FSSCoding
af4db45ce9
- Create global wrapper script in /usr/local/bin/rag-mini - Automatically handles virtual environment activation - Suppress virtual environment warnings when using global wrapper - Update installation scripts to install global wrapper automatically - Add comprehensive timing documentation (2-3 min fast, 5-10 min slow internet) - Add agent warnings for background process execution - Update all documentation with realistic timing expectations - Fix README commands to use correct syntax (rag-mini init -p .) Major improvements: - Users can now run 'rag-mini' from anywhere without activation - Installation creates transparent global command automatically - No more virtual environment complexity for end users - Comprehensive agent/CI/CD guidance with timeout warnings - Complete documentation consistency across all files 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
2.2 KiB
2.2 KiB
RAG System - Hybrid Mode Setup
This RAG system can operate in three modes:
🚀 Mode 1: Standard Installation (Recommended)
python3 -m venv .venv
.venv/bin/python -m pip install -r requirements.txt # 2-8 minutes
.venv/bin/python -m pip install . # ~1 minute
source .venv/bin/activate
- Size: ~123MB total (LanceDB 36MB + PyArrow 43MB + PyLance 44MB)
- Performance: Excellent hybrid embedding system
- Timing: 2-3 minutes fast internet, 5-10 minutes slow internet
🔄 Mode 2: Light Installation (Alternative)
python3 -m venv .venv
.venv/bin/python -m pip install -r requirements-light.txt # If available
.venv/bin/python -m pip install .
source .venv/bin/activate
- Size: ~426MB total (includes basic dependencies only)
- Requires: Ollama server running locally
- Use case: Minimal installations, edge devices
🛡️ Mode 3: Full Installation (Maximum Features)
python3 -m venv .venv
.venv/bin/python -m pip install -r requirements-full.txt # If available
.venv/bin/python -m pip install .
source .venv/bin/activate
- Size: ~3GB total (includes all ML fallbacks)
- Compatibility: Works anywhere, all features enabled
- Use case: Offline environments, complete feature set
🔧 Configuration
Edit .mini-rag/config.json in your project:
{
"embedding": {
"provider": "hybrid", // "hybrid", "ollama", "fallback"
"model": "nomic-embed-text:latest",
"base_url": "http://localhost:11434",
"enable_fallback": true // Set to false to disable ML fallback
}
}
📊 Status Check
from mini_rag.ollama_embeddings import OllamaEmbedder
embedder = OllamaEmbedder()
status = embedder.get_status()
print(f"Mode: {status['mode']}")
print(f"Ollama: {'✅' if status['ollama_available'] else '❌'}")
print(f"ML Fallback: {'✅' if status['fallback_available'] else '❌'}")
🎯 Automatic Behavior
- Try Ollama first - fastest and most efficient
- Fall back to ML - if Ollama unavailable and ML dependencies installed
- Use hash fallback - deterministic embeddings as last resort
The system automatically detects what's available and uses the best option!