Sunculp09 的 AI-Portfolio：让招聘方与 AI 代表对话的交互式求职作品集
Sunculp09/AI-Portfolio
Sunculp Shukla 开源了 AI-Portfolio，一个让招聘方直接与 AI 代表对话的交互式求职作品集。项目用 FastAPI 后端、Groq API 和结构化 candidate.json 简历数据生成回答，支持对话记忆、JD 与候选人画像匹配及缺失技能识别，并基于简历内容作答以规避模型幻觉。前端部署在 Vercel，后端部署在 Render。
An AI-powered portfolio that allows recruiters to interact with an AI representative of my professional profile.
Overview
This project uses an AI model, structured candidate data, and a web interface to create an interactive portfolio for recruiters.
Key Features
- AI-powered recruiter chatbot
- Answers questions about my professional profile
- Uses structured candidate data from my resume
- Conversation memory
- Job Description analysis
- JD vs profile matching
- Identifies missing skills
- Designed to avoid AI hallucinations
- AI-powered recruiter chatbot
- Streaming AI responses
- Conversation memory
- Job Description analysis
- JD vs profile matching
- Identifies missing skills
- Resume-grounded responses
- GitHub, LinkedIn, and Resume access
- Deployed frontend and backend
How It Works
- Recruiter enters a question or Job Description.
- FastAPI receives the request.
- The system identifies whether it is a normal question or JD.
- Candidate information is loaded from structured JSON data.
- Groq AI generates the response.
- The response is displayed in the web interface.
Technology Stack
- Python
- FastAPI
- Groq API
- Pydantic
- HTML
- CSS
- JavaScript
- JSON
- Git & GitHub
- Render
- Vercel
Project Architecture
Recruiter ↓ Vercel Frontend ↓ FastAPI Backend ↓ Question / JD Classification ↓ Groq AI ↓ Candidate Profile JSON ↓ AI Response
AI Behavior
The AI representative is designed to:
- Use only the provided candidate information
- Avoid making up information
- Clearly state when information is unavailable
- Answer professional profile-related questions
- Analyze Job Descriptions against the candidate profile
- Provide concise and relevant responses.
Project Structure
AI-Portfolio/
├── main.py
├── chatbot.py
├── candidate.json
├── index.html
├── resume.md
├── requirements.txt
├── pyproject.toml
├── uv.lock
├── README.md
└── .gitignore
Deployment
Frontend: Vercel Backend: Render AI Model: Groq
Purpose
This project demonstrates the integration of Generative AI, structured candidate data, FastAPI, and a web interface to build an interactive AI-powered professional portfolio.
Author:
Sunculp Shukla Data Analytics | AI Engineering | Business Analysis
Live Demo
来源：GitHub · github.com