BobAi
4166d0a362
🎯 Complete transformation from 5.9GB bloated system to 70MB optimized solution ✨ Key Features: - Hybrid embedding system (Ollama + ML fallback + hash backup) - Intelligent chunking with language-aware parsing - Semantic + BM25 hybrid search with rich context - Zero-config portable design with graceful degradation - Beautiful TUI for beginners + powerful CLI for experts - Comprehensive documentation with 8+ Mermaid diagrams - Professional animated demo (183KB optimized GIF) 🏗️ Architecture Highlights: - LanceDB vector storage with streaming indexing - Smart file tracking (size/mtime) to avoid expensive rehashing - Progressive chunking: Markdown headers → Python functions → fixed-size - Quality filtering: 200+ chars, 20+ words, 30% alphanumeric content - Concurrent batch processing with error recovery 📦 Package Contents: - Core engine: claude_rag/ (11 modules, 2,847 lines) - Entry points: rag-mini (unified), rag-tui (beginner interface) - Documentation: README + 6 guides with visual diagrams - Assets: 3D icon, optimized demo GIF, recording tools - Tests: 8 comprehensive integration and validation tests - Examples: Usage patterns, config templates, dependency analysis 🎥 Demo System: - Scripted demonstration showing 12 files → 58 chunks indexing - Semantic search with multi-line result previews - Complete workflow from TUI startup to CLI mastery - Professional recording pipeline with asciinema + GIF conversion 🛡️ Security & Quality: - Complete .gitignore with personal data protection - Dependency optimization (removed python-dotenv) - Code quality validation and educational test suite - Agent-reviewed architecture and documentation Ready for production use - copy folder, run ./rag-mini, start searching\!
638 lines
20 KiB
Bash
Executable File
638 lines
20 KiB
Bash
Executable File
#!/bin/bash
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# FSS-Mini-RAG Installation Script
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# Interactive installer that sets up Python environment and dependencies
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set -e # Exit on any error
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# Colors for output
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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BLUE='\033[0;34m'
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CYAN='\033[0;36m'
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BOLD='\033[1m'
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NC='\033[0m' # No Color
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# Get script directory
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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# Print colored output
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print_header() {
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echo -e "\n${CYAN}${BOLD}=== $1 ===${NC}"
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}
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print_success() {
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echo -e "${GREEN}✅ $1${NC}"
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}
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print_warning() {
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echo -e "${YELLOW}⚠️ $1${NC}"
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}
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print_error() {
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echo -e "${RED}❌ $1${NC}"
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}
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print_info() {
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echo -e "${BLUE}ℹ️ $1${NC}"
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}
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# Check if command exists
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command_exists() {
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command -v "$1" >/dev/null 2>&1
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}
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# Check Python version
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check_python() {
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print_header "Checking Python Installation"
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# Check for python3 first, then python
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local python_cmd=""
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if command_exists python3; then
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python_cmd="python3"
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elif command_exists python; then
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python_cmd="python"
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else
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print_error "Python not found!"
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echo -e "${YELLOW}Please install Python 3.8+ from:${NC}"
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echo " • https://python.org/downloads"
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echo " • Or use your system package manager:"
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echo " - Ubuntu/Debian: sudo apt install python3 python3-pip python3-venv"
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echo " - macOS: brew install python"
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echo " - Windows: Download from python.org"
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echo ""
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echo -e "${CYAN}After installing Python, run this script again.${NC}"
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exit 1
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fi
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# Check Python version
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local python_version=$($python_cmd -c "import sys; print('.'.join(map(str, sys.version_info[:2])))")
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local major=$(echo $python_version | cut -d. -f1)
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local minor=$(echo $python_version | cut -d. -f2)
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if [ "$major" -lt 3 ] || ([ "$major" -eq 3 ] && [ "$minor" -lt 8 ]); then
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print_error "Python $python_version found, but 3.8+ required"
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echo "Please upgrade Python to 3.8 or higher."
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exit 1
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fi
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print_success "Found Python $python_version ($python_cmd)"
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export PYTHON_CMD="$python_cmd"
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}
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# Check if virtual environment exists
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check_venv() {
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if [ -d "$SCRIPT_DIR/.venv" ]; then
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print_info "Virtual environment already exists at $SCRIPT_DIR/.venv"
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echo -n "Recreate it? (y/N): "
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read -r recreate
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if [[ $recreate =~ ^[Yy]$ ]]; then
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print_info "Removing existing virtual environment..."
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rm -rf "$SCRIPT_DIR/.venv"
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return 1 # Needs creation
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else
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return 0 # Use existing
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fi
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else
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return 1 # Needs creation
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fi
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}
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# Create virtual environment
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create_venv() {
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print_header "Creating Python Virtual Environment"
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if ! check_venv; then
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print_info "Creating virtual environment at $SCRIPT_DIR/.venv"
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$PYTHON_CMD -m venv "$SCRIPT_DIR/.venv"
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if [ $? -ne 0 ]; then
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print_error "Failed to create virtual environment"
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echo "This might be because python3-venv is not installed."
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echo "Try: sudo apt install python3-venv (Ubuntu/Debian)"
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exit 1
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fi
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print_success "Virtual environment created"
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else
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print_success "Using existing virtual environment"
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fi
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# Activate virtual environment
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source "$SCRIPT_DIR/.venv/bin/activate"
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print_success "Virtual environment activated"
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# Upgrade pip
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print_info "Upgrading pip..."
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pip install --upgrade pip >/dev/null 2>&1
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}
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# Check Ollama installation
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check_ollama() {
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print_header "Checking Ollama (AI Model Server)"
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if command_exists ollama; then
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print_success "Ollama is installed"
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# Check if Ollama is running
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if curl -s http://localhost:11434/api/version >/dev/null 2>&1; then
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print_success "Ollama server is running"
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return 0
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else
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print_warning "Ollama is installed but not running"
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echo -n "Start Ollama now? (Y/n): "
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read -r start_ollama
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if [[ ! $start_ollama =~ ^[Nn]$ ]]; then
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print_info "Starting Ollama server..."
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ollama serve &
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sleep 3
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if curl -s http://localhost:11434/api/version >/dev/null 2>&1; then
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print_success "Ollama server started"
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return 0
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else
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print_warning "Failed to start Ollama automatically"
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echo "Please start Ollama manually: ollama serve"
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return 1
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fi
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else
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return 1
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fi
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fi
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else
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print_warning "Ollama not found"
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echo ""
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echo -e "${CYAN}Ollama provides the best embedding quality and performance.${NC}"
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echo -e "${YELLOW}To install Ollama:${NC}"
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echo " 1. Visit: https://ollama.ai/download"
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echo " 2. Download and install for your system"
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echo " 3. Run: ollama serve"
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echo " 4. Re-run this installer"
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echo ""
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echo -e "${BLUE}Alternative: Use ML fallback (requires more disk space)${NC}"
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echo ""
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echo -n "Continue without Ollama? (y/N): "
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read -r continue_without
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if [[ $continue_without =~ ^[Yy]$ ]]; then
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return 1
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else
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print_info "Install Ollama first, then re-run this script"
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exit 0
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fi
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fi
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}
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# Setup Ollama model based on configuration
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setup_ollama_model() {
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# Skip if custom config says to skip
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if [ "$CUSTOM_OLLAMA_MODEL" = "skip" ]; then
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print_info "Skipping Ollama model setup (custom configuration)"
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return 1
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fi
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print_header "Ollama Model Setup"
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print_info "Checking available Ollama models..."
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# Get list of installed models
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local available_models=$(ollama list 2>/dev/null | grep -v "NAME" | awk '{print $1}' | grep -v "^$")
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if echo "$available_models" | grep -q "nomic-embed-text"; then
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print_success "nomic-embed-text model already installed"
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local model_info=$(ollama list | grep "nomic-embed-text")
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echo -e "${BLUE}• $model_info${NC}"
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return 0
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fi
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if [ -n "$available_models" ]; then
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print_info "Other Ollama models found:"
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echo "$available_models" | sed 's/^/ • /'
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echo ""
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fi
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# For custom installations, we already asked. For auto installations, ask now
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local should_download="$CUSTOM_OLLAMA_MODEL"
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if [ -z "$should_download" ] || [ "$should_download" = "auto" ]; then
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echo -e "${CYAN}Model: nomic-embed-text (~270MB)${NC}"
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echo " • Purpose: High-quality semantic embeddings"
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echo " • Alternative: System will use ML/hash fallbacks"
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echo ""
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echo -n "Download model? [y/N]: "
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read -r download_model
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should_download=$([ "$download_model" = "y" ] && echo "download" || echo "skip")
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fi
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if [ "$should_download" != "download" ]; then
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print_info "Skipping model download"
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echo " Install later: ollama pull nomic-embed-text"
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return 1
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fi
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# Test connectivity and download
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print_info "Testing Ollama connection..."
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if ! curl -s --connect-timeout 5 http://localhost:11434/api/version >/dev/null; then
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print_error "Cannot connect to Ollama server"
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echo " Ensure Ollama is running: ollama serve"
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echo " Then install manually: ollama pull nomic-embed-text"
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return 1
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fi
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print_info "Downloading nomic-embed-text..."
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echo -e "${BLUE} Press Ctrl+C to cancel if needed${NC}"
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if ollama pull nomic-embed-text; then
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print_success "Model ready"
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return 0
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else
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print_warning "Download failed - will use fallback embeddings"
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return 1
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fi
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}
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# Get installation preferences with smart defaults
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get_installation_preferences() {
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print_header "Installation Configuration"
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echo -e "${CYAN}FSS-Mini-RAG can run with different embedding backends:${NC}"
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echo ""
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echo -e "${GREEN}• Ollama${NC} (recommended) - Best quality, local AI server"
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echo -e "${YELLOW}• ML Fallback${NC} - Offline transformers, larger but always works"
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echo -e "${BLUE}• Hash-based${NC} - Lightweight fallback, basic similarity"
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echo ""
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# Smart recommendation based on detected setup
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local recommended=""
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if [ "$ollama_available" = true ]; then
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recommended="light (Ollama detected)"
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echo -e "${GREEN}✓ Ollama detected - light installation recommended${NC}"
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else
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recommended="full (no Ollama)"
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echo -e "${YELLOW}⚠ No Ollama - full installation recommended for better quality${NC}"
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fi
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echo ""
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echo -e "${BOLD}Installation options:${NC}"
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echo -e "${GREEN}L) Light${NC} - Ollama + basic deps (~50MB)"
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echo -e "${YELLOW}F) Full${NC} - Light + ML fallback (~2-3GB)"
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echo -e "${BLUE}C) Custom${NC} - Configure individual components"
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echo ""
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while true; do
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echo -n "Choose [L/F/C] or Enter for recommended ($recommended): "
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read -r choice
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# Default to recommendation if empty
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if [ -z "$choice" ]; then
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if [ "$ollama_available" = true ]; then
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choice="L"
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else
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choice="F"
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fi
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fi
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case "${choice^^}" in
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L)
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export INSTALL_TYPE="light"
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echo -e "${GREEN}Selected: Light installation${NC}"
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break
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;;
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F)
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export INSTALL_TYPE="full"
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echo -e "${YELLOW}Selected: Full installation${NC}"
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break
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;;
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C)
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configure_custom_installation
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break
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;;
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*)
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print_warning "Please choose L, F, C, or press Enter for default"
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;;
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esac
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done
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}
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# Custom installation configuration
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configure_custom_installation() {
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print_header "Custom Installation Configuration"
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echo -e "${CYAN}Configure each component individually:${NC}"
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echo ""
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# Base dependencies (always required)
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echo -e "${GREEN}✓ Base dependencies${NC} (lancedb, pandas, numpy, etc.) - Required"
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# Ollama model
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local ollama_model="skip"
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if [ "$ollama_available" = true ]; then
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echo ""
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echo -e "${BOLD}Ollama embedding model:${NC}"
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echo " • nomic-embed-text (~270MB) - Best quality embeddings"
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echo -n "Download Ollama model? [y/N]: "
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read -r download_ollama
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if [[ $download_ollama =~ ^[Yy]$ ]]; then
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ollama_model="download"
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fi
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fi
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# ML dependencies
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echo ""
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echo -e "${BOLD}ML fallback system:${NC}"
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echo " • PyTorch + transformers (~2-3GB) - Works without Ollama"
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echo " • Useful for: Offline use, server deployments, CI/CD"
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echo -n "Include ML dependencies? [y/N]: "
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read -r include_ml
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# Pre-download models
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local predownload_ml="skip"
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if [[ $include_ml =~ ^[Yy]$ ]]; then
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echo ""
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echo -e "${BOLD}Pre-download ML models:${NC}"
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echo " • sentence-transformers model (~80MB)"
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echo " • Skip: Models download automatically when first used"
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echo -n "Pre-download now? [y/N]: "
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read -r predownload
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if [[ $predownload =~ ^[Yy]$ ]]; then
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predownload_ml="download"
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fi
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fi
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# Set configuration
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if [[ $include_ml =~ ^[Yy]$ ]]; then
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export INSTALL_TYPE="full"
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else
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export INSTALL_TYPE="light"
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fi
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export CUSTOM_OLLAMA_MODEL="$ollama_model"
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export CUSTOM_ML_PREDOWNLOAD="$predownload_ml"
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echo ""
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echo -e "${GREEN}Custom configuration set:${NC}"
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echo " • Base deps: ✓"
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echo " • Ollama model: $ollama_model"
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echo " • ML deps: $([ "$INSTALL_TYPE" = "full" ] && echo "✓" || echo "skip")"
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echo " • ML predownload: $predownload_ml"
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}
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# Install dependencies with progress
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install_dependencies() {
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print_header "Installing Python Dependencies"
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if [ "$INSTALL_TYPE" = "light" ]; then
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print_info "Installing core dependencies (~50MB)..."
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echo -e "${BLUE} Installing: lancedb, pandas, numpy, PyYAML, etc.${NC}"
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if pip install -r "$SCRIPT_DIR/requirements.txt" --quiet; then
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print_success "Dependencies installed"
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else
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print_error "Failed to install dependencies"
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echo "Try: pip install -r requirements.txt"
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exit 1
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fi
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else
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print_info "Installing full dependencies (~2-3GB)..."
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echo -e "${YELLOW} This includes PyTorch and transformers - will take several minutes${NC}"
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echo -e "${BLUE} Progress will be shown...${NC}"
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if pip install -r "$SCRIPT_DIR/requirements-full.txt"; then
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print_success "All dependencies installed"
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else
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print_error "Failed to install dependencies"
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echo "Try: pip install -r requirements-full.txt"
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exit 1
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fi
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fi
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print_info "Verifying installation..."
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if python3 -c "import lancedb, pandas, numpy" 2>/dev/null; then
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print_success "Core packages verified"
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else
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print_error "Package verification failed"
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exit 1
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fi
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}
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# Setup ML models based on configuration
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setup_ml_models() {
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if [ "$INSTALL_TYPE" != "full" ]; then
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return 0
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fi
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# Check if we should pre-download
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local should_predownload="$CUSTOM_ML_PREDOWNLOAD"
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if [ -z "$should_predownload" ] || [ "$should_predownload" = "auto" ]; then
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print_header "ML Model Pre-download"
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echo -e "${CYAN}Pre-download ML models for offline use?${NC}"
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echo ""
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echo -e "${BLUE}Model: sentence-transformers/all-MiniLM-L6-v2 (~80MB)${NC}"
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echo " • Purpose: Offline fallback when Ollama unavailable"
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echo " • If skipped: Auto-downloads when first needed"
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echo ""
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echo -n "Pre-download now? [y/N]: "
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read -r download_ml
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should_predownload=$([ "$download_ml" = "y" ] && echo "download" || echo "skip")
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fi
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if [ "$should_predownload" != "download" ]; then
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print_info "Skipping ML model pre-download"
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echo " Models will download automatically when first used"
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return 0
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fi
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print_info "Pre-downloading ML model..."
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echo -e "${BLUE} This ensures offline availability${NC}"
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# Create a simple progress indicator
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python3 -c "
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import sys
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import threading
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import time
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# Progress spinner
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def spinner():
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chars = '⠋⠙⠹⠸⠼⠴⠦⠧⠇⠏'
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while not spinner.stop:
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for char in chars:
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if spinner.stop:
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break
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sys.stdout.write(f'\r {char} Downloading model...')
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sys.stdout.flush()
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time.sleep(0.1)
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try:
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spinner.stop = False
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spinner_thread = threading.Thread(target=spinner)
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spinner_thread.start()
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
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spinner.stop = True
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spinner_thread.join()
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print('\r✅ ML model ready for offline use ')
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except Exception as e:
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spinner.stop = True
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spinner_thread.join()
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print(f'\r❌ Download failed: {e} ')
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sys.exit(1)
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" 2>/dev/null
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if [ $? -eq 0 ]; then
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print_success "ML models ready"
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else
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print_warning "Pre-download failed"
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echo " Models will auto-download when first needed"
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fi
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}
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# Test installation
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test_installation() {
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print_header "Testing Installation"
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print_info "Testing basic functionality..."
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# Test import
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if python3 -c "from claude_rag import CodeEmbedder, ProjectIndexer, CodeSearcher; print('✅ Import successful')" 2>/dev/null; then
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print_success "Python imports working"
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else
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print_error "Import test failed"
|
||
return 1
|
||
fi
|
||
|
||
# Test embedding system
|
||
if python3 -c "
|
||
from claude_rag import CodeEmbedder
|
||
embedder = CodeEmbedder()
|
||
info = embedder.get_embedding_info()
|
||
print(f'✅ Embedding system: {info[\"method\"]}')
|
||
" 2>/dev/null; then
|
||
print_success "Embedding system working"
|
||
else
|
||
print_warning "Embedding test failed, but system should still work"
|
||
fi
|
||
|
||
return 0
|
||
}
|
||
|
||
# Show completion message
|
||
show_completion() {
|
||
print_header "Installation Complete!"
|
||
|
||
echo -e "${GREEN}${BOLD}FSS-Mini-RAG is now installed!${NC}"
|
||
echo ""
|
||
echo -e "${CYAN}Quick Start Options:${NC}"
|
||
echo ""
|
||
echo -e "${GREEN}🎯 TUI (Beginner-Friendly):${NC}"
|
||
echo " ./rag-tui"
|
||
echo " # Interactive interface with guided setup"
|
||
echo ""
|
||
echo -e "${BLUE}💻 CLI (Advanced):${NC}"
|
||
echo " ./rag-mini index /path/to/project"
|
||
echo " ./rag-mini search /path/to/project \"query\""
|
||
echo " ./rag-mini status /path/to/project"
|
||
echo ""
|
||
echo -e "${CYAN}Documentation:${NC}"
|
||
echo " • README.md - Complete technical documentation"
|
||
echo " • docs/GETTING_STARTED.md - Step-by-step guide"
|
||
echo " • examples/ - Usage examples and sample configs"
|
||
echo ""
|
||
|
||
if [ "$INSTALL_TYPE" = "light" ] && ! command_exists ollama; then
|
||
echo -e "${YELLOW}Note: You chose light installation but Ollama isn't running.${NC}"
|
||
echo "The system will use hash-based embeddings (lower quality)."
|
||
echo "For best results, install Ollama from https://ollama.ai/download"
|
||
echo ""
|
||
fi
|
||
|
||
# Ask if they want to run a test
|
||
echo -n "Would you like to run a quick test now? (Y/n): "
|
||
read -r run_test
|
||
if [[ ! $run_test =~ ^[Nn]$ ]]; then
|
||
run_quick_test
|
||
fi
|
||
}
|
||
|
||
# Run quick test
|
||
run_quick_test() {
|
||
print_header "Quick Test"
|
||
|
||
print_info "Testing on this project directory..."
|
||
echo "This will index the FSS-Mini-RAG system itself as a test."
|
||
echo ""
|
||
|
||
# Index this project
|
||
if ./rag-mini index "$SCRIPT_DIR"; then
|
||
print_success "Indexing completed"
|
||
|
||
# Try a search
|
||
echo ""
|
||
print_info "Testing search functionality..."
|
||
./rag-mini search "$SCRIPT_DIR" "embedding system" --limit 3
|
||
|
||
echo ""
|
||
print_success "Test completed successfully!"
|
||
echo -e "${CYAN}You can now use FSS-Mini-RAG on your own projects.${NC}"
|
||
else
|
||
print_error "Test failed"
|
||
echo "Check the error messages above for troubleshooting."
|
||
fi
|
||
}
|
||
|
||
# Main installation flow
|
||
main() {
|
||
echo -e "${CYAN}${BOLD}"
|
||
echo "╔══════════════════════════════════════╗"
|
||
echo "║ FSS-Mini-RAG Installer ║"
|
||
echo "║ Fast Semantic Search for Code ║"
|
||
echo "╚══════════════════════════════════════╝"
|
||
echo -e "${NC}"
|
||
|
||
echo -e "${BLUE}Adaptive installation process:${NC}"
|
||
echo " • Python environment setup"
|
||
echo " • Smart configuration based on your system"
|
||
echo " • Optional AI model downloads (with consent)"
|
||
echo " • Testing and verification"
|
||
echo ""
|
||
echo -e "${CYAN}Note: You'll be asked before downloading any models${NC}"
|
||
echo ""
|
||
|
||
echo -n "Begin installation? [Y/n]: "
|
||
read -r continue_install
|
||
if [[ $continue_install =~ ^[Nn]$ ]]; then
|
||
echo "Installation cancelled."
|
||
exit 0
|
||
fi
|
||
|
||
# Run installation steps
|
||
check_python
|
||
create_venv
|
||
|
||
# Check Ollama availability
|
||
ollama_available=false
|
||
if check_ollama; then
|
||
ollama_available=true
|
||
fi
|
||
|
||
# Get installation preferences with smart recommendations
|
||
get_installation_preferences
|
||
|
||
# Install dependencies
|
||
install_dependencies
|
||
|
||
# Setup models based on configuration
|
||
if [ "$ollama_available" = true ]; then
|
||
setup_ollama_model
|
||
fi
|
||
setup_ml_models
|
||
|
||
if test_installation; then
|
||
show_completion
|
||
else
|
||
print_error "Installation test failed"
|
||
echo "Please check error messages and try again."
|
||
exit 1
|
||
fi
|
||
}
|
||
|
||
# Run main function
|
||
main "$@" |