以下是专业人士 README.md 后端和前端的文件。
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1.后端自述
地点： my-portfolio-ai-backend/README.md
# 🧠 MCP AI Portfolio Backend
This is the intelligent backend for my personal portfolio website. It leverages **Google Gemini 2.0 Flash** and the **Model Context Protocol (MCP)** to create a "living" AI agent that knows everything about my professional background, GitHub stats, and blogs.
Built with **FastAPI** and **Python**, it acts as a bridge between the frontend and the LLM, enabling tool calling and real-time data fetching.
## 🚀 Features
- **MCP Architecture:** Uses Anthropic's Model Context Protocol to standardize tool usage.
- **Single Source of Truth:** Reads data dynamically from modular Markdown files (`data/`).
- **Live GitHub Stats:** Fetches real-time stars, repositories, and streak data using the GitHub API.
- **Context Aware:** Handles user history and remembers names across sessions.
- **Gemini 2.0 Flash:** Powered by Google's latest high-speed model.
## 📂 Project Structure
/ ├── main.py # FastAPI entry point & Chat Logic ├── server.py # MCP Server & Tool Definitions ├── data/ # The Knowledge Base (Markdown) │ ├── about.md # Bio, Contact, Skills │ ├── experience.md # Work History │ ├── projects.md # Static Projects List │ └── blogs.md # Blog Links ├── .env # Secrets (Not on GitHub) └── requirements.txt # Dependencies
## 🛠️ 安装和设置
1. **克隆存储库：**
git clone cd my-portfolio-ai-backend
1. **创建虚拟环境（可选但推荐）：**
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
1. **安装依赖项：**
pip install -r requirements.txt
1. **设置环境变量：**
创建一个 `.env` 根目录中的文件：
GOOGLE_API_KEY=your_gemini_api_key GITHUB_TOKEN=your_github_classic_token GITHUB_USERNAME=Chauhan-yuvraj DEV_TO_USER=uvizhere
1. **运行服务器：**
uvicorn main:app --reload
API将在 `http://127.0.0.1:8000/chat`.
## 📡 API端点
**发布** `/chat`
**有效载荷：**
{ "message": "What is his GitHub streak?", "username": "Aditya", "history": [ {"role": "user", "content": "Hi"}, {"role": "model", "content": "Hello Aditya!"} ] }
## ☁️ 部署（渲染）
1. 将repo连接到Render。
1. 选择 **Python 3** 运行时间。
1. 构建命令： `pip install -r requirements.txt`
1. 启动命令： `uvicorn main:app --host 0.0.0.0 --port $PORT`
1. 从您的 `.env` 文件。
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2. Frontend README
Location: my-portfolio-frontend/README.md (or your root folder)
# 🎨 Yuvraj's Portfolio & AI Chatbot
A modern, highly interactive portfolio website built with **Next.js**, **TypeScript**, and **Tailwind CSS**.
The highlight of this project is the integrated **AI Assistant (UV)**. Unlike standard chatbots, UV uses the **Model Context Protocol (MCP)** to fetch real-time data about my GitHub activity, read my resume, and answer questions about my specific tech stack.
## ✨ Key Features
- **🤖 AI Agent Integration:** Embeds a custom MCP-powered chatbot that can browse my projects and stats.
- **💾 Persistent Memory:** Remembers the user's name across sessions using LocalStorage.
- **📱 Responsive Design:** Features a dedicated mobile drawer for the chat interface.
- **🎭 Animations:** Smooth transitions and "dust" text effects using **Framer Motion**.
- **🖼️ Dynamic Avatars:** Generates custom pixel-art avatars for users via DiceBear API.
## 🛠️ Tech Stack
- **Framework:** Next.js 14 (App Router)
- **Language:** TypeScript
- **Styling:** Tailwind CSS
- **Animations:** Framer Motion
- **Icons:** Lucide React
- **Backend:** Python (FastAPI + MCP) [Hosted separately]
## 🚀 Getting Started
1. **Clone the repository:**git clone cd my-portfolio
2. **安装依赖项：**
npm install # or yarn install
1. **配置后端：**
打开 `src/services/chatService.ts` 并更新 `API_URL` 指向您的后端：
const API_URL = "http://127.0.0.1:8000/chat"; // Local // OR const API_URL = "https://your-backend.onrender.com/chat"; // Production
1. **运行开发服务器：**
npm run dev
1. 打开 [http://localhost:3000](http://localhost:3000) 使用您的浏览器。
## 📂 架构概述
1. **用户提问** （例如。， *“列出他的repos”*).
1. **前端** 将消息+历史记录发送到 **FastAPI后端**.
1. **后端** 初始化 **Gemini 2.0代理** 系统提示。
1. **双子座** 决定它需要一个工具(`get_github_repos`)并请求它。
1. **MCP服务器** 执行Python函数从GitHub API获取数据。
1. **双子座** 总结原始数据并将自然响应发送回 **前端**.
## 📄 许可证
该项目是开源的，可在MIT许可证下使用。