RAG Interview Preparation Application

An intelligent interview preparation system that generates personalized questions based on candidate profiles and documents using RAG (Retrieval-Augmented Generation) technology.

Features

• Multi-step persona assessment form
• Document upload and processing (PDF, DOCX, TXT, RTF)
• AI-powered question generation across multiple categories
• Local-first processing for privacy
• Export capabilities (PDF, Word, JSON)
• Cross-platform support (Web + Desktop)

Project Structure

rag-interview-prep/
├── frontend/          # React TypeScript application
├── backend/           # FastAPI Python application
├── docs/              # Documentation
├── scripts/           # Development and deployment scripts
└── .kiro/             # Kiro specifications

Quick Start

Option 1: Docker (Recommended) 🐳

Prerequisites:

• Docker and Docker Compose

One-command setup:

./scripts/docker-setup.sh

Development commands:

# Start application
./scripts/docker-dev.sh start

# View logs
./scripts/docker-dev.sh logs

# Stop application
./scripts/docker-dev.sh stop

# Restart services
./scripts/docker-dev.sh restart

# Check status
./scripts/docker-dev.sh status

Option 2: Manual Setup

Prerequisites:

• Node.js 18+ and npm
• Python 3.11+
• Git

Setup steps:

# 1. Setup backend
cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements-minimal.txt
cp .env.example .env

# 2. Setup frontend
cd frontend
npm install

# 3. Start development servers
# Terminal 1 - Backend
cd backend && source venv/bin/activate && python main.py

# Terminal 2 - Frontend
cd frontend && npm run dev

Technology Stack

Frontend:

• React 18 with TypeScript
• Vite for build tooling
• Tailwind CSS + shadcn/ui
• Zustand for state management
• React Hook Form + Zod validation

Backend:

• FastAPI with Python 3.11+
• LangChain for document processing
• FAISS for vector search
• SQLite for local storage
• Sentence-Transformers for embeddings

Docker Commands

Basic Operations

# Start all services
docker-compose up -d

# Stop all services
docker-compose down

# View logs
docker-compose logs -f

# Check service status
docker-compose ps

# Rebuild services
docker-compose build --no-cache

Development Workflow

# Use the helper script for common operations
./scripts/docker-dev.sh start    # Start application
./scripts/docker-dev.sh logs     # View logs
./scripts/docker-dev.sh stop     # Stop application
./scripts/docker-dev.sh status   # Check status

Accessing Services

• Frontend: http://localhost:3001
• Backend API: http://localhost:8001
• API Documentation: http://localhost:8001/docs
• Redis: localhost:6379

License

MIT License