📝 Update more documentations (phase 1)

Author: Phinease

README.md

@@ -53,103 +53,15 @@ bash deploy.sh
 
 When the containers are running, open **http://localhost:3000** in your browser and follow the setup wizard.
 
-### 3. 🤖 Model Configuration & Provider Recommendations
 
-We recommend the following model providers:
-
-| Model Type | Provider | Notes |
-|------------|----------|-------|
-| LLM & VLLM | [Silicon Flow](https://siliconflow.cn/) | Free tier available |
-| LLM & VLLM | [Alibaba Bailian](https://bailian.console.aliyun.com/) | Free tier available |
-| Embedding | [Jina](https://jina.ai/) | Free tier available |
-| TTS & STT | [Volcengine Voice](https://www.volcengine.com/product/voice-tech) | Free for personal use |
-| Search | [EXA](https://exa.ai/) | Free tier available |
-
-You'll need to input the following information in the model configuration page:
-- Base URL
-- API Key
-- Model Name
-
-The following configurations need to be added to your `.env` file (we'll make these configurable through the frontend soon):
-- TTS and STT related configurations
-- EXA search API Key
-
-> ℹ️ Due to core features development, currently, we only support Jina Embedding model. Support for other models will be added in future releases. For Jina API key setup, please refer to our [FAQ](https://modelengine-group.github.io/nexent/en/faq).
-
-### 4. ❓ Need help?
-
-- Browse the [FAQ](https://modelengine-group.github.io/nexent/en/faq) for common install issues.  
-- Drop questions in our [Discord community](https://discord.gg/tb5H3S3wyv).  
-- File bugs or feature ideas in [GitHub Issues](https://github.com/ModelEngine-Group/nexent/issues).
-
-### 5. 🔧 Hack on Nexent
-
-Want to build from source or add new features? Check the [Contribution Guide](https://modelengine-group.github.io/nexent/en/contributing) for step-by-step instructions.
-
-### 6. 🛠️ Build from Source
-
-Prefer to run Nexent from source code? Follow our [Developer Guide](https://modelengine-group.github.io/nexent/en/getting-started/development-guide) for detailed setup instructions and customization options.
 
 ## 🌱 MCP Tool Ecosystem
 
-Nexent is built on the Model Context Protocol (MCP) tool ecosystem, providing a flexible and extensible framework for integrating various tools and services. MCP serves as the "USB-C of AI" - a universal interface standard that allows AI agents to seamlessly connect with external data sources, tools, and services.
-
-### 🌐 MCP Community Hub
-
-The global MCP ecosystem is thriving with multiple platforms supporting MCP development and deployment:
-
-| Platform | Description | Notes |
-|----------|-------------|-------|
-| **[GitHub MCP Server](https://github.com/github/github-mcp-server)** | Deep integration with Claude, GPT-4, Copilot etc., supports Go and Python | OAuth/GitHub account authorization |
-| **[Qdrant MCP Vector Server](https://github.com/qdrant/mcp-server-qdrant)** | Semantic vector storage with Python/Go compatibility | Compatible with LangChain and other tools |
-| **[Anthropic Reference MCP Servers](https://github.com/modelcontextprotocol/servers)** | Lightweight teaching and prototyping tools, Python | Includes fetch, git and other universal tools |
-| **[AWS Labs MCP Server](https://github.com/awslabs/mcp)** | AWS+Go+CDK cloud reference services | Suitable for cloud environments |
-| **[MCP Hub China](https://www.mcp-cn.com/)** | Chinese curated high-quality MCP service platform | Focuses on quality over quantity, community-driven |
-| **[ModelScope MCP Marketplace](https://modelscope.cn/mcp)** | China's largest MCP community with 1,500+ services | From Amap to Alipay, comprehensive service coverage |
-| **Community MCP Servers** | Various scenario-specific source code collection | Mostly experimental and innovative tools |
-
-### 🛠️ Recommended MCP Tools
-
-| Tool Name | Function | Description |
-|-----------|----------|-------------|
-| **[Amap Maps](https://modelscope.cn/mcp/servers/@amap/amap-maps)** | Geographic services and navigation | Comprehensive mapping, geocoding, routing, and location services |
-| **[Bing Search (Chinese)](https://modelscope.cn/mcp/servers/@yan5236/bing-cn-mcp-server)** | Web search in Chinese | Optimized Chinese web search and information retrieval |
-| **[12306 Train Ticket Query](https://modelscope.cn/mcp/servers/@Joooook/12306-mcp)** | China railway ticket booking | Real-time train schedules, ticket availability, and booking assistance |
-| **[Alipay MCP](https://modelscope.cn/mcp/servers/@alipay/mcp-server-alipay)** | Payment and financial services | Digital payments, financial tools, and services integration |
-| **[Variflight Aviation](https://modelscope.cn/mcp/servers/@variflight-ai/variflight-mcp)** | Flight information and aviation data | Real-time flight tracking, schedules, and aviation analytics |
-| **[Sequential Thinking](https://modelscope.cn/mcp/servers/@modelcontextprotocol/sequentialthinking)** | Structured problem-solving framework | Break down complex problems into manageable, sequential steps |
-| **[ArXiv AI Search](https://modelscope.cn/mcp/servers/@blazickjp/arxiv-mcp-server)** | Academic paper search and research | Advanced search and retrieval of scientific papers and research |
-| **[Firecrawl MCP Server](https://modelscope.cn/mcp/servers/@mendableai/firecrawl-mcp-server)** | Web scraping and content extraction | Intelligent web scraping, data extraction, and content processing |
+Check our [MCP Ecosystem page](https://modelengine-group.github.io/nexent/en/mcp-ecosystem/overview.html) for detailed information about the MCP tool ecosystem, including community hubs, recommended tools, and integration guides.
 
 ### 🚀 Suggested Agent Scenarios
 
-With MCP's powerful ecosystem, you can create sophisticated AI agents for various scenarios:
-
-🌍 **Travel Planning Agent** 
-- Use Amap for route planning and navigation 📍
-- Integrate 12306 for train bookings 🚄
-- Connect Variflight for flight information ✈️
-- Enable Alipay for seamless payments 💳
-
-🔬 **Research Assistant Agent**
-- Leverage ArXiv search for academic papers 📚
-- Use Bing Search for comprehensive web research 🔍
-- Apply Sequential Thinking for structured analysis 🧠
-- Integrate Firecrawl for web data extraction 🕷️
-
-💼 **Business Intelligence Agent**
-- Connect multiple data sources through various MCP servers 📊
-- Use geographic tools for location-based insights 🗺️
-- Integrate payment systems for financial analysis 💰
-- Apply structured thinking frameworks for decision-making 🎯
-
-🏠 **Smart Lifestyle Agent**
-- Combine mapping services with payment integration 🛒
-- Use transportation tools for commute optimization 🚗
-- Integrate web search for local recommendations 🏪
-- Apply intelligent content extraction for information gathering 📱
-
-The MCP ecosystem empowers you to build agents that can seamlessly interact with the real world, accessing live data, performing complex operations, and providing contextual assistance across virtually any domain. Each tool brings specialized capabilities that can be combined to create powerful, multi-functional AI experiences.
+Check our [Agent Scenarios page](https://modelengine-group.github.io/nexent/en/mcp-ecosystem/use-cases.html) for detailed agent use cases and best practices, including travel planning, research assistant, business intelligence, smart lifestyle, and more scenarios.
 
 ## ✨ Key Features
 
@@ -188,15 +100,29 @@ The MCP ecosystem empowers you to build agents that can seamlessly interact with
 
    ![Feature 7](./assets/Feature7.png)
 
-# 🐛 Known Issues
+# 🛠️ Developer Guide
+
+### 🤖 Model Configuration & Provider Recommendations
+
+Check our [Model Providers page](https://modelengine-group.github.io/nexent/en/getting-started/model-providers.html) for detailed model configuration guides and recommended provider information.
 
-1📝 **Code Output May Be Misinterpreted as Executable**  
-   In Nexent conversations, if the model outputs code-like text, it may sometimes be misinterpreted as something that should be executed. We will fix this as soon as possible.
+### 🔧 Hack on Nexent
 
+Want to build from source or add new features? Check the [Contribution Guide](https://modelengine-group.github.io/nexent/en/contributing) for step-by-step instructions.
+
+### 🛠️ Build from Source
+
+Prefer to run Nexent from source code? Follow our [Developer Guide](https://modelengine-group.github.io/nexent/en/getting-started/development-guide) for detailed setup instructions and customization options.
+
+# 🐛 Known Issues
+
+Check our [Known Issues page](https://modelengine-group.github.io/nexent/en/known-issues.html) for the latest issue status and solutions.
 
 # 💬 Community & contact
 
-Join our [Discord community](https://discord.gg/tb5H3S3wyv) to chat with other developers and get help!
+- Browse the [FAQ](https://modelengine-group.github.io/nexent/en/faq) for common install issues.  
+- Join our [Discord community](https://discord.gg/tb5H3S3wyv) to chat with other developers and get help!
+- File bugs or feature ideas in [GitHub Issues](https://github.com/ModelEngine-Group/nexent/issues).
 
 # 📄 License
 

README_CN.md

@@ -53,103 +53,13 @@ bash deploy.sh
 
 当容器运行后，在浏览器中打开 **http://localhost:3000** 并按照设置向导操作。
 
-### 3. 🤖 模型配置与模型提供商推荐
-
-我们建议使用以下模型提供商：
-
-| 模型类型 | 提供商 | 说明 |
-|------------|----------|-------|
-| LLM 与 VLLM | [硅基流动](https://siliconflow.cn/) | 提供免费额度 |
-| LLM 与 VLLM | [阿里云百炼](https://bailian.console.aliyun.com/) | 提供免费额度 |
-| Embedding | [Jina](https://jina.ai/) | 提供免费额度 |
-| TTS 与 STT | [火山引擎语音](https://www.volcengine.com/product/voice-tech) | 个人用户免费 |
-| 搜索 | [EXA](https://exa.ai/) | 提供免费额度 |
-
-您需要在模型配置页面输入以下信息：
-- Base URL
-- API Key
-- Model Name
-
-以下配置需要添加到您的 `.env` 文件中（我们将尽快把这些配置前端化）：
-- TTS 与 STT 相关配置
-- EXA 搜索 API Key
-
-> ℹ️ 由于开发紧张，目前我们仅支持 Jina Embedding 模型。其他模型的支持将在未来版本中添加。有关 Jina API 密钥获取，请参阅我们的[常见问题](https://modelengine-group.github.io/nexent/zh/faq)。
-
-### 4. ❓ 需要帮助？
-
-- 浏览 [常见问题](https://modelengine-group.github.io/nexent/zh/faq) 了解常见安装问题。  
-- 在我们的 [Discord 社区](https://discord.gg/tb5H3S3wyv) 中提问。  
-- 在 [GitHub Issues](https://github.com/ModelEngine-Group/nexent/issues) 中提交错误报告或功能建议。
-
-### 5. 🔧 开发 Nexent
-
-想要从源代码构建或添加新功能？查看 [贡献指南](https://modelengine-group.github.io/nexent/zh/contributing) 获取分步说明。
-
-### 6. 🛠️ 从源码构建
-
-想要从源码运行 Nexent？查看我们的[开发者指南](https://modelengine-group.github.io/nexent/zh/getting-started/development-guide)获取详细的设置说明和自定义选项。
-
 ## 🌱 MCP 工具生态
 
-Nexent 基于模型上下文协议（MCP）工具生态系统构建，为集成各种工具和服务提供了灵活且可扩展的框架。MCP 被誉为"AI 的 USB-C" - 一个通用接口标准，让 AI 智能体能够无缝连接外部数据源、工具和服务。
-
-### 🌐 MCP 社区中心
-
-全球 MCP 生态系统蓬勃发展，多个平台支持 MCP 开发和部署：
-
-| 平台 | 描述 | 备注 |
-|------|------|------|
-| **[GitHub MCP Server](https://github.com/github/github-mcp-server)** | 深度集成 Claude、GPT-4、Copilot 等，支持 Go 和 Python | OAuth/GitHub 账户授权 |
-| **[Qdrant MCP Vector Server](https://github.com/qdrant/mcp-server-qdrant)** | 语义向量存储，兼容 Python/Go | 与 LangChain 等工具兼容 |
-| **[Anthropic Reference MCP Servers](https://github.com/modelcontextprotocol/servers)** | 轻量级教学与原型工具，Python | 包含 fetch、git 等通用工具 |
-| **[AWS Labs MCP Server](https://github.com/awslabs/mcp)** | AWS+Go+CDK 云端参考服务 | 适合云环境 |
-| **[MCP Hub 中国](https://www.mcp-cn.com/)** | 中国精选优质 MCP 服务平台 | 追求质量而非数量，社区驱动 |
-| **[ModelScope MCP 广场](https://modelscope.cn/mcp)** | 中国最大的 MCP 社区，拥有 1,500+ 服务 | 从高德地图到支付宝，全面的服务覆盖 |
-| **Community MCP Servers** | 各类场景源码聚集地 | 多为实验和创新工具 |
-
-### 🛠️ 推荐 MCP 工具
-
-| 工具名称 | 功能 | 描述 |
-|----------|------|------|
-| **[高德地图](https://modelscope.cn/mcp/servers/@amap/amap-maps)** | 地理服务和导航 | 全面的地图、地理编码、路线规划和位置服务 |
-| **[必应搜索中文](https://modelscope.cn/mcp/servers/@yan5236/bing-cn-mcp-server)** | 中文网络搜索 | 优化的中文网络搜索和信息检索 |
-| **[12306车票查询工具](https://modelscope.cn/mcp/servers/@Joooook/12306-mcp)** | 中国铁路购票 | 实时列车时刻表、票务查询和订票助手 |
-| **[支付宝MCP](https://modelscope.cn/mcp/servers/@alipay/mcp-server-alipay)** | 支付和金融服务 | 数字支付、金融工具和服务集成 |
-| **[飞常准-Aviation](https://modelscope.cn/mcp/servers/@variflight-ai/variflight-mcp)** | 航班信息和航空数据 | 实时航班跟踪、时刻表和航空分析 |
-| **[Sequential Thinking](https://modelscope.cn/mcp/servers/@modelcontextprotocol/sequentialthinking)** | 结构化问题解决框架 | 将复杂问题分解为可管理的连续步骤 |
-| **[ArXiv AI搜索服务](https://modelscope.cn/mcp/servers/@blazickjp/arxiv-mcp-server)** | 学术论文搜索和研究 | 科学论文和研究的高级搜索和检索 |
-| **[Firecrawl MCP 服务器](https://modelscope.cn/mcp/servers/@mendableai/firecrawl-mcp-server)** | 网页抓取和内容提取 | 智能网页抓取、数据提取和内容处理 |
+查看我们的[MCP 生态系统页面](https://modelengine-group.github.io/nexent/zh/mcp-ecosystem/overview.html)了解 MCP 工具生态系统的详细信息，包括社区中心、推荐工具和集成指南。
 
 ### 🚀 建议的智能体场景
 
-借助 MCP 强大的生态系统，您可以为各种场景创建复杂的 AI 智能体：
-
-🌍 **旅行规划智能体** 
-- 使用高德地图进行路线规划和导航 📍
-- 集成 12306 进行火车订票 🚄
-- 连接飞常准获取航班信息 ✈️
-- 启用支付宝实现无缝支付 💳
-
-🔬 **研究助手智能体**
-- 利用 ArXiv 搜索学术论文 📚
-- 使用必应搜索进行全面的网络研究 🔍
-- 应用 Sequential Thinking 进行结构化分析 🧠
-- 集成 Firecrawl 进行网络数据提取 🕷️
-
-💼 **商业智能智能体**
-- 通过各种 MCP 服务器连接多个数据源 📊
-- 使用地理工具进行基于位置的洞察 🗺️
-- 集成支付系统进行财务分析 💰
-- 应用结构化思维框架进行决策制定 🎯
-
-🏠 **智能生活智能体**
-- 结合地图服务与支付集成 🛒
-- 使用交通工具优化通勤 🚗
-- 集成网络搜索获取本地推荐 🏪
-- 应用智能内容提取进行信息收集 📱
-
-MCP 生态系统让您能够构建可以与现实世界无缝交互的智能体，访问实时数据、执行复杂操作，并在几乎任何领域提供上下文辅助。每个工具都带来了专门的能力，可以组合起来创建强大的、多功能的 AI 体验。
+查看我们的[智能体场景页面](https://modelengine-group.github.io/nexent/zh/mcp-ecosystem/use-cases.html)了解详细的智能体用例和最佳实践，包括旅行规划、研究助手、商业智能、智能生活等场景。
 
 ## ✨ 主要特性
 
@@ -188,16 +98,29 @@ MCP 生态系统让您能够构建可以与现实世界无缝交互的智能体
 
    ![Feature 7](./assets/Feature7.png)
 
-# 🐛 已知问题
+# 🛠️ 开发者指南
+
+### 🤖 模型配置与模型提供商推荐
 
-1📝 **代码类输出可能被误认为可执行**  
-   Nexent对话时如果模型输出代码类的文本，可能会被错误理解为需要被执行，我们会尽快修复。
+查看我们的[模型提供商页面](https://modelengine-group.github.io/nexent/zh/getting-started/model-providers.html)了解详细的模型配置指南和推荐的提供商信息。
 
+### 🔧 开发 Nexent
 
+想要从源代码构建或添加新功能？查看 [贡献指南](https://modelengine-group.github.io/nexent/zh/contributing) 获取分步说明。
+
+### 🛠️ 从源码构建
+
+想要从源码运行 Nexent？查看我们的[开发者指南](https://modelengine-group.github.io/nexent/zh/getting-started/development-guide)获取详细的设置说明和自定义选项。
+
+# 🐛 已知问题
+
+查看我们的[已知问题页面](https://modelengine-group.github.io/nexent/zh/known-issues.html)了解最新的问题状态和解决方案。
 
 # 💬 社区与联系方式
 
-加入我们的 [Discord 社区](https://discord.gg/tb5H3S3wyv) 与其他开发者交流并获取帮助！
+- 浏览 [常见问题](https://modelengine-group.github.io/nexent/zh/faq) 了解常见安装问题。  
+- 加入我们的 [Discord 社区](https://discord.gg/tb5H3S3wyv) 与其他开发者交流并获取帮助！
+- 在 [GitHub Issues](https://github.com/ModelEngine-Group/nexent/issues) 中提交错误报告或功能建议。
 
 # 📄 许可证
 

doc/docs/.vitepress/config.mts

@@ -30,6 +30,7 @@ export default defineConfig({
               { text: 'Installation & Setup', link: '/en/getting-started/installation' },
               { text: 'Model Providers', link: '/en/getting-started/model-providers' },
               { text: 'Key Features', link: '/en/getting-started/features' },
+              { text: 'Software Architecture', link: '/en/getting-started/software-architecture' },
               { text: 'Development Guide', link: '/en/getting-started/development-guide' },
               { text: 'FAQ', link: '/en/faq' }
             ]
@@ -77,7 +78,7 @@ export default defineConfig({
             ]
           },
           {
-            text: 'Deployment',
+            text: 'Container Build & Containerized Development',
             items: [
               { text: 'Docker Build', link: '/en/deployment/docker-build' },
               { text: 'Dev Container', link: '/en/deployment/devcontainer' }
@@ -131,6 +132,7 @@ export default defineConfig({
               { text: '安装与配置', link: '/zh/getting-started/installation' },
               { text: '模型提供商', link: '/zh/getting-started/model-providers' },
               { text: '核心特性', link: '/zh/getting-started/features' },
+              { text: '软件架构', link: '/zh/getting-started/software-architecture' },
               { text: '开发指南', link: '/zh/getting-started/development-guide' },
               { text: '常见问题', link: '/zh/faq' }
             ]
@@ -178,7 +180,7 @@ export default defineConfig({
             ]
           },
           {
-            text: '部署指南',
+            text: '容器构建与容器化开发',
             items: [
               { text: 'Docker 构建', link: '/zh/deployment/docker-build' },
               { text: '开发容器', link: '/zh/deployment/devcontainer' }