docs: make Chinese README the default

README.md

@@ -1,3 +1,9 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/patchy631/ai-engineering-hub) · [上游 README](https://github.com/patchy631/ai-engineering-hub/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 <p align="center">
   <a href="https://trendshift.io/repositories/12800">
     <img src="assets/TRENDING-BADGE.png" alt="Trending Badge" style="width: 250px; height: 55px;" width="250" height="55"/>
@@ -12,212 +18,212 @@
 
 # AI Engineering Hub 🚀
 
-Welcome to the **AI Engineering Hub** - your comprehensive resource for learning and building with AI!
+欢迎来到 **AI Engineering Hub** —— 你学习与构建 AI 应用的全面资源库！
 
-## 🌟 Why This Repo?
+## 🌟 为什么选择本仓库？
 
-AI Engineering is advancing rapidly, and staying at the forefront requires both deep understanding and hands-on experience. Here, you will find:
-- **93+ Production-Ready Projects** across all skill levels
-- In-depth tutorials on **LLMs, RAG, Agents, and more**
-- Real-world **AI agent** applications
-- Examples to implement, adapt, and scale in your projects
+AI Engineering（AI 工程）正在快速发展，要站在前沿既需要深入理解，也需要动手实践。在这里，你将找到：
+- **93+ 个生产就绪项目**，覆盖各个技能水平
+- 关于 **LLM、RAG、Agent** 等的深度教程
+- 真实世界的 **AI agent** 应用案例
+- 可在你的项目中实现、改编和扩展的示例
 
-Whether you're a beginner, practitioner, or researcher, this repo provides resources for all skill levels to experiment and succeed in AI engineering.
+无论你是初学者、实践者还是研究者，本仓库都为各个技能水平提供资源，助你在 AI 工程中实验并取得成功。
 
 ---
 
-## 📋 Table of Contents
+## 📋 目录
 
-- [Getting Started](#-getting-started)
+- [快速入门](#-getting-started)
 - [Newsletter](#-stay-updated-with-our-newsletter)
-- [Projects by Difficulty](#-projects-by-difficulty)
-  - [Beginner Projects (22)](#-beginner-projects)
-  - [Intermediate Projects (48)](#-intermediate-projects)
-  - [Advanced Projects (23)](#-advanced-projects)
-- [Contributing](#-contribute-to-the-ai-engineering-hub)
-- [License](#-license)
+- [按难度划分的项目](#-projects-by-difficulty)
+  - [初级项目 (22)](#-beginner-projects)
+  - [中级项目 (48)](#-intermediate-projects)
+  - [高级项目 (23)](#-advanced-projects)
+- [参与贡献](#-contribute-to-the-ai-engineering-hub)
+- [许可证](#-license)
 
 ---
 
-## 🎯 Getting Started
+## 🎯 快速入门
 
-New to AI Engineering? Start here:
+刚接触 AI Engineering？从这里开始：
 
-1. **Complete Beginners**: Check out the [AI Engineering Roadmap](./ai-engineering-roadmap) for a comprehensive learning path
-2. **Learn the Basics**: Start with [Beginner Projects](#-beginner-projects) like OCR apps and simple RAG implementations
-3. **Build Your Skills**: Move to [Intermediate Projects](#-intermediate-projects) with agents and complex workflows
-4. **Master Advanced Concepts**: Tackle [Advanced Projects](#-advanced-projects) including fine-tuning and production systems
+1. **完全零基础**：查看 [AI Engineering Roadmap](./ai-engineering-roadmap)，获取完整学习路径
+2. **学习基础**：从 [初级项目](#-beginner-projects) 入手，例如 OCR 应用和简单的 RAG 实现
+3. **提升技能**：进阶到 [中级项目](#-intermediate-projects)，涉及 agent 与复杂工作流
+4. **掌握高级概念**：挑战 [高级项目](#-advanced-projects)，包括微调与生产级系统
 
 ---
 
-## 📬 Stay Updated with Our Newsletter!
+## 📬 订阅我们的 Newsletter，保持更新！
 
-**Get a FREE Data Science eBook** 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. [Subscribe now!](https://join.dailydoseofds.com)
+**免费获取 Data Science 电子书** 📖 —— 订阅我们的 newsletter 即可获得，内含 150+ 门 Data Science 核心课程！随时掌握最新教程、见解与独家资源。[立即订阅！](https://join.dailydoseofds.com)
 
 [![Daily Dose of Data Science Newsletter](https://github.com/patchy631/ai-engineering/blob/main/resources/join_ddods.png)](https://join.dailydoseofds.com)
 
 ---
 
-## 🎓 Projects by Difficulty
+## 🎓 按难度划分的项目
 
-### 🟢 Beginner Projects
+### 🟢 初级项目
 
-Perfect for getting started with AI engineering. These projects focus on single components and straightforward implementations.
+非常适合 AI 工程入门。这些项目聚焦单一组件与直观实现。
 
 #### OCR & Vision
-- [**LaTeX OCR with Llama**](./LaTeX-OCR-with-Llama) - Convert LaTeX equation images to code using Llama 3.2 vision
-- [**Llama OCR**](./llama-ocr) - 100% local OCR app with Llama 3.2 and Streamlit
-- [**Gemma-3 OCR**](./gemma3-ocr) - Local OCR with structured text extraction using Gemma-3
-- [**Qwen 2.5 OCR**](./qwen-2.5VL-ocr) - Text extraction using Qwen 2.5 VL model
+- [**LaTeX OCR with Llama**](./LaTeX-OCR-with-Llama) - 使用 Llama 3.2 vision 将 LaTeX 公式图片转换为代码
+- [**Llama OCR**](./llama-ocr) - 基于 Llama 3.2 与 Streamlit 的 100% 本地 OCR 应用
+- [**Gemma-3 OCR**](./gemma3-ocr) - 使用 Gemma-3 进行本地 OCR 与结构化文本提取
+- [**Qwen 2.5 OCR**](./qwen-2.5VL-ocr) - 使用 Qwen 2.5 VL 模型进行文本提取
 
 #### Chat Interfaces & UI
-- [**Local ChatGPT with DeepSeek**](./local-chatgpt%20with%20DeepSeek) - Mini-ChatGPT with DeepSeek-R1 and Chainlit
-- [**Local ChatGPT with Llama**](./local-chatgpt) - ChatGPT clone using Llama 3.2 vision
-- [**Local ChatGPT with Gemma 3**](./local-chatgpt%20with%20Gemma%203) - Local chat interface with Gemma 3
-- [**DeepSeek Thinking UI**](./deepseek-thinking-ui) - ChatGPT with visible reasoning using DeepSeek-R1
-- [**Qwen3 Thinking UI**](./qwen3-thinking-ui) - Thinking UI with Qwen3:4B and Streamlit
-- [**GPT-OSS Thinking UI**](./gpt-oss-thinking-ui) - GPT-OSS with reasoning visualization
-- [**Streaming AI Chatbot**](./streaming-ai-chatbot) - Real-time AI streaming with Motia framework
+- [**Local ChatGPT with DeepSeek**](./local-chatgpt%20with%20DeepSeek) - 基于 DeepSeek-R1 与 Chainlit 的迷你 ChatGPT
+- [**Local ChatGPT with Llama**](./local-chatgpt) - 使用 Llama 3.2 vision 的 ChatGPT 克隆版
+- [**Local ChatGPT with Gemma 3**](./local-chatgpt%20with%20Gemma%203) - 基于 Gemma 3 的本地聊天界面
+- [**DeepSeek Thinking UI**](./deepseek-thinking-ui) - 使用 DeepSeek-R1 展示可见推理过程的 ChatGPT
+- [**Qwen3 Thinking UI**](./qwen3-thinking-ui) - 基于 Qwen3:4B 与 Streamlit 的 Thinking UI
+- [**GPT-OSS Thinking UI**](./gpt-oss-thinking-ui) - 带推理可视化的 GPT-OSS
+- [**Streaming AI Chatbot**](./streaming-ai-chatbot) - 基于 Motia 框架的实时 AI 流式对话
 
 #### Basic RAG
-- [**Simple RAG Workflow**](./simple-rag-workflow) - Basic RAG with LlamaIndex and Ollama
-- [**Document Chat RAG**](./document-chat-rag) - Chat with documents using Llama 3.3
-- [**Fastest RAG Stack**](./fastest-rag-stack) - Fast RAG with SambaNova, LlamaIndex, and Qdrant
-- [**GitHub RAG**](./github-rag) - Chat with GitHub repos locally
-- [**ModernBERT RAG**](./modernbert-rag) - RAG with ModernBert embeddings
-- [**Llama 4 RAG**](./llama-4-rag) - RAG powered by Meta's Llama 4
+- [**Simple RAG Workflow**](./simple-rag-workflow) - 使用 LlamaIndex 与 Ollama 的基础 RAG
+- [**Document Chat RAG**](./document-chat-rag) - 使用 Llama 3.3 与文档对话
+- [**Fastest RAG Stack**](./fastest-rag-stack) - 基于 SambaNova、LlamaIndex 与 Qdrant 的快速 RAG
+- [**GitHub RAG**](./github-rag) - 在本地与 GitHub 仓库对话
+- [**ModernBERT RAG**](./modernbert-rag) - 使用 ModernBert embeddings 的 RAG
+- [**Llama 4 RAG**](./llama-4-rag) - 由 Meta 的 Llama 4 驱动的 RAG
 
 #### Multimodal & Media
-- [**Image Generation with Janus-Pro**](./imagegen-janus-pro) - Local image generation with DeepSeek Janus-pro 7B
-- [**Video RAG with Gemini**](./video-rag-gemini) - Chat with videos using Gemini AI
+- [**Image Generation with Janus-Pro**](./imagegen-janus-pro) - 使用 DeepSeek Janus-pro 7B 进行本地图像生成
+- [**Video RAG with Gemini**](./video-rag-gemini) - 使用 Gemini AI 与视频对话
 
 #### Other Tools
-- [**Website to API with FireCrawl**](./Website-to-API-with-FireCrawl) - Convert websites to APIs
-- [**AI News Generator**](./ai_news_generator) - News generation with CrewAI and Cohere
-- [**Siamese Network**](./siamese-network) - Digit similarity detection on MNIST
+- [**Website to API with FireCrawl**](./Website-to-API-with-FireCrawl) - 将网站转换为 API
+- [**AI News Generator**](./ai_news_generator) - 使用 CrewAI 与 Cohere 生成新闻
+- [**Siamese Network**](./siamese-network) - 在 MNIST 上进行数字相似度检测
 
 ---
 
-### 🟡 Intermediate Projects
+### 🟡 中级项目
 
-Multi-component systems, agentic workflows, and advanced features for experienced practitioners.
+多组件系统、agentic 工作流与高级特性，适合有经验的实践者。
 
 #### AI Agents & Workflows
-- [**YouTube Trend Analysis**](./Youtube-trend-analysis) - Analyze YouTube trends with CrewAI and BrightData
-- [**AutoGen Stock Analyst**](./autogen-stock-analyst) - Advanced analyst with Microsoft AutoGen
-- [**Agentic RAG**](./agentic_rag) - RAG with document search and web fallback
-- [**Agentic RAG with DeepSeek**](./agentic_rag_deepseek) - Enterprise agentic RAG with GroundX
-- [**Book Writer Flow**](./book-writer-flow) - Automated book writing with CrewAI
-- [**Content Planner Flow**](./content_planner_flow) - Content workflow with CrewAI Flow
-- [**Brand Monitoring**](./brand-monitoring) - Automated brand monitoring system
-- [**Hotel Booking Crew**](./hotel-booking-crew) - Multi-agent hotel booking with DeepSeek-R1
-- [**Deploy Agentic RAG**](./deploy-agentic-rag) - Private Agentic RAG API with LitServe
-- [**Zep Memory Assistant**](./zep-memory-assistant) - AI Agent with human-like memory
-- [**Agent with MCP Memory**](./agent-with-mcp-memory) - Agents with Graphiti memory and Opik
-- [**ACP Code**](./acp-code) - Agent Communication Protocol demo
-- [**Motia Content Creation**](./motia-content-creation) - Social media automation workflow
+- [**YouTube Trend Analysis**](./Youtube-trend-analysis) - 使用 CrewAI 与 BrightData 分析 YouTube 趋势
+- [**AutoGen Stock Analyst**](./autogen-stock-analyst) - 基于 Microsoft AutoGen 的高级分析师
+- [**Agentic RAG**](./agentic_rag) - 支持文档搜索与网页回退的 RAG
+- [**Agentic RAG with DeepSeek**](./agentic_rag_deepseek) - 基于 GroundX 的企业级 agentic RAG
+- [**Book Writer Flow**](./book-writer-flow) - 使用 CrewAI 自动撰写书籍
+- [**Content Planner Flow**](./content_planner_flow) - 基于 CrewAI Flow 的内容工作流
+- [**Brand Monitoring**](./brand-monitoring) - 自动化品牌监测系统
+- [**Hotel Booking Crew**](./hotel-booking-crew) - 基于 DeepSeek-R1 的多 agent 酒店预订
+- [**Deploy Agentic RAG**](./deploy-agentic-rag) - 使用 LitServe 部署私有 Agentic RAG API
+- [**Zep Memory Assistant**](./zep-memory-assistant) - 具备类人记忆的 AI Agent
+- [**Agent with MCP Memory**](./agent-with-mcp-memory) - 集成 Graphiti 记忆与 Opik 的 Agent
+- [**ACP Code**](./acp-code) - Agent Communication Protocol 演示
+- [**Motia Content Creation**](./motia-content-creation) - 社交媒体自动化工作流
 
 #### Voice & Audio
-- [**Real-time Voice Bot**](./real-time-voicebot) - Conversational travel guide with AssemblyAI
-- [**RAG Voice Agent**](./rag-voice-agent) - Real-time RAG Voice Agent with Cartesia
-- [**Chat with Audios**](./chat-with-audios) - RAG over audio files
-- [**Audio Analysis Toolkit**](./audio-analysis-toolkit) - Audio analysis with AssemblyAI
-- [**Multilingual Meeting Notes**](./multilingual-meeting-notes-generator) - Auto meeting notes with language detection
+- [**Real-time Voice Bot**](./real-time-voicebot) - 基于 AssemblyAI 的对话式旅行向导
+- [**RAG Voice Agent**](./rag-voice-agent) - 基于 Cartesia 的实时 RAG Voice Agent
+- [**Chat with Audios**](./chat-with-audios) - 面向音频文件的 RAG
+- [**Audio Analysis Toolkit**](./audio-analysis-toolkit) - 使用 AssemblyAI 进行音频分析
+- [**Multilingual Meeting Notes**](./multilingual-meeting-notes-generator) - 支持语言检测的自动会议纪要
 
 #### Advanced RAG
-- [**RAG with Dockling**](./rag-with-dockling) - RAG over Excel with IBM's Docling
-- [**Trustworthy RAG**](./trustworthy-rag) - RAG over complex docs with TLM
-- [**Fastest RAG with Milvus and Groq**](./fastest-rag-milvus-groq) - Sub-15ms retrieval latency
-- [**Chat with Code**](./chat-with-code) - Chat with code using Qwen3-Coder
-- [**RAG SQL Router**](./rag-sql-router) - Agent with RAG and SQL routing
+- [**RAG with Dockling**](./rag-with-dockling) - 使用 IBM 的 Docling 对 Excel 进行 RAG
+- [**Trustworthy RAG**](./trustworthy-rag) - 使用 TLM 对复杂文档进行 RAG
+- [**Fastest RAG with Milvus and Groq**](./fastest-rag-milvus-groq) - 亚 15ms 检索延迟
+- [**Chat with Code**](./chat-with-code) - 使用 Qwen3-Coder 与代码对话
+- [**RAG SQL Router**](./rag-sql-router) - 集成 RAG 与 SQL 路由的 Agent
 
 #### Multimodal
-- [**DeepSeek Multimodal RAG**](./deepseek-multimodal-RAG) - MultiModal RAG with DeepSeek-Janus-Pro
-- [**ColiVara Website RAG**](./Colivara-deepseek-website-RAG) - MultiModal RAG for websites
-- [**Multimodal RAG with AssemblyAI**](./multimodal-rag-assemblyai) - Audio + vector database + CrewAI
+- [**DeepSeek Multimodal RAG**](./deepseek-multimodal-RAG) - 基于 DeepSeek-Janus-Pro 的多模态 RAG
+- [**ColiVara Website RAG**](./Colivara-deepseek-website-RAG) - 面向网站的多模态 RAG
+- [**Multimodal RAG with AssemblyAI**](./multimodal-rag-assemblyai) - 音频 + 向量数据库 + CrewAI
 
 #### MCP (Model Context Protocol)
-- [**Cursor Linkup MCP**](./cursor_linkup_mcp) - Custom MCP with deep web search
-- [**EyeLevel MCP RAG**](./eyelevel-mcp-rag) - MCP for RAG over complex docs
-- [**LlamaIndex MCP**](./llamaindex-mcp) - Local MCP client with LlamaIndex
-- [**MCP Agentic RAG**](./mcp-agentic-rag) - MCP-powered Agentic RAG for Cursor
-- [**MCP Agentic RAG Firecrawl**](./mcp-agentic-rag-firecrawl) - Agentic RAG with Firecrawl
-- [**MCP Video RAG**](./mcp-video-rag) - Video RAG using Ragie via MCP
-- [**MCP Voice Agent**](./mcp-voice-agent) - Voice agent with Firecrawl and Supabase
-- [**SDV MCP**](./sdv-mcp) - Synthetic Data Vault orchestration
-- [**KitOps MCP**](./kitops-mcp) - ML model management with KitOps
-- [**Stagehand × MCP-Use**](./stagehand%20x%20mcp-use) - Web automation with Stagehand MCP
+- [**Cursor Linkup MCP**](./cursor_linkup_mcp) - 支持深度网页搜索的自定义 MCP
+- [**EyeLevel MCP RAG**](./eyelevel-mcp-rag) - 面向复杂文档的 MCP RAG
+- [**LlamaIndex MCP**](./llamaindex-mcp) - 基于 LlamaIndex 的本地 MCP 客户端
+- [**MCP Agentic RAG**](./mcp-agentic-rag) - 面向 Cursor 的 MCP 驱动 Agentic RAG
+- [**MCP Agentic RAG Firecrawl**](./mcp-agentic-rag-firecrawl) - 集成 Firecrawl 的 Agentic RAG
+- [**MCP Video RAG**](./mcp-video-rag) - 通过 MCP 使用 Ragie 进行 Video RAG
+- [**MCP Voice Agent**](./mcp-voice-agent) - 集成 Firecrawl 与 Supabase 的 Voice agent
+- [**SDV MCP**](./sdv-mcp) - Synthetic Data Vault 编排
+- [**KitOps MCP**](./kitops-mcp) - 使用 KitOps 进行 ML 模型管理
+- [**Stagehand × MCP-Use**](./stagehand%20x%20mcp-use) - 基于 Stagehand MCP 的 Web 自动化
 
-#### Model Comparison & Evaluation
-- [**Evaluation and Observability**](./eval-and-observability) - E2E RAG evaluation with CometML Opik
-- [**Llama 4 vs DeepSeek-R1**](./llama-4_vs_deepseek-r1) - Compare models using RAG
-- [**Qwen3 vs DeepSeek-R1**](./qwen3_vs_deepseek-r1) - Model comparison with Opik
-- [**O3 vs Claude Code**](./o3-vs-claude-code) - Compare Claude 3.7 and o3
-- [**Sonnet4 vs O4**](./sonnet4-vs-o4) - Code generation comparison
-- [**Sonnet4 vs Qwen3-Coder**](./sonnet4-vs-qwen3-coder) - Coder model comparison
-- [**Code Model Comparison**](./code-model-comparison) - Frontier model code comparison
-- [**GPT-OSS vs Qwen3**](./gpt-oss-vs-qwen3) - Reasoning capabilities comparison
+#### 模型对比与评估
+- [**评估与可观测性（Evaluation and Observability）**](./eval-and-observability) - 使用 CometML Opik 进行端到端 RAG 评估
+- [**Llama 4 vs DeepSeek-R1**](./llama-4_vs_deepseek-r1) - 使用 RAG 对比模型
+- [**Qwen3 vs DeepSeek-R1**](./qwen3_vs_deepseek-r1) - 使用 Opik 进行模型对比
+- [**O3 vs Claude Code**](./o3-vs-claude-code) - 对比 Claude 3.7 与 o3
+- [**Sonnet4 vs O4**](./sonnet4-vs-o4) - 代码生成对比
+- [**Sonnet4 vs Qwen3-Coder**](./sonnet4-vs-qwen3-coder) - Coder 模型对比
+- [**代码模型对比（Code Model Comparison）**](./code-model-comparison) - 前沿模型代码对比
+- [**GPT-OSS vs Qwen3**](./gpt-oss-vs-qwen3) - 推理能力对比
 
 ---
 
-### 🔴 Advanced Projects
+### 🔴 高级项目
 
-Complex systems, fine-tuning, production deployments, and cutting-edge implementations.
+复杂系统、微调、生产部署与前沿实现。
 
-#### Fine-tuning & Model Development
-- [**DeepSeek Fine-tuning**](./DeepSeek-finetuning) - Fine-tune DeepSeek with Unsloth and Ollama
-- [**Build Reasoning Model**](./Build-reasoning-model) - Build DeepSeek-R1-like reasoning models
-- [**Attention Is All You Need Implementation**](./attention-is-all-you-need-impl) - Transformer architecture from scratch
+#### 微调与模型开发
+- [**DeepSeek 微调（Fine-tuning）**](./DeepSeek-finetuning) - 使用 Unsloth 与 Ollama 微调 DeepSeek
+- [**构建推理模型（Build Reasoning Model）**](./Build-reasoning-model) - 构建类 DeepSeek-R1 的推理模型
+- [**Attention Is All You Need 实现**](./attention-is-all-you-need-impl) - 从零实现 Transformer 架构
 
-#### Advanced Agent Systems
-- [**NVIDIA Demo**](./nvidia-demo) - Documentation writer with CrewAI Flows and NVIDIA NIM
-- [**Documentation Writer Flow**](./documentation-writer-flow) - Agentic documentation workflow
-- [**Multi-Agent Deep Researcher**](./Multi-Agent-deep-researcher-mcp-windows-linux) - MCP-powered deep researcher
-- [**Multiplatform Deep Researcher**](./multiplatform_deep_researcher) - Multi-platform research with BrightData
-- [**Web Browsing Agent**](./web-browsing-agent) - Browser automation with CrewAI and Stagehand
-- [**Paralegal Agent Crew**](./paralegal-agent-crew) - Intelligent paralegal with RAG
-- [**FireCrawl Agent**](./firecrawl-agent) - Corrective RAG with web search fallback
-- [**Context Engineering Workflow**](./context-engineering-workflow) - Research assistant with TensorLake and Zep
-- [**Parlant Conversational Agent**](./parlant-conversational-agent) - Compliance-driven conversational agent
-- [**Stock Portfolio Analysis Agent**](./stock-portfolio-analysis-agent) - Portfolio analysis with React frontend
-- [**Guidelines vs Traditional Prompt**](./guidelines-vs-traditional-prompt) - Structured guidelines comparison
+#### 高级智能体系统
+- [**NVIDIA Demo**](./nvidia-demo) - 使用 CrewAI Flows 与 NVIDIA NIM 的文档撰写工具
+- [**文档撰写流程（Documentation Writer Flow）**](./documentation-writer-flow) - 智能体式文档工作流
+- [**多智能体深度研究员（Multi-Agent Deep Researcher）**](./Multi-Agent-deep-researcher-mcp-windows-linux) - 基于 MCP 的深度研究员
+- [**多平台深度研究员（Multiplatform Deep Researcher）**](./multiplatform_deep_researcher) - 使用 BrightData 进行多平台研究
+- [**网页浏览智能体（Web Browsing Agent）**](./web-browsing-agent) - 使用 CrewAI 与 Stagehand 的浏览器自动化
+- [**法务助理智能体团队（Paralegal Agent Crew）**](./paralegal-agent-crew) - 结合 RAG 的智能法务助理
+- [**FireCrawl Agent**](./firecrawl-agent) - 带网页搜索回退的纠正式 RAG（Corrective RAG）
+- [**上下文工程工作流（Context Engineering Workflow）**](./context-engineering-workflow) - 使用 TensorLake 与 Zep 的研究助手
+- [**Parlant 对话智能体（Parlant Conversational Agent）**](./parlant-conversational-agent) - 合规驱动的对话智能体
+- [**股票投资组合分析智能体（Stock Portfolio Analysis Agent）**](./stock-portfolio-analysis-agent) - 带 React 前端的投资组合分析
+- [**Guidelines vs 传统 Prompt**](./guidelines-vs-traditional-prompt) - 结构化指南对比
 
-#### Advanced MCP & Infrastructure
-- [**MindsDB MCP**](./mindsdb-mcp) - Unified MCP for all data sources
-- [**Financial Analyst DeepSeek**](./financial-analyst-deepseek) - MCP financial analysis workflow
-- [**Graphiti MCP**](./graphiti-mcp) - Persistent memory with Zep's Graphiti
-- [**Pixeltable MCP**](./pixeltable-mcp) - Unified multimodal data orchestration
-- [**Ultimate AI Assistant**](./ultimate-ai-assitant-using-mcp) - Multi-MCP server interface
+#### 高级 MCP 与基础设施
+- [**MindsDB MCP**](./mindsdb-mcp) - 面向所有数据源的统一 MCP
+- [**Financial Analyst DeepSeek**](./financial-analyst-deepseek) - MCP 金融分析工作流
+- [**Graphiti MCP**](./graphiti-mcp) - 使用 Zep 的 Graphiti 实现持久化记忆
+- [**Pixeltable MCP**](./pixeltable-mcp) - 统一多模态数据编排
+- [**Ultimate AI Assistant**](./ultimate-ai-assitant-using-mcp) - 多 MCP 服务器接口
 
-#### Production Systems
-- [**GroundX Document Pipeline**](./groundX-doc-pipeline) - World-class document processing
-- [**NotebookLM Clone**](./notebook-lm-clone) - Full NotebookLM with RAG, citations, and podcasts
+#### 生产系统
+- [**GroundX 文档流水线（Document Pipeline）**](./groundX-doc-pipeline) - 世界级文档处理
+- [**NotebookLM Clone**](./notebook-lm-clone) - 完整 NotebookLM，含 RAG、引用与播客
 
-#### Learning Resources
-- [**AI Engineering Roadmap**](./ai-engineering-roadmap) - Complete guide from Python to production AI
+#### 学习资源
+- [**AI 工程路线图（AI Engineering Roadmap）**](./ai-engineering-roadmap) - 从 Python 到生产级 AI 的完整指南
 
 ---
 
-## 📢 Contribute to the AI Engineering Hub!
+## 📢 为 AI Engineering Hub 做贡献！
 
-We welcome contributors! Whether you want to add new tutorials, improve existing code, or report issues, your contributions make this community thrive. Here's how to get involved:
+我们欢迎贡献者！无论你是想添加新教程、改进现有代码，还是报告问题，你的贡献都能让这个社区蓬勃发展。参与方式如下：
 
-1. **Fork** the repository
-2. Create a new branch for your contribution
-3. Submit a **Pull Request** and describe the improvements
+1. **Fork** 本仓库
+2. 为你的贡献创建新分支
+3. 提交 **Pull Request** 并说明改进内容
 
-Check out our [contributing guidelines](CONTRIBUTING.md) for more details.
+更多详情请参阅我们的[贡献指南](CONTRIBUTING.md)。
 
 ---
 
-## 📜 License
+## 📜 许可证
 
-This repository is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+本仓库采用 MIT License 许可 — 详见 [LICENSE](LICENSE) 文件。
 
 ---
 
-## 💬 Connect
+## 💬 联系
 
-For discussions, suggestions, and more, feel free to [create an issue](https://github.com/patchy631/ai-engineering/issues) or reach out directly!
+欢迎讨论、提出建议等，可随时[创建 issue](https://github.com/patchy631/ai-engineering/issues))或直接联系我们！
 
 **Happy Coding!** 🎉