存限制，可根据实际负载调整（推荐 ≥ 2G）
 ```
 
 ## 🤝 开发指南
@@ -124,6 +148,7 @@ LMeterX/
 
 ### 规划中
 - [ ] CLI 命令行工具
+- [ ] 支持 `/v1/embedding` 和 `/v1/rerank` 接口压测
 
 ## 📚 相关文档
 

docker-compose.dev.yml

@@ -68,7 +68,7 @@ services:
       ENABLE_MULTIPROCESS: auto
       LOCUST_CPU_CORES: 4.0
       MULTIPROCESS_THRESHOLD: 1000           # Enable multiprocess only for 1000+ users
-      MIN_USERS_PER_PROCESS: 600             # Each process handles at least 500 users
+      MIN_USERS_PER_PROCESS: 600             # Each process handles at least 600 users
       # Process management and stability
       PYTHONUNBUFFERED: 1                    # Ensure immediate log output
       LOCUST_WORKER_HEARTBEAT_INTERVAL: 3    # Worker heartbeat interval (seconds)

docker-compose.yml

@@ -62,7 +62,7 @@ services:
       ENABLE_MULTIPROCESS: auto
       LOCUST_CPU_CORES: 4.0
       MULTIPROCESS_THRESHOLD: 1000           # Enable multiprocess only for 1000+ users
-      MIN_USERS_PER_PROCESS: 600             # Each process handles at least 500 users
+      MIN_USERS_PER_PROCESS: 600             # Each process handles at least 600 users
       # Process management and stability
       PYTHONUNBUFFERED: 1                    # Ensure immediate log output
       LOCUST_WORKER_HEARTBEAT_INTERVAL: 3    # Worker heartbeat interval (seconds)
@@ -80,7 +80,7 @@ services:
     deploy:
       resources:
         limits:
-          cpus: '2.0'
+          cpus: '2.0'                        # Multi-process mode requires at least 2 cores
           memory: 2G
         reservations:
           cpus: '1.0'