Nexa – AI-Powered Interview Preparation Platform

Watch the Video Demo: https://vimeo.com/1158085304?fl=ip&fe=ec

Prepare Smarter. Get Hired Faster.

An intelligent interview preparation platform that combines job search, resume parsing, automated resource curation, and AI-powered coaching to help candidates prepare effectively for their dream jobs.

What is Nexa?

Nexa is an end-to-end interview preparation platform that revolutionizes how candidates prepare for job interviews. By leveraging cutting-edge AI and automation, Nexa:

Searches for Jobs - Finds relevant job opportunities based on your resume
Analyzes Job Requirements - Extracts missing skills and requirements using AI
Auto-Curates Learning Resources - Automatically searches and scrapes relevant learning materials
Builds RAG Knowledge Base - Creates a personalized RAG (Retrieval-Augmented Generation) system from curated resources
Provides AI Interview Coach - Chat with an AI coach that has context about your target role and company

Key Features

Resume Parser - AI-powered resume parsing with Groq LLM
Smart Job Search - Intelligent job matching with SerpAPI
Automated Resource Discovery - Uses Tavily AI to find the best learning materials
RAG-Powered Chat - Context-aware interview coaching using embedded knowledge
Chat History - Persistent chat sessions for each job preparation
Dashboard - Real-time analytics and activity tracking

Tech Stack

Frontend

Framework: Next.js 15.5.9 (App Router)
Language: TypeScript
Styling: Tailwind CSS
UI Components: shadcn/ui, kokonutui
State Management: React Hooks
Animations: Framer Motion
Markdown Rendering: react-markdown

Backend & APIs

Runtime: Node.js
Package Manager: pnpm
Authentication: Supabase Auth (Google OAuth, GitHub OAuth, Email OTP)
File Processing: pdf-parse, mammoth

AI & Machine Learning

Service	Model/API	Purpose
Groq	Llama 3.3 70B Versatile	Resume parsing, skill extraction
GitHub Models	GPT-4o	Resource summarization, interview prep responses
Hugging Face	sentence-transformers/all-mpnet-base-v2	Text embeddings for RAG
Tavily AI	Search API	Learning resource discovery

External Services

SerpAPI - Job search and aggregation
Supabase - Authentication & user management
Vercel - Hosting and deployment

Data Processing Pipeline

RAG (Retrieval-Augmented Generation) Pipeline:

Web Scraping - Fetches content from discovered resources
Summarization - GPT-4o generates interview-focused summaries
Chunking - Splits content into semantic chunks
Embedding - Converts chunks to vector embeddings
Retrieval - Cosine similarity search for relevant context
Generation - GPT-4o generates responses with retrieved context

Project Structure

├── app/
│   ├── api/
│   │   ├── auto-prepare-interview/    # Automated prep workflow
│   │   ├── build-vector-db/           # RAG embeddings creation
│   │   ├── find-sources/              # Resource discovery
│   │   ├── get-response/              # Chat completions
│   │   ├── parse-resume/              # Resume parsing endpoint
│   │   ├── search-jobs/               # Job search API
│   │   └── save-selected-sources/     # Resource persistence
│   ├── command-center/                # Analytics dashboard
│   ├── dashboard/                     # Main dashboard layout
│   ├── interview-preperation/         # Chat interface
│   │   ├── ChatInterface.tsx          # Main chat UI
│   │   └── history.ts                 # Chat session management
│   ├── jobsFound/                     # Job listings
│   └── userProfile/                   # Resume upload & profile
│
├── backend/
│   ├── jobSearcher/                   # Job search & matching
│   │   ├── jobSearcher.ts            # Main job search logic
│   │   ├── matcher.ts                # AI-powered job matching
│   │   └── serpApiClient.ts          # SerpAPI integration
│   ├── prep/                          # Resource search
│   │   └── searchprepsources.ts      # Tavily API integration
│   ├── rag/
│   │   ├── chat/                      # Response generation
│   │   │   ├── responsegeneration.ts  # GPT-4o chat
│   │   │   └── retrieval.ts           # Vector search
│   │   └── ingestion/                 # RAG pipeline
│   │       ├── chunking.ts            # Text chunking
│   │       ├── embedding.ts           # HF embeddings
│   │       ├── summarization.ts       # Content summarization
│   │       └── webscraper.ts          # Web content extraction
│   └── resume-parser/                 # AI resume parser
│       ├── aiParser.ts               # Groq-powered parsing
│       ├── pdfExtractor.ts           # PDF text extraction
│       └── types.ts                  # Type definitions
│
├── components/                        # Reusable UI components
│   ├── ui/                           # shadcn/ui components
│   └── auth-page.tsx                 # Authentication UI
│
└── lib/                              # Utilities & helpers
    ├── supabase.ts                   # Supabase client
    └── utils.ts                      # Helper functions

Getting Started

Prerequisites

Node.js 18+
pnpm (recommended) or npm
API keys for required services

Installation

Clone the repository

git clone https://github.com/shilok09/v0-cyberpunk-dashboard-design.git
cd v0-cyberpunk-dashboard-design

Install dependencies

pnpm install

Set up environment variables

Create a .env.local file in the root directory:

# AI Services
GITHUB_TOKEN=your_github_token_here
GROQ_API_KEY=your_groq_api_key_here
HUGGINGFACE_API_TOKEN=your_huggingface_token_here

# Search APIs
SERPAPI_KEY=your_serpapi_key_here
TAVILY_API_KEY=your_tavily_api_key_here

# Authentication (Supabase)
NEXT_PUBLIC_SUPABASE_URL=your_supabase_project_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key

Run the development server

pnpm dev

Open your browser Navigate to http://localhost:3000

API Keys Setup Guide

1. GitHub Models API (GPT-4o)

Visit GitHub Settings → Tokens
Generate a personal access token
Add to .env.local as GITHUB_TOKEN

2. Groq API (Llama 3.3)

Sign up at console.groq.com
Create API key in dashboard
Add to .env.local as GROQ_API_KEY

3. Hugging Face (Embeddings)

Create account at huggingface.co
Generate token at Settings → Access Tokens
Add to .env.local as HUGGINGFACE_API_TOKEN

4. SerpAPI (Job Search)

Sign up at serpapi.com
Get API key from dashboard
Add to .env.local as SERPAPI_KEY

5. Tavily AI (Resource Search)

Sign up at tavily.com
Get API key from dashboard
Add to .env.local as TAVILY_API_KEY

6. Supabase (Authentication)

Create project at supabase.com
Enable Google and GitHub OAuth providers
Copy project URL and anon key
Add to .env.local as NEXT_PUBLIC_SUPABASE_URL and NEXT_PUBLIC_SUPABASE_ANON_KEY

How It Works

Workflow Overview

1. User uploads resume
   ↓
2. AI parses resume (Groq Llama 3.3)
   ↓
3. User searches for jobs
   ↓
4. SerpAPI finds matching jobs
   ↓
5. AI scores job relevance
   ↓
6. User clicks "Start Preparing"
   ↓
7. AUTOMATED FLOW BEGINS:
   ├─ Extract missing skills (GPT-4o)
   ├─ Search for resources (Tavily)
   ├─ Scrape web content
   ├─ Summarize with AI (GPT-4o)
   ├─ Chunk text semantically
   ├─ Generate embeddings (HuggingFace)
   └─ Build vector database
   ↓
8. Chat interface launches with RAG
   ↓
9. User chats with AI coach
   ├─ Retrieves relevant chunks
   ├─ Generates contextual responses (GPT-4o)
   └─ Saves chat history

RAG Architecture

Query → Embedding → Vector Search → Top-K Chunks → Context + Query → GPT-4o → Response

Design Philosophy

Cyberpunk Aesthetic

Dark theme with orange (#f97316) accents
Monospace fonts for technical data
Animated status indicators
Real-time activity logs

User Experience

Fully automated workflow (no manual steps)
Real-time progress feedback
chat history for Context
Persistent sessions per job

Build & Deploy

Local Build

pnpm install
pnpm build

Deploy to Vercel

Push code to GitHub
Import project in Vercel
Add environment variables
Deploy!

Or use CLI:

vercel --prod

Environment Variables for Production

Ensure all API keys from .env.local are added to your Vercel project settings.

Development Notes

Project Commands

pnpm dev          # Start development server (localhost:3000)
pnpm build        # Build for production
pnpm start        # Start production server
pnpm lint         # Run ESLint

Key Technologies

App Router - Next.js 15 with server/client components
API Routes - RESTful endpoints with TypeScript
Real-time Updates - useState + useEffect hooks
File Storage - Server-side storage for RAG artifacts

Team

Shilok Kumar - Developer

Fatima Tu Zahra - Developer

Ramalah Amir - Developer

Built with Love For The World

Name		Name	Last commit message	Last commit date
Latest commit History 87 Commits
app		app
backend		backend
components		components
hooks		hooks
lib		lib
public		public
styles		styles
.gitignore		.gitignore
README.md		README.md
components.json		components.json
next.config.mjs		next.config.mjs
package-lock.json		package-lock.json
package.json		package.json
pnpm-lock.yaml		pnpm-lock.yaml
postcss.config.mjs		postcss.config.mjs
sample.env		sample.env
tailwind.config.ts		tailwind.config.ts
tsconfig.json		tsconfig.json

Folders and files

Latest commit

History

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Nexa – AI-Powered Interview Preparation Platform

What is Nexa?

Key Features

Tech Stack

Frontend

Backend & APIs

AI & Machine Learning

External Services

Data Processing Pipeline

Project Structure

Getting Started

Prerequisites

Installation

API Keys Setup Guide

1. GitHub Models API (GPT-4o)

2. Groq API (Llama 3.3)

3. Hugging Face (Embeddings)

4. SerpAPI (Job Search)

5. Tavily AI (Resource Search)

6. Supabase (Authentication)

How It Works

Workflow Overview

RAG Architecture

Design Philosophy

Build & Deploy

Local Build

Deploy to Vercel

Environment Variables for Production

Development Notes

Project Commands

Key Technologies

Team

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