Ready2Interview

Ready2Interview is an AI-powered mock interview platform that generates personalized interview questions based on a user's resume and target job description.

The platform uses Retrieval-Augmented Generation (RAG) to retrieve relevant experience from the candidate's resume, generate contextual interview questions, and provide structured feedback on each answer.

Features

• User registration and authentication
• PDF resume upload and processing
• Resume text extraction and chunking
• Vector embeddings with semantic search
• Job description management
• RAG-based resume context retrieval
• Personalized AI interview question generation
• Real-time question streaming
• Multiple interview types and difficulty levels
• AI-powered answer evaluation
• Structured scores, strengths, weaknesses, and follow-up questions
• Interview session history
• User-level data isolation

---

How It Works

1. Upload Resume

   • The user uploads a PDF resume.
   • Resume processing runs asynchronously in the background.

2. Resume Processing

   • Text is extracted from the PDF.
   • The resume is divided into smaller chunks.
   • Each chunk is converted into a vector embedding and stored in PostgreSQL with pgvector.

3. Add Job Description

   • The user provides a company, role, and job description.

4. Start Interview

   • The user selects a processed resume, job description, interview type, and difficulty.

5. Retrieve Relevant Context

   • The job description is used to search the resume vector database.
   • The most relevant resume sections are retrieved using vector similarity search.

6. Generate Interview Question

   • Relevant resume context, the job description, and previous questions are provided to the LLM.
   • The next interview question is streamed to the frontend in real time.

7. Submit Answer

   • The user submits an answer to the generated question.

8. Receive AI Feedback

   • The answer is evaluated using a structured rubric.
   • The platform returns an overall score, category scores, strengths, weaknesses, and a follow-up question.

---

Architecture

                         ┌─────────────────────┐
                         │       User          │
                         └──────────┬──────────┘
                                    │
                                    ▼
                         ┌─────────────────────┐
                         │   Next.js Frontend  │
                         │  React + TypeScript │
                         └──────────┬──────────┘
                                    │
                              REST API / SSE
                                    │
                                    ▼
                         ┌─────────────────────┐
                         │   FastAPI Backend   │
                         └──────────┬──────────┘
                                    │
               ┌────────────────────┼────────────────────┐
               │                    │                    │
               ▼                    ▼                    ▼
       ┌──────────────┐    ┌────────────────┐   ┌────────────────┐
       │ PostgreSQL   │    │   RabbitMQ     │   │   OpenAI API   │
       │ + pgvector   │    │   Task Queue   │   │ LLM/Embeddings │
       └──────────────┘    └───────┬────────┘   └────────────────┘
                                   │
                                   ▼
                           ┌──────────────┐
                           │Celery Worker │
                           │Resume Process│
                           └───────┬──────┘
                                   │
                                   ▼
                           ┌──────────────┐
                           │    Redis     │
                           │Task Results  │
                           └──────────────┘

RAG Pipeline

PDF Resume
    │
    ▼
Text Extraction
    │
    ▼
Section Detection & Chunking
    │
    ▼
OpenAI Embeddings
    │
    ▼
PostgreSQL + pgvector
    │
    │
    ├─────────────── Job Description
    │                       │
    ▼                       ▼
Vector Similarity Search ◄──┘
    │
    ▼
Relevant Resume Context
    │
    ▼
LLM
    │
    ▼
Personalized Interview Question

---

Tech Stack

Frontend

• Next.js
• React
• TypeScript
• Tailwind CSS

Backend

• FastAPI
• Python
• SQLAlchemy
• Alembic
• Pydantic

Database

• PostgreSQL
• pgvector

Background Processing

• Celery
• RabbitMQ
• Redis

AI

• OpenAI API
• Vector Embeddings
• Retrieval-Augmented Generation (RAG)
• Structured LLM Outputs

Infrastructure

• Docker
• Docker Compose

---

Running Locally

Prerequisites

Make sure you have installed:

• Python 3.10+
• Node.js
• Docker
• Docker Compose
• An OpenAI API key

1. Clone the repository

git clone https://github.com/123jklas/Interview-Generator-v2.git
cd Interview-Generator-v2

2. Configure environment variables

cp .env.example .env

Add your own credentials and API keys.

3. Start infrastructure

docker compose up -d

This starts the services required by the backend, including PostgreSQL, RabbitMQ, and Redis.

4. Set up the backend

python -m venv venv
source venv/bin/activate

pip install -r backend/requirements.txt

Run database migrations:

cd backend
alembic upgrade head

Start the FastAPI server:

uvicorn app.main:app --reload

5. Start the Celery worker

cd backend
source ../venv/bin/activate

python -m celery -A app.workers.celery_app worker --loglevel=info --pool=solo

6. Start the frontend

cd frontend
npm install
npm run dev

---

Application Flow

Register / Login
       ↓
Upload Resume
       ↓
Resume Processing
       ↓
Add Job Description
       ↓
Create Interview
       ↓
AI Generates Question
       ↓
Submit Answer
       ↓
AI Feedback
       ↓
Next Question

---

Security

Ready2Interview includes several basic security measures:

• JWT-based authentication
• User-scoped resume and interview access
• Protected API endpoints
• Resume ownership validation
• Environment-based secret management
• User-level filtering for vector retrieval

---

Future Improvements

Potential future improvements include:

• Production cloud deployment
• Improved authentication and token handling
• Rate limiting
• Interview completion workflows
• Detailed analytics and performance history
• Additional interview modes
• Improved RAG retrieval and ranking
• Voice-based interviews
• Automated testing and CI/CD

---

Author

Taegang Kim

Computer Science
The University of Texas at Austin

This project was designed and developed by Taegang Kim.

---

License

Copyright © 2026 Taegang Kim. All rights reserved.