Resume Processing API

Overview

This project provides an API to process and search resumes. It extracts structured information from PDF resumes using pdf-parse and Gemini AI model, encrypts sensitive data, and stores it in MongoDB.

Features

• Upload resume via URL
• Extract structured data (name, email, education, experience, skills)
• Encrypt sensitive data before saving
• Search resumes with token-agnostic and case-insensitive matching
• Deployable on Render with MongoDB Atlas
• User authentication with login functionality

Tech Stack

• Backend: Node.js, Express.js
• Database: MongoDB Atlas
• AI Model: Gemini API
• Deployment: Render (free tier)

Setup Instructions

Prerequisites

• Node.js installed
• MongoDB Atlas account

Installation

1. Clone the repository:

   git clone https://github.com/Kumar-Amitesh/backend-assignment
   cd backend_assignment

2. Install dependencies:

   npm install

3. Create a .env file and add:

   PORT=3000
   MONGO_URI=<your_mongodb_connection_string>
   GEMINI_API_KEY=<your_gemini_api_key>
   JWT_SECRET=<your_JWT_SECRET>
   ENCRYPTION_KEY=<your_ENCRYPTION_key>

4. Start the server:

   node index.js

API Endpoints

1. User Login

POST /login

• Body: { "username":"naval.ravikant", "password":"05111974" }
• Response: { token: "<jwt_token>" }

2. Process Resume (Requires Authentication)

POST /resumes/process

• Headers: Authorization: Bearer <jwt_token>
• Body: { "url": "<pdf-url>" }
• Response: { message: "Resume processed successfully", data: {...} }

3. Search Resume (Requires Authentication)

GET /resumes/search

• Headers: Authorization: Bearer <jwt_token>
• Body: { "name": "John" }
• Response: [ { name: "John Doe", email: "john@example.com", ... }, ... ]

Deployment on Render

• The API is deployed on Render at: Deployed URL
• GitHub Repository: Repo

License

This project is licensed under the MIT License.

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