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INSTALLATION_GUIDE.md

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🚀 NexusAI Installation Guide

Quick Start (Minimal Installation)

For basic functionality with simplified RAG/LoRA systems:

# Install core dependencies only
pip install Flask Werkzeug python-dotenv requests flask-cors groq

Full Installation (All Features)

For complete functionality with advanced ML features:

  1. Edit requirements.txt - Uncomment the sections you need:

    • AI/ML Advanced Features (for full RAG/LoRA)
    • Document Processing (for file uploads)
    • Vector Databases (for advanced search)
    • LangChain Ecosystem (for advanced RAG)

  2. Install all dependencies:

    pip install -r requirements.txt

Production Installation

For production deployment:

  1. Edit requirements.txt - Uncomment these sections:

    • Database (for persistent storage)
    • Security & Production
    • Production Server
    • Caching & Performance
    • Monitoring & Logging

  2. Install production dependencies:

    pip install -r requirements.txt

Installation Profiles

🏃‍♂️ Minimal Profile (Fastest setup)

  • Core Flask app
  • Basic AI chat functionality
  • Simplified RAG/LoRA systems
  • Size: ~50MB

🧠 Full ML Profile (Complete features)

  • All AI/ML capabilities
  • Advanced RAG with vector search
  • Full LoRA fine-tuning
  • Document processing
  • Size: ~2-3GB

🏭 Production Profile (Enterprise ready)

  • Database support
  • Caching and performance optimization
  • Security features
  • Monitoring and logging
  • Production WSGI server
  • Size: ~100-200MB (without ML)

Environment Setup

  1. Create virtual environment:

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

  2. Set up environment variables:

    cp .env.example .env
# Edit .env with your API keys

  3. Install dependencies (choose your profile above)

  4. Run the application:

    ./run-local.sh
# OR manually: cd backend && python app.py

Troubleshooting

Common Issues:

PyTorch Installation Issues:

# For CPU-only (smaller, faster install)
pip install torch --index-url https://download.pytorch.org/whl/cpu

# For GPU support (if you have CUDA)
pip install torch --index-url https://download.pytorch.org/whl/cu118

Memory Issues with Large Models:

  • Use minimal installation for development
  • Consider cloud deployment for full ML features

Dependency Conflicts:

# Create fresh environment
pip freeze > old_requirements.txt
deactivate
rm -rf venv
python -m venv venv
source venv/bin/activate
# Install step by step

Docker Installation (Alternative)

For containerized deployment:

# Build and run with Docker
docker-compose up --build

This uses the Dockerfile which automatically handles all dependencies.

Verification

Test your installation:

python -c "import flask, groq; print('✅ Core dependencies installed')"

# For full ML installation:
python -c "import torch, transformers; print('✅ ML dependencies installed')"

Support

  • Minimal issues: Check Flask and Groq API setup
  • ML issues: Verify PyTorch and transformers installation
  • Production issues: Check database and server configuration

Choose the installation profile that matches your needs and system resources! 🎯