BobAi
a84ff94fba
This comprehensive update enhances user experience with several key improvements: ## Enhanced Streaming & Thinking Display - Implement real-time streaming with gray thinking tokens that collapse after completion - Fix thinking token redisplay bug with proper content filtering - Add clear "AI Response:" headers to separate thinking from responses - Enable streaming by default for better user engagement - Keep thinking visible for exploration, collapse only for suggested questions ## Natural Conversation Responses - Convert clunky JSON exploration responses to natural, conversational format - Improve exploration prompts for friendly, colleague-style interactions - Update summary generation with better context handling - Eliminate double response display issues ## Model Reference Updates - Remove all llama3.2 references in favor of qwen3 models - Fix non-existent qwen3:3b references, replace with proper model names - Update model rankings to prioritize working qwen models across all components - Ensure consistent model recommendations in docs and examples ## Cross-Platform Icon Integration - Add desktop icon setup to Linux installer with .desktop entry - Add Windows shortcuts for desktop and Start Menu integration - Improve installer user experience with visual branding ## Configuration & Navigation Fixes - Fix "0" option in configuration menu to properly go back - Improve configuration menu user-friendliness - Update troubleshooting guides with correct model suggestions These changes significantly improve the beginner experience while maintaining technical accuracy and system reliability.
877 lines
30 KiB
Bash
Executable File
877 lines
30 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 ""
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echo -e "${BOLD}Options:${NC}"
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echo -e "${GREEN}1) Install Ollama automatically${NC} (recommended)"
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echo -e "${YELLOW}2) Manual installation${NC} - Visit https://ollama.com/download"
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echo -e "${BLUE}3) Continue without Ollama${NC} (uses ML fallback)"
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echo ""
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echo -n "Choose [1/2/3]: "
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read -r ollama_choice
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case "$ollama_choice" in
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1|"")
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print_info "Installing Ollama using official installer..."
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echo -e "${CYAN}Running: curl -fsSL https://ollama.com/install.sh | sh${NC}"
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if curl -fsSL https://ollama.com/install.sh | sh; then
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print_success "Ollama installed successfully"
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print_info "Starting Ollama server..."
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ollama serve &
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sleep 3
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if curl -s http://localhost:11434/api/version >/dev/null 2>&1; then
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print_success "Ollama server started"
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echo ""
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echo -e "${CYAN}💡 Pro tip: Download an LLM for AI-powered search synthesis!${NC}"
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echo -e " Lightweight: ${GREEN}ollama pull qwen3:0.6b${NC} (~500MB, very fast)"
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echo -e " Balanced: ${GREEN}ollama pull qwen3:1.7b${NC} (~1.4GB, good quality)"
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echo -e " Excellent: ${GREEN}ollama pull qwen3:4b${NC} (~2.5GB, sweet spot for most users)"
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echo -e " Maximum: ${GREEN}ollama pull qwen3:8b${NC} (~5GB, slower but top quality)"
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echo ""
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echo -e "${BLUE}🧠 RAG works great with smaller models! 4B is usually perfect.${NC}"
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echo -e "${BLUE}Creative possibilities: Try mistral for storytelling, qwen2.5-coder for development!${NC}"
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echo ""
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return 0
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else
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print_warning "Ollama installed but failed to start automatically"
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echo "Please start Ollama manually: ollama serve"
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echo "Then re-run this installer"
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exit 1
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fi
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else
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print_error "Failed to install Ollama automatically"
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echo "Please install manually from https://ollama.com/download"
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exit 1
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fi
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;;
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2)
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echo ""
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echo -e "${YELLOW}Manual Ollama installation:${NC}"
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echo " 1. Visit: https://ollama.com/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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print_info "Exiting for manual installation..."
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exit 0
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;;
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3)
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print_info "Continuing without Ollama (will use ML fallback)"
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return 1
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;;
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*)
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print_warning "Invalid choice, continuing without Ollama"
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return 1
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;;
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esac
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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) ${CYAN}← Best performance + AI chat${NC}"
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echo -e "${YELLOW}F) Full${NC} - Light + ML fallback (~2-3GB) ${CYAN}← RAG-only if no Ollama${NC}"
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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 application icon for desktop integration
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setup_desktop_icon() {
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print_header "Setting Up Desktop Integration"
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# Check if we're in a GUI environment
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if [ -z "$DISPLAY" ] && [ -z "$WAYLAND_DISPLAY" ]; then
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print_info "No GUI environment detected - skipping desktop integration"
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return 0
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fi
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local icon_source="$SCRIPT_DIR/assets/Fss_Mini_Rag.png"
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local desktop_dir="$HOME/.local/share/applications"
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local icon_dir="$HOME/.local/share/icons"
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# Check if icon file exists
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if [ ! -f "$icon_source" ]; then
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print_warning "Icon file not found at $icon_source"
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return 1
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fi
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# Create directories if needed
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mkdir -p "$desktop_dir" "$icon_dir" 2>/dev/null
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# Copy icon to standard location
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local icon_dest="$icon_dir/fss-mini-rag.png"
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if cp "$icon_source" "$icon_dest" 2>/dev/null; then
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print_success "Icon installed to $icon_dest"
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else
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print_warning "Could not install icon (permissions?)"
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return 1
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||
fi
|
||
|
||
# Create desktop entry
|
||
local desktop_file="$desktop_dir/fss-mini-rag.desktop"
|
||
cat > "$desktop_file" << EOF
|
||
[Desktop Entry]
|
||
Name=FSS-Mini-RAG
|
||
Comment=Fast Semantic Search for Code and Documents
|
||
Exec=$SCRIPT_DIR/rag-tui
|
||
Icon=fss-mini-rag
|
||
Terminal=true
|
||
Type=Application
|
||
Categories=Development;Utility;TextEditor;
|
||
Keywords=search;code;rag;semantic;ai;
|
||
StartupNotify=true
|
||
EOF
|
||
|
||
if [ -f "$desktop_file" ]; then
|
||
chmod +x "$desktop_file"
|
||
print_success "Desktop entry created"
|
||
|
||
# Update desktop database if available
|
||
if command_exists update-desktop-database; then
|
||
update-desktop-database "$desktop_dir" 2>/dev/null
|
||
print_info "Desktop database updated"
|
||
fi
|
||
|
||
print_info "✨ FSS-Mini-RAG should now appear in your application menu!"
|
||
print_info " Look for it in Development or Utility categories"
|
||
else
|
||
print_warning "Could not create desktop entry"
|
||
return 1
|
||
fi
|
||
|
||
return 0
|
||
}
|
||
|
||
# Setup ML models based on configuration
|
||
setup_ml_models() {
|
||
if [ "$INSTALL_TYPE" != "full" ]; then
|
||
return 0
|
||
fi
|
||
|
||
# Check if we should pre-download
|
||
local should_predownload="$CUSTOM_ML_PREDOWNLOAD"
|
||
if [ -z "$should_predownload" ] || [ "$should_predownload" = "auto" ]; then
|
||
print_header "ML Model Pre-download"
|
||
echo -e "${CYAN}Pre-download ML models for offline use?${NC}"
|
||
echo ""
|
||
echo -e "${BLUE}Model: sentence-transformers/all-MiniLM-L6-v2 (~80MB)${NC}"
|
||
echo " • Purpose: Offline fallback when Ollama unavailable"
|
||
echo " • If skipped: Auto-downloads when first needed"
|
||
echo ""
|
||
echo -n "Pre-download now? [y/N]: "
|
||
read -r download_ml
|
||
should_predownload=$([ "$download_ml" = "y" ] && echo "download" || echo "skip")
|
||
fi
|
||
|
||
if [ "$should_predownload" != "download" ]; then
|
||
print_info "Skipping ML model pre-download"
|
||
echo " Models will download automatically when first used"
|
||
return 0
|
||
fi
|
||
|
||
print_info "Pre-downloading ML model..."
|
||
echo -e "${BLUE} This ensures offline availability${NC}"
|
||
|
||
# Create a simple progress indicator
|
||
python3 -c "
|
||
import sys
|
||
import threading
|
||
import time
|
||
|
||
# Progress spinner
|
||
def spinner():
|
||
chars = '⠋⠙⠹⠸⠼⠴⠦⠧⠇⠏'
|
||
while not spinner.stop:
|
||
for char in chars:
|
||
if spinner.stop:
|
||
break
|
||
sys.stdout.write(f'\r {char} Downloading model...')
|
||
sys.stdout.flush()
|
||
time.sleep(0.1)
|
||
|
||
try:
|
||
spinner.stop = False
|
||
spinner_thread = threading.Thread(target=spinner)
|
||
spinner_thread.start()
|
||
|
||
from sentence_transformers import SentenceTransformer
|
||
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
|
||
|
||
spinner.stop = True
|
||
spinner_thread.join()
|
||
print('\r✅ ML model ready for offline use ')
|
||
|
||
except Exception as e:
|
||
spinner.stop = True
|
||
spinner_thread.join()
|
||
print(f'\r❌ Download failed: {e} ')
|
||
sys.exit(1)
|
||
" 2>/dev/null
|
||
|
||
if [ $? -eq 0 ]; then
|
||
print_success "ML models ready"
|
||
else
|
||
print_warning "Pre-download failed"
|
||
echo " Models will auto-download when first needed"
|
||
fi
|
||
}
|
||
|
||
# Test installation
|
||
test_installation() {
|
||
print_header "Testing Installation"
|
||
|
||
print_info "Testing basic functionality..."
|
||
|
||
# Test import
|
||
if python3 -c "from mini_rag import CodeEmbedder, ProjectIndexer, CodeSearcher; print('✅ Import successful')" 2>/dev/null; then
|
||
print_success "Python imports working"
|
||
else
|
||
print_error "Import test failed"
|
||
return 1
|
||
fi
|
||
|
||
# Test embedding system
|
||
if python3 -c "
|
||
from mini_rag import CodeEmbedder
|
||
embedder = CodeEmbedder()
|
||
info = embedder.get_embedding_info()
|
||
print(f'✅ Embedding system: {info[\"method\"]}')
|
||
" 2>/dev/null; then
|
||
print_success "Embedding system working"
|
||
else
|
||
echo ""
|
||
echo -e "${YELLOW}⚠️ System Check${NC}"
|
||
|
||
# Smart diagnosis - check what's actually available
|
||
if command_exists ollama && curl -s http://localhost:11434/api/version >/dev/null 2>&1; then
|
||
# Ollama is running, check for models
|
||
local available_models=$(ollama list 2>/dev/null | grep -E "(qwen3|llama|mistral|gemma)" | head -5)
|
||
local embedding_models=$(ollama list 2>/dev/null | grep -E "(embed|bge)" | head -2)
|
||
|
||
if [[ -n "$available_models" ]]; then
|
||
echo -e "${GREEN}✅ Ollama is running with available models${NC}"
|
||
echo -e "${CYAN}Your setup will work great! The system will auto-select the best models.${NC}"
|
||
echo ""
|
||
echo -e "${BLUE}💡 RAG Performance Tip:${NC} Smaller models often work better with RAG!"
|
||
echo -e " With context provided, even 0.6B models give good results"
|
||
echo -e " 4B models = excellent, 8B+ = overkill (slower responses)"
|
||
else
|
||
echo -e "${BLUE}Ollama is running but no chat models found.${NC}"
|
||
echo -e "Download a lightweight model: ${GREEN}ollama pull qwen3:0.6b${NC} (fast)"
|
||
echo -e "Or balanced option: ${GREEN}ollama pull qwen3:4b${NC} (excellent quality)"
|
||
fi
|
||
else
|
||
echo -e "${BLUE}Ollama not running or not installed.${NC}"
|
||
echo -e "Start Ollama: ${GREEN}ollama serve${NC}"
|
||
echo -e "Or install from: https://ollama.com/download"
|
||
fi
|
||
|
||
echo ""
|
||
echo -e "${CYAN}✅ FSS-Mini-RAG will auto-detect and use the best available method.${NC}"
|
||
echo ""
|
||
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 ""
|
||
echo -e "${BOLD}🧪 Quick Test Available${NC}"
|
||
echo -e "${CYAN}Test FSS-Mini-RAG with a small sample project (takes ~10 seconds)${NC}"
|
||
echo ""
|
||
|
||
# Ensure output is flushed and we're ready for input
|
||
printf "Run quick test now? [Y/n]: "
|
||
|
||
# More robust input handling
|
||
if read -r run_test < /dev/tty 2>/dev/null; then
|
||
echo "User chose: '$run_test'" # Debug output
|
||
if [[ ! $run_test =~ ^[Nn]$ ]]; then
|
||
run_quick_test
|
||
echo ""
|
||
show_beginner_guidance
|
||
else
|
||
echo -e "${BLUE}Skipping test - you can run it later with: ./rag-tui${NC}"
|
||
show_beginner_guidance
|
||
fi
|
||
else
|
||
# Fallback if interactive input fails
|
||
echo ""
|
||
echo -e "${YELLOW}⚠️ Interactive input not available - skipping test prompt${NC}"
|
||
echo -e "${BLUE}You can test FSS-Mini-RAG anytime with: ./rag-tui${NC}"
|
||
show_beginner_guidance
|
||
fi
|
||
}
|
||
|
||
# Note: Sample project creation removed - now indexing real codebase/docs
|
||
|
||
# Run quick test with sample data
|
||
run_quick_test() {
|
||
print_header "Quick Test"
|
||
|
||
# Ask what to index: code vs docs
|
||
echo -e "${CYAN}What would you like to explore with FSS-Mini-RAG?${NC}"
|
||
echo ""
|
||
echo -e "${GREEN}1) Code${NC} - Index the FSS-Mini-RAG codebase (~50 files)"
|
||
echo -e "${BLUE}2) Docs${NC} - Index the documentation (~10 files)"
|
||
echo ""
|
||
echo -n "Choose [1/2] or Enter for code: "
|
||
read -r index_choice
|
||
|
||
# Determine what to index
|
||
local target_dir="$SCRIPT_DIR"
|
||
local target_name="FSS-Mini-RAG codebase"
|
||
if [[ "$index_choice" == "2" ]]; then
|
||
target_dir="$SCRIPT_DIR/docs"
|
||
target_name="FSS-Mini-RAG documentation"
|
||
fi
|
||
|
||
# Ensure we're in the right directory and have the right permissions
|
||
if [[ ! -f "./rag-mini" ]]; then
|
||
print_error "rag-mini script not found in current directory: $(pwd)"
|
||
print_info "This might be a path issue. The installer should run from the project directory."
|
||
return 1
|
||
fi
|
||
|
||
if [[ ! -x "./rag-mini" ]]; then
|
||
print_info "Making rag-mini executable..."
|
||
chmod +x ./rag-mini
|
||
fi
|
||
|
||
# Index the chosen target
|
||
print_info "Indexing $target_name..."
|
||
echo -e "${CYAN}This will take 10-30 seconds depending on your system${NC}"
|
||
echo ""
|
||
|
||
if ./rag-mini index "$target_dir"; then
|
||
print_success "✅ Indexing completed successfully!"
|
||
|
||
echo ""
|
||
print_info "🎯 Launching Interactive Tutorial..."
|
||
echo -e "${CYAN}The TUI has 6 sample questions to get you started.${NC}"
|
||
echo -e "${CYAN}Try the suggested queries or enter your own!${NC}"
|
||
echo ""
|
||
echo -n "Press Enter to start interactive tutorial: "
|
||
read -r
|
||
|
||
# Launch the TUI which has the existing interactive tutorial system
|
||
./rag-tui.py "$target_dir" || true
|
||
|
||
echo ""
|
||
print_success "🎉 Tutorial completed!"
|
||
echo -e "${CYAN}FSS-Mini-RAG is working perfectly!${NC}"
|
||
|
||
else
|
||
print_error "❌ Indexing failed"
|
||
echo ""
|
||
echo -e "${YELLOW}Possible causes:${NC}"
|
||
echo "• Virtual environment not properly activated"
|
||
echo "• Missing dependencies (try: pip install -r requirements.txt)"
|
||
echo "• Path issues (ensure script runs from project directory)"
|
||
echo "• Ollama connection issues (if using Ollama)"
|
||
echo ""
|
||
return 1
|
||
fi
|
||
}
|
||
|
||
# Show beginner-friendly first steps
|
||
show_beginner_guidance() {
|
||
print_header "Getting Started - Your First Search"
|
||
|
||
echo -e "${CYAN}FSS-Mini-RAG is ready! Here's how to start:${NC}"
|
||
echo ""
|
||
echo -e "${GREEN}🎯 For Beginners (Recommended):${NC}"
|
||
echo " ./rag-tui"
|
||
echo " ↳ Interactive interface with sample questions"
|
||
echo ""
|
||
echo -e "${BLUE}💻 For Developers:${NC}"
|
||
echo " ./rag-mini index /path/to/your/project"
|
||
echo " ./rag-mini search /path/to/your/project \"your question\""
|
||
echo ""
|
||
echo -e "${YELLOW}📚 What can you search for in FSS-Mini-RAG?${NC}"
|
||
echo " • Technical: \"chunking strategy\", \"ollama integration\", \"indexing performance\""
|
||
echo " • Usage: \"how to improve search results\", \"why does indexing take long\""
|
||
echo " • Your own projects: any code, docs, emails, notes, research"
|
||
echo ""
|
||
echo -e "${CYAN}💡 Pro tip:${NC} You can drag ANY text-based documents into a folder"
|
||
echo " and search through them - emails, notes, research, chat logs!"
|
||
}
|
||
|
||
# Main installation flow
|
||
main() {
|
||
echo -e "${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 [ "$ollama_available" = true ]; then
|
||
setup_ollama_model
|
||
fi
|
||
setup_ml_models
|
||
|
||
# Setup desktop integration with icon
|
||
setup_desktop_icon
|
||
|
||
if test_installation; then
|
||
show_completion
|
||
else
|
||
print_error "Installation test failed"
|
||
echo "Please check error messages and try again."
|
||
exit 1
|
||
fi
|
||
}
|
||
|
||
# Run main function
|
||
main "$@" |