System Requirements

Overview

This page outlines all system requirements, dependencies, and hardware specifications needed to run the CS Interview Assistant platform in both development and production environments.

Operating System

Linux: Ubuntu 20.04+ or any modern distribution
macOS: 10.15 (Catalina) or later
Windows: Windows 10/11 with WSL2 recommended for development

Runtime Environments

Python

version

required

Version 3.8 or higher required for backend servicesRecommended: Python 3.10+ for optimal performance with async features

Node.js

version

required

Version 14.x or higher required for frontend build toolsRecommended: Node.js 18.x LTS or 20.x LTS

npm

version

required

Version 6.x or higher required (bundled with Node.js)Recommended: npm 9.x or later

Backend Dependencies

All backend dependencies are specified in requirements.txt. Install with:

pip install -r requirements.txt

Web Framework & Server

Core Flask framework and extensions for the API server:

flask (3.1.3) - Core web framework
flask-cors (6.0.2) - Cross-Origin Resource Sharing support
flask-sqlalchemy (3.1.1) - Database ORM integration
flask-login (0.6.3) - User session management
flask-socketio (5.6.1) - WebSocket support for real-time features
eventlet (0.40.4) - Concurrent networking library
werkzeug (3.1.6) - WSGI utilities

Authentication & Security

Libraries for user authentication and data encryption:

PyJWT (2.11.0) - JSON Web Token implementation
cryptography (46.0.5) - Cryptographic primitives

AI & Large Language Models

Mistral AI integration and supporting libraries:

mistralai (1.12.4) - Official Mistral AI Python client
openai (1.65.0) - OpenAI API compatibility layer
tenacity (9.1.4) - Retry logic for API calls
pydantic (2.12.5) - Data validation using Python type hints

The MISTRAL_API_KEY environment variable must be set for AI features to work. See Environment Variables for details.

Machine Learning & NLP

Libraries for vector embeddings, semantic search, and text processing:

numpy (2.4.2) - Numerical computing
faiss-cpu (1.13.2) - Vector similarity search and clustering
sentence-transformers (5.2.3) - State-of-the-art sentence embeddings
scikit-learn (1.8.0) - Machine learning utilities
torch (2.10.0) - PyTorch deep learning framework
spacy (3.8.11) - Industrial-strength NLP
langchain-text-splitters (1.1.1) - Text chunking for RAG

torch and sentence-transformers are large packages (1-2GB combined). Ensure sufficient disk space during installation.

Audio Processing & Speech-to-Text

Libraries for audio analysis and transcription:

librosa (0.11.0) - Audio analysis and feature extraction
pydub (0.25.1) - Audio file manipulation
faster-whisper (1.2.1) - Fast Whisper implementation for transcription
soundfile (0.13.1) - Audio file I/O

Document Processing

Libraries for resume and document parsing:

PyPDF2 (3.0.1) - PDF document processing
python-docx (1.2.0) - Microsoft Word document processing

Networking & Utilities

requests (2.32.5) - HTTP library
websocket-client (1.9.0) - WebSocket client
websockets (16.0) - WebSocket server implementation
python-dotenv (1.2.1) - Environment variable management
unidecode (1.4.0) - Unicode text transliteration
regex (2026.2.19) - Advanced regular expressions
tqdm (4.67.3) - Progress bars
colorama (0.4.6) - Terminal color output

Frontend Dependencies

All frontend dependencies are specified in frontend/package.json. Install with:

cd frontend
npm install

Core Framework

react (19.2.0) - UI library
react-dom (19.2.0) - React DOM renderer
react-router-dom (7.9.4) - Client-side routing
react-scripts (5.0.1) - Create React App build tools

UI Components & Visualization

@react-three/fiber (9.5.0) - React renderer for Three.js
@react-three/drei (10.7.7) - Useful helpers for react-three-fiber
three (0.183.1) - 3D graphics library
lucide-react (0.575.0) - Icon library

Real-time Communication

socket.io-client (4.8.3) - WebSocket client for real-time features
axios (1.12.2) - HTTP client

Content Rendering

react-markdown (10.1.0) - Markdown renderer
remark-gfm (4.0.1) - GitHub Flavored Markdown support
react-syntax-highlighter (16.1.1) - Code syntax highlighting

Testing

@testing-library/react (16.3.0) - React testing utilities
@testing-library/jest-dom (6.9.1) - Custom Jest matchers
@testing-library/user-event (13.5.0) - User interaction simulation

Hardware Requirements

Minimum Requirements (Development)

CPU

hardware

Dual-core processor (2.0 GHz or higher)

RAM

hardware

8 GB minimum

4 GB for Python runtime and ML models
2 GB for FAISS indices (scales with knowledge base size)
2 GB for frontend build tools and Node.js

Storage

hardware

10 GB minimum free space

3 GB for Python dependencies (PyTorch, transformers)
2 GB for Node.js dependencies
2 GB for FAISS indices and knowledge base
1 GB for database and user uploads
2 GB for temporary files and build artifacts

Recommended Requirements (Production)

CPU

hardware

Quad-core processor (3.0 GHz or higher)

Multi-core CPU recommended for concurrent user sessions
SSE4.2 instruction set for optimized FAISS operations

RAM

hardware

16 GB or higher

6 GB for Python runtime and ML models
4 GB for FAISS indices (allows for larger knowledge bases)
2 GB for concurrent WebSocket connections
4 GB for OS and other services

Storage

hardware

50 GB+ SSD storage

SSD strongly recommended for FAISS index performance
Additional space scales with number of user uploads and sessions

Optional: GPU Acceleration

GPU

hardware

NVIDIA GPU with CUDA supportFor significantly faster FAISS operations, install faiss-gpu instead of faiss-cpu:

pip uninstall faiss-cpu
pip install faiss-gpu

Requirements:

CUDA 11.0 or higher
4+ GB VRAM
NVIDIA driver 450.x or higher

GPU acceleration is optional but can improve RAG query performance by 5-10x for large knowledge bases.

Development vs Production

Development Environment

For local development, minimum requirements are sufficient:

Use SQLite database (default configuration)
Use faiss-cpu for vector search
Single-threaded Flask development server
React development server with hot reload

Production Environment

Production deployments should meet recommended requirements plus:

PostgreSQL database instead of SQLite
Gunicorn or uWSGI as WSGI server
Nginx reverse proxy for static files and load balancing
Redis for session management (optional)
Process manager like systemd or supervisord

See Production Setup for detailed production configuration.

Verification

Verify Python Installation

python --version  # Should be 3.8+
pip --version

Verify Node.js Installation

node --version  # Should be 14+
npm --version

Verify Dependencies Installation

# Backend
pip list | grep flask
pip list | grep faiss
pip list | grep mistralai

# Frontend
cd frontend
npm list react
npm list socket.io-client

Contributing

Knowledge Base

Deployment

System Requirements

Overview