ContextCV

A full-stack “ContextCV” platform where users author their portfolio in Markdown, publish a public profile, and visitors can chat with an AI that answers strictly from the portfolio content.

Features

• User registration and login (JWT)
• Markdown portfolio editor with live preview
• Public profile page rendering Markdown
• Real-time chat with owner’s AI (answers only from portfolio context)
• Swagger API docs
• Pinecone vector storage for portfolio context
• CORS configured for local dev (React 3000 → API 5000)

Tech Stack

• Backend: Node.js, Express, Socket.io, Mongoose, JWT, Swagger, Google Generative AI, Pinecone
• Frontend: React, Axios, socket.io-client, react-markdown, lucide-react

Backend Setup

1. Install dependencies:

cd backend-node
npm install

2. Environment variables: create .env in backend-node:

PORT=5000
MONGODB_URI=mongodb://localhost:27017/livingcv
JWT_SECRET=replace_with_a_strong_secret

# Google Gemini
GEMINI_API_KEY=your_gemini_api_key

# Pinecone
PINECONE_API_KEY=your_pinecone_api_key
# IMPORTANT: keep names consistent; set BOTH to the same index name
PINECONE_INDEX=my-livingcv-index
PINECONE_INDEX_NAME=my-livingcv-index

3. Start backend:

npm start

• Swagger docs available at: http://localhost:5000/api-docs

API Overview

• Auth
  • POST /api/auth/register
  • POST /api/auth/login → returns { token }
• Portfolio (private)
  • GET /api/portfolio → requires Authorization: Bearer <token>
  • PUT /api/portfolio → save markdown (syncs to Pinecone), requires Bearer
• Portfolio (public)
  • GET /api/portfolio/:userId → public view of portfolio markdown
• Socket.io
  • Client emits: chat_with_bot { targetUserId, question }
  • Server emits: bot_stream chunks of AI answer

RAG and AI

• The user’s portfolio markdown is embedded (Pinecone Llama v2) and stored as a single vector per user with metadata { text: <markdown> }.
• Chat fetches the full text directly via index.fetch([userId]) to ensure accurate context.
• Prompt enforces strict persona: first-person answers, use ONLY provided context; if missing, respond: “I haven’t included that in my portfolio yet.”

Frontend Setup

1. Install dependencies:

cd frontend
npm install

2. Start frontend:

npm start

• Auth/Dashboard: http://localhost:3000/
• Public profile: http://localhost:3000/profile/

Usage Flow

• Register → Login → Edit your Markdown portfolio → Save → Click “View Public”
• Share the public URL /profile/<userId> with anyone
• Visitors ask questions in the chat; AI answers from your portfolio only

Sample Requests

• Register

curl -X POST http://localhost:5000/api/auth/register ^
  -H "Content-Type: application/json" ^
  -d "{\"email\":\"dev@example.com\",\"password\":\"SuperSecret!123\"}"

• Login

curl -X POST http://localhost:5000/api/auth/login ^
  -H "Content-Type: application/json" ^
  -d "{\"email\":\"dev@example.com\",\"password\":\"SuperSecret!123\"}"

• Save portfolio (replace TOKEN)

curl -X PUT http://localhost:5000/api/portfolio ^
  -H "Authorization: Bearer TOKEN" ^
  -H "Content-Type: application/json" ^
  -d "{\"content\":\"# Jane Doe\\n\\n## Skills\\n- Node.js\\n- React\"}"

• Get public portfolio

curl http://localhost:5000/api/portfolio/<userId>

Environment Notes

• Pinecone index should match embedding dimension: 768 (llama-text-embed-v2)
• Ensure both PINECONE_INDEX and PINECONE_INDEX_NAME reference the same index name (the code reads both in different places)
• CORS is enabled in server.js for development

Troubleshooting

• “No chat response”
  • Check backend logs (console) for socket events and Gemini errors
  • Confirm GEMINI_API_KEY and PINECONE_API_KEY are valid
  • Ensure portfolio has been saved at least once (so Pinecone has content)
• “Portfolio not found”
  • The user hasn’t saved yet; save a portfolio in Dashboard first
• Swagger shows no routes
  • Verify swagger-ui-express and swagger-jsdoc are installed
  • Check apis: ['./routes/*.js'] and JSDoc comments exist

License

MIT