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PrepAI – Intelligent Interview & Notes Assistant

PrepAI is a comprehensive AI-powered platform designed to help users prepare for interviews, generate personalized quizzes, manage study notes, and even participate in real-time mock interviews using conversational AI. It blends multiple modern technologies such as FastAPI, React, ChromaDB, PostgreSQL, Vapi, Groq, and LlamaIndex into a cohesive, production-ready system.

🚀 Live Demo

▶ Try the project here: https://huggingface.co/spaces/aki-008/prepAI

🚀 Overview

PrepAI enables users to:

  • Conduct AI-powered mock interviews with real-time voice interaction.
  • Upload PDFs and chat with notes using vector search + RAG.
  • Generate MCQ quizzes from resumes or study material.
  • Maintain persistent chat sessions, interview transcripts, and summaries.
  • Work inside a fully containerized architecture with isolated services.

This system is ideal for interview preparation platforms, ed-tech tools, or personal study automation.

🧩 Architecture

PrepAI is built with a modular full-stack architecture:

Backend (FastAPI)

  • JWT Authentication
  • Upload & process PDFs
  • ChromaDB vector store integration
  • Streaming LLM responses
  • Vapi-powered live interview assistant
  • Quiz generation using Groq/OpenAI

Frontend (React + TypeScript + Vite)

  • User onboarding (Sign In / Sign Up)
  • PDF upload, preview, rename, deletion
  • Interactive note-chat interface
  • Dashboard with metrics
  • Seamless Vapi interview client

Database Layer

  • PostgreSQL stores:

    • Users
    • PDFs
    • Chat sessions
    • Messages
    • Metadata

Vector Search Layer

  • ChromaDB stores:

    • Chunked PDF embeddings
    • User-ingested knowledge

AI Services

  • Groq/OpenAI for LLM responses
  • SentenceTransformers for embeddings
  • Vapi for real-time voice conversations
  • LlamaIndex for PDF parsing & chunking

📦 Directory Structure 

aki-008-prepai/
├── Backend/
│   ├── app/
│   ├── Dockerfile
│   └── requirements.txt
│
├── Frontend/
│   ├── src/
│   ├── Dockerfile
│   └── vite.config.ts
│
├── docker-compose.yml
└── RUN.md

⚙️ Installation & Setup

1. Clone the repository 

git clone <repo-url>
cd aki-008-prepai

2. Create your .env file

Provide:

  • PostgreSQL credentials
  • Groq API key
  • OpenAI key (if needed)
  • VAPI_PRIVATE_KEY
  • VAPI_PUBLIC_KEY
  • VAPI_ASSISTANT_ID

Example:

DATABASE_URL=postgresql+asyncpg://postgres:password@db:5432/studentdb
GROQ_API_KEY=your-key
VAPI_PRIVATE_KEY=your-key
VAPI_PUBLIC_KEY=your-key
VAPI_ASSISTANT_ID=your-assistant-id

3. Run the system (Docker) 

docker-compose up --build

Services started:

4. Dev mode (manual)

Backend:

cd Backend
python run.py

Frontend:

cd Frontend
npm install
npm run dev

ChromaDB:

chroma run --host 0.0.0.0 --port 8080 --path ./chroma_store

🧪 Key Features in Detail

1. PDF Upload & Notes Chat

  • PDFs are chunked using PyMuPDF + LlamaIndex.
  • Embeddings generated via MiniLM.
  • Chunks stored in ChromaDB.
  • Users can open chats tied to each PDF with full chat history.

2. AI Interview System

  • Dynamic prompt generation based on job-role, experience, difficulty.

  • Real-time Vapi-based interview with:

    • Emotion recognition
    • Adjustable voice
    • Adaptive follow-ups
    • Strict 5-minute flow

  • Transcripts saved automatically.

3. Quiz Generation

From resumes or notes:

  • Strict rules enforced by the SYSTEM_PROMPT
  • Always 10 MCQs with 4 options
  • JSON-structured output
  • Options + explanations

4. Authentication

  • Secure hashing using Argon2
  • JWT tokens
  • Protected routes for all user-specific actions

🛠️ Development Notes

Backend

  • Powered by FastAPI with async SQLAlchemy.
  • Auto-table creation on startup.
  • Organized into clear routers: Auth, Notes, Interview, Quiz.
  • Streaming responses for chat.

Frontend

  • Built on React + TypeScript.
  • Modern UI with Tailwind.
  • Routes include: Home, Dashboard, Notes, Interview.
  • ProtectedRoute ensures authentication.

Docker Setup

docker-compose.yml orchestrates:

  • PostgreSQL database
  • ChromaDB vector server
  • Backend (Python)
  • Frontend (Nginx)

📄 Interview Transcript Storage

During every interview:

  • All real-time transcripts are appended in Backend/transcripts/<call_id>.txt.
  • Summary is appended at end of call.

🚧 Roadmap

Future improvements:

  • User analytics dashboard
  • Multi-file knowledge merging
  • Advanced scoring for interview responses
  • Multi-voice model selector
  • Mobile-friendly front-end layout

🧑‍💻 Contributing

Feel free to open issues or submit pull requests. Contributions are welcome for both frontend and backend.

📜 License

This project is licensed under your chosen license (MIT recommended).

❤️ Thank you for using PrepAI!

If you'd like additional documentation (API reference, UML diagrams, onboarding guide), just ask!