Interview AI Backend

A FastAPI-based backend service for analyzing interview transcripts using LLM. The service provides different levels of analysis (basic, pro, and advanced) and processes them asynchronously using Celery.

Project Structure

interviewai-be/
├── app/
│   ├── api/                    # API endpoints
│   │   ├── v1/                # API version 1
│   │   │   ├── endpoints/     # Individual endpoint modules
│   │   │   │   ├── analyze.py # Analysis endpoints
│   │   │   │   └── hello.py   # Hello world endpoint
│   │   │   └── router.py      # API router configuration
│   │   ├── core/                   # Core functionality
│   │   │   └── config.py          # Application settings
│   │   ├── services/              # Business logic
│   │   │   └── analysis.py        # Analysis service
│   │   └── tasks/                 # Celery tasks
│   │       └── celery_tasks.py    # Task definitions
│   ├── tests/                     # Test files
│   ├── .env                       # Environment variables
│   ├── docker-compose.yml         # Docker services configuration
│   ├── Dockerfile                 # Docker image configuration
│   └── requirements.txt           # Python dependencies

Features

• Asynchronous processing of interview analysis
• Multiple analysis levels (basic, pro, advanced)
• Task status tracking
• Dockerized deployment
• Redis for task queue and results backend
• Celery for task management

Setup

1. Clone the repository:

git clone <repository-url>
cd interviewai-be

2. Create a .env file with the following variables:

APP_HOST=0.0.0.0
APP_PORT=8000
DEBUG=True
REDIS_HOST=redis
REDIS_PORT=6379
REDIS_DB=0
LOG_LEVEL=INFO

3. Run with Docker Compose:

docker compose up --build

API Endpoints

Hello World

• GET /api/v1/hello
• Returns a welcome page

Analysis

• POST /api/v1/analyze

  • Request body:

    {
      "transcript": "Interview transcript text...",
      "userId": "user123",
      "requestedReports": ["basic", "pro", "advanced"],
      "metadata": {
        "interviewId": "interview456",
        "timestamp": "2023-10-01T12:00:00Z"
      }
    }

  • Returns task IDs for tracking

• GET /api/v1/analyze/status/{taskId}

  • Returns task status and result if completed

Adding New Features

1. Adding a New Endpoint

1. Create a new file in app/api/v1/endpoints/:

from fastapi import APIRouter

router = APIRouter()

@router.get("/")
async def new_endpoint():
    return {"message": "New endpoint"}

2. Add the router to app/api/v1/router.py:

from app.api.v1.endpoints import new_endpoint

api_router.include_router(new_endpoint.router, prefix="/new", tags=["new"])

2. Adding a New Service

1. Create a new file in app/services/:

def new_service():
    # Business logic here
    return result

2. Import and use in your endpoints or tasks:

from app.services.new_service import new_service

3. Adding a New Celery Task

1. Add the task to app/tasks/celery_tasks.py:

@celery.task
def new_task():
    # Task logic here
    return result

2. Import and use in your endpoints:

from app.tasks.celery_tasks import new_task

Development

Running Tests

# Add test commands here when tests are implemented

Code Style

• Follow PEP 8 guidelines
• Use type hints
• Document functions and classes

Adding Dependencies

1. Add to requirements.txt
2. Rebuild Docker containers:

docker compose up --build

Deployment

Production Considerations

1. Set DEBUG=False in .env
2. Configure proper CORS settings
3. Set up proper logging
4. Configure proper security measures

Scaling

• The application is designed to scale horizontally
• Celery workers can be scaled independently
• Redis can be configured for high availability

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request