AI Career Mentor - Backend

Backend service for the AI Career Mentor application built using Django and Django REST Framework. It processes LinkedIn profile data, communicates with AI models, and returns personalized career guidance.

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🚀 Features

• Django REST API
• AI-powered Career Guidance
• LinkedIn Profile Processing
• RESTful Architecture
• Environment Variable Support
• Modular Project Structure

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🛠️ Tech Stack

• Python
• Django
• Django REST Framework
• SQLite
• FastEmbed
• Groq Llama 3.3 70B
• Cognee (Experimental)

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📂 Project Structure

backend/
│
├── careermentor/
├── mentor/
├── media/
├── manage.py
├── requirements.txt
└── README.md

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⚙️ Installation

Clone the repository

git clone <repository-url>

Navigate to backend

cd backend

Create virtual environment

python -m venv venv

Activate virtual environment

Windows

venv\Scripts\activate

Linux / macOS

source venv/bin/activate

Install dependencies

pip install -r requirements.txt

Run migrations

python manage.py migrate

Run the development server

python manage.py runserver

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🔑 Environment Variables

Create a .env file inside the backend folder.

Example:

GROQ_API_KEY=your_api_key

LLM_PROVIDER=custom
LLM_MODEL=groq/llama-3.3-70b-versatile

EMBEDDING_PROVIDER=fastembed
EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2

EMBEDDING_DIMENSIONS=384
EMBEDDING_MAX_TOKENS=256

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📡 API

The backend exposes REST APIs that communicate with the frontend to generate AI-powered career recommendations.

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📌 Future Improvements

• JWT Authentication
• PostgreSQL Support
• Docker Deployment
• Cloud Storage
• Redis Caching
• Automated Testing

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👨‍💻 Author

Simran Sajid