🤖 AI Mock Interview Platform

An AI-powered mock interview platform that simulates real technical interviews using Retrieval-Augmented Generation (RAG), LangChain, Gemini, Whisper, FastAPI, and the MERN Stack.

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📖 Overview

Preparing for technical interviews often means solving generic questions that do not reflect real interview experiences.

This platform solves that problem by using Retrieval-Augmented Generation (RAG) to retrieve company-specific interview experiences and combines them with Google Gemini to generate realistic interview questions.

Candidates can:

• 🎯 Attempt AI-generated mock interviews
• 🎤 Answer using voice or text
• 📝 Get speech converted into text using Whisper
• 📊 Receive AI-based feedback and scoring
• 📚 Practice interviews tailored to companies and roles

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✨ Features

👤 User Features

• Secure Authentication
• Google OAuth Login
• Create Unlimited Mock Interviews
• Text-Based Answers
• Voice-Based Answers
• AI Evaluation & Feedback
• Interview History
• Responsive UI

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🧠 AI Features

• Retrieval-Augmented Generation (RAG)
• LangChain Pipeline
• ChromaDB Vector Database
• Google Gemini LLM
• Whisper Speech-to-Text
• Company-specific Interview Questions
• Context-aware Question Generation
• AI Feedback & Scoring

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🏗️ System Architecture

                +---------------------+
                |      React UI       |
                +----------+----------+
                           |
                    REST APIs
                           |
                +----------v----------+
                | Node.js + Express   |
                | Authentication      |
                | Sessions            |
                | Database            |
                +----------+----------+
                           |
                    HTTP Requests
                           |
                +----------v----------+
                |    FastAPI AI       |
                |---------------------|
                | LangChain           |
                | Gemini              |
                | ChromaDB            |
                | Whisper             |
                +----------+----------+
                           |
                   Vector Retrieval
                           |
                Company Interview Data

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🧩 Tech Stack

Frontend

• React.js
• Tailwind CSS
• React Router
• Axios
• Socket.io Client

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Backend

• Node.js
• Express.js
• JWT Authentication
• Google OAuth
• Socket.io
• MongoDB
• Mongoose

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AI Service

• FastAPI
• LangChain
• Google Gemini
• ChromaDB
• Whisper
• Sentence Transformers

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Database

• MongoDB
• ChromaDB (Vector Database)

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📂 Project Structure

AI-MOCK-INTERVIEW/
│
├── frontend/
│   ├── public/
│   ├── src/
│   │   ├── assets/
│   │   ├── components/
│   │   ├── pages/
│   │   ├── context/
│   │   ├── hooks/
│   │   ├── services/
│   │   ├── utils/
│   │   └── App.jsx
│   └── package.json
│
├── backend/
│   ├── config/
│   ├── controllers/
│   ├── middleware/
│   ├── models/
│   ├── routes/
│   ├── sockets/
│   ├── utils/
│   ├── server.js
│   └── package.json
│
├── ai-service/
│   ├── rag/
│   ├── vectorstore/
│   ├── embeddings/
│   ├── prompts/
│   ├── services/
│   ├── api/
│   ├── main.py
│   └── requirements.txt
│
├── screenshots/
├── README.md
└── .env.example

Note: Folder names inside the AI service may vary depending on your implementation. Update them if your repository uses different names.

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🔄 Application Flow

User
   │
   ▼
Login / Register
   │
   ▼
Select Company & Role
   │
   ▼
Node Backend
   │
   ▼
FastAPI AI Service
   │
   ▼
Retrieve Context using RAG
   │
   ▼
Gemini Generates Questions
   │
   ▼
User Answers (Voice/Text)
   │
   ▼
Whisper Converts Speech → Text
   │
   ▼
Gemini Evaluates Response
   │
   ▼
Feedback Stored in MongoDB

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🧠 How RAG Works

Company Interview Dataset
            │
            ▼
Text Chunking
            │
            ▼
Embeddings
            │
            ▼
ChromaDB
            │
            ▼
Relevant Context Retrieval
            │
            ▼
Gemini Prompt
            │
            ▼
Interview Questions

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🚀 Getting Started

1. Clone Repository

git clone https://github.com/yourusername/AI-Mock-Interview.git

cd AI-Mock-Interview

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2. Install Dependencies

Frontend

cd frontend
npm install

Backend

cd backend
npm install

AI Service

cd ai-service
pip install -r requirements.txt

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🔐 Environment Variables

Backend

PORT=

MONGO_URI=

JWT_SECRET=

GOOGLE_CLIENT_ID=

GOOGLE_CLIENT_SECRET=

AI_SERVICE_URL=

---

AI Service

GEMINI_API_KEY=

CHROMA_DB_PATH=

EMBEDDING_MODEL=

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▶️ Run the Project

Backend

npm run dev

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Frontend

npm run dev

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AI Service

uvicorn main:app --reload

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📸 Screenshots

Add screenshots of your application here.

screenshots/

├── Home.png

├── Login.png

├── Dashboard.png

├── Interview.png

└── Feedback.png

Example:

## Home

![Home](screenshots/Home.png)

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🎯 Future Improvements

• Resume Parsing
• Adaptive Difficulty
• Coding Interviews
• Video Interview Support
• AI Follow-up Questions
• Performance Analytics Dashboard
• Personalized Learning Roadmap
• Multi-language Interviews
• Company-wise Interview Analytics

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🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a new feature branch

git checkout -b feature/new-feature

3. Commit your changes

git commit -m "Add new feature"

4. Push your branch

git push origin feature/new-feature

5. Open a Pull Request