AI Interviewer

A LangGraph-based application for conducting technical interviews with AI. This system helps candidates practice for technical interviews by simulating a real interview experience with voice capabilities.

Features

• AI-powered technical interviewer
• Voice interface (speech-to-text and text-to-speech)
• Chat-based interface
• Session persistence with MongoDB
• Coding challenges with real-time feedback
• Interview progress tracking and history

Architecture

The application consists of:

1. A backend built with LangGraph, FastAPI, and MongoDB
2. A frontend built with React, Chakra UI, and React Router

Installation

Prerequisites

• Python 3.9+
• Node.js 14+
• MongoDB (optional, can be configured to use memory storage)

Backend Setup

1. Clone the repository

   git clone https://github.com/yourusername/ai-interviewer.git
   cd ai-interviewer
   

2. Create and activate a virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Set up environment variables

   • Create a .env file in the root directory
   • Add the following variables:

     OPENAI_API_KEY=your_openai_api_key
     MONGODB_URI=your_mongodb_uri  # Optional
     

Frontend Setup

1. Install dependencies

   cd frontend
   npm install
   

2. Build the frontend

   npm run build
   

Running the Application

Once you've set up the backend and built the frontend, you can run the full application:

python -m ai_interviewer.server

The application will be available at:

• Web interface: http://localhost:8000
• API documentation: http://localhost:8000/docs

Development

Backend Development

To run only the backend API server during development:

python -m ai_interviewer.server

Frontend Development

To run the frontend in development mode with hot reloading:

cd frontend
npm start

This will start the React development server at http://localhost:3000. API requests will be proxied to the backend at http://localhost:8000.

Usage

1. Open the application in your browser
2. Start a new interview or continue a previous session
3. Respond to the AI interviewer's questions via text or voice
4. Complete the interview process including coding challenges
5. Review your performance and feedback

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Dynamic Question Generation

The AI Interviewer platform now includes enhanced dynamic question generation with the following features:

• Context-Aware Questions: Questions adapt based on previous candidate responses, creating a more natural conversation flow
• Difficulty Level Control: Specify beginner, intermediate, or advanced difficulty levels to match candidate experience
• Skill-Specific Focus: Target specific skill areas relevant to the job role (e.g., Python, React, Data Structures)
• Job Role Alignment: Questions automatically align with job role requirements
• Follow-Up Generation: Create targeted follow-up questions based on specific candidate answers
• Response Analysis: Analyze candidate responses to identify strengths, weaknesses and follow-up areas

These question generation features are available both through the LLM agent workflow and directly via API endpoints:

# Generate a question
curl -X POST http://localhost:8000/api/questions/generate \
  -H "Content-Type: application/json" \
  -d '{
    "job_role": "Frontend Developer",
    "skill_areas": ["JavaScript", "React", "CSS"],
    "difficulty_level": "intermediate"
  }'

# Analyze a response
curl -X POST http://localhost:8000/api/questions/analyze-response \
  -H "Content-Type: application/json" \
  -d '{
    "question": "Explain the virtual DOM in React and why it is important.",
    "response": "The virtual DOM is React's way of improving performance...",
    "job_role": "Frontend Developer",
    "skill_areas": ["React", "JavaScript"]
  }'