Fix installer indexing hang and improve user experience
🔧 Script Handling Improvements: - Fix infinite recursion in bash wrapper for index/search commands - Improve embedding system diagnostics with intelligent detection - Add timeout protection and progress indicators to installer test - Enhance interactive input handling with graceful fallbacks 🎯 User Experience Enhancements: - Replace confusing error messages with educational diagnostics - Add RAG performance tips about model sizing (4B optimal, 8B+ overkill) - Correct model recommendations (qwen3:4b not qwen3:3b) - Smart Ollama model detection shows available models - Clear guidance for next steps after installation 🛠 Technical Fixes: - Add get_embedding_info() method to CodeEmbedder class - Robust test prompt handling with /dev/tty input - Path validation and permission fixing in test scripts - Comprehensive error diagnostics with actionable solutions Installation now completes reliably with clear feedback and guidance.
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@ -188,12 +188,13 @@ check_ollama() {
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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} (~400MB, very fast)"
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echo -e " Balanced: ${GREEN}ollama pull qwen3:1.7b${NC} (~1GB, good quality)"
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echo -e " Excellent: ${GREEN}ollama pull qwen3:3b${NC} (~2GB, great for this project)"
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echo -e " Premium: ${GREEN}ollama pull qwen3:8b${NC} (~5GB, amazing results)"
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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 "${BLUE}Creative possibilities: Try mistral for storytelling, or qwen3-coder for development!${NC}"
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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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@ -558,7 +559,36 @@ print(f'✅ Embedding system: {info[\"method\"]}')
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" 2>/dev/null; then
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print_success "Embedding system working"
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else
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print_warning "Embedding test failed, but system should still work"
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echo ""
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echo -e "${YELLOW}⚠️ System Check${NC}"
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# Smart diagnosis - check what's actually available
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if command_exists ollama && curl -s http://localhost:11434/api/version >/dev/null 2>&1; then
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# Ollama is running, check for models
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local available_models=$(ollama list 2>/dev/null | grep -E "(qwen3|llama|mistral|gemma)" | head -5)
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local embedding_models=$(ollama list 2>/dev/null | grep -E "(embed|bge)" | head -2)
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if [[ -n "$available_models" ]]; then
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echo -e "${GREEN}✅ Ollama is running with available models${NC}"
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echo -e "${CYAN}Your setup will work great! The system will auto-select the best models.${NC}"
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echo ""
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echo -e "${BLUE}💡 RAG Performance Tip:${NC} Smaller models often work better with RAG!"
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echo -e " With context provided, even 0.6B models give good results"
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echo -e " 4B models = excellent, 8B+ = overkill (slower responses)"
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else
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echo -e "${BLUE}Ollama is running but no chat models found.${NC}"
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echo -e "Download a lightweight model: ${GREEN}ollama pull qwen3:0.6b${NC} (fast)"
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echo -e "Or balanced option: ${GREEN}ollama pull qwen3:4b${NC} (excellent quality)"
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fi
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else
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echo -e "${BLUE}Ollama not running or not installed.${NC}"
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echo -e "Start Ollama: ${GREEN}ollama serve${NC}"
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echo -e "Or install from: https://ollama.com/download"
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fi
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echo ""
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echo -e "${CYAN}✅ FSS-Mini-RAG will auto-detect and use the best available method.${NC}"
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echo ""
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fi
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return 0
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@ -595,13 +625,30 @@ show_completion() {
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fi
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# Ask if they want to run a test
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echo -n "Would you like to run a quick test now? (Y/n): "
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read -r run_test
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echo ""
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echo -e "${BOLD}🧪 Quick Test Available${NC}"
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echo -e "${CYAN}Test FSS-Mini-RAG with a small sample project (takes ~10 seconds)${NC}"
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echo ""
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# Ensure output is flushed and we're ready for input
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printf "Run quick test now? [Y/n]: "
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# More robust input handling
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if read -r run_test < /dev/tty 2>/dev/null; then
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echo "User chose: '$run_test'" # Debug output
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if [[ ! $run_test =~ ^[Nn]$ ]]; then
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run_quick_test
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echo ""
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show_beginner_guidance
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else
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echo -e "${BLUE}Skipping test - you can run it later with: ./rag-tui${NC}"
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show_beginner_guidance
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fi
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else
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# Fallback if interactive input fails
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echo ""
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echo -e "${YELLOW}⚠️ Interactive input not available - skipping test prompt${NC}"
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echo -e "${BLUE}You can test FSS-Mini-RAG anytime with: ./rag-tui${NC}"
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show_beginner_guidance
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fi
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}
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@ -664,34 +711,57 @@ run_quick_test() {
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print_info "Creating small sample project for testing..."
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local sample_dir=$(create_sample_project)
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echo "Sample project created with 3 files for fast testing."
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echo "✅ Sample project created: $sample_dir"
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echo ""
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# Index the sample project (much faster)
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print_info "Indexing sample project (this should be fast)..."
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if ./rag-mini index "$sample_dir" --quiet; then
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print_success "Sample project indexed successfully"
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# Ensure we're in the right directory and have the right permissions
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if [[ ! -f "./rag-mini" ]]; then
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print_error "rag-mini script not found in current directory: $(pwd)"
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print_info "This might be a path issue. The installer should run from the project directory."
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return 1
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fi
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if [[ ! -x "./rag-mini" ]]; then
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print_info "Making rag-mini executable..."
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chmod +x ./rag-mini
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fi
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# Test with explicit error handling and timeout
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print_info "Indexing sample project (should complete in ~5 seconds)..."
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echo -e "${CYAN}Command: ./rag-mini index \"$sample_dir\" --quiet${NC}"
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if timeout 30 ./rag-mini index "$sample_dir" --quiet; then
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print_success "✅ Indexing completed successfully"
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echo ""
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print_info "Testing search with sample queries..."
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echo -e "${BLUE}Running search: 'user authentication'${NC}"
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./rag-mini search "$sample_dir" "user authentication" --limit 2
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print_info "Testing search functionality..."
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echo -e "${CYAN}Command: ./rag-mini search \"$sample_dir\" \"user authentication\" --limit 2${NC}"
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if timeout 15 ./rag-mini search "$sample_dir" "user authentication" --limit 2; then
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echo ""
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print_success "Test completed successfully!"
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echo -e "${CYAN}Ready to use FSS-Mini-RAG on your own projects!${NC}"
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# Offer beginner guidance
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echo ""
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echo -e "${YELLOW}💡 Beginner Tip:${NC} Try the interactive mode with pre-made questions"
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echo " Run: ./rag-tui for guided experience"
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print_success "🎉 Test completed successfully!"
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echo -e "${CYAN}FSS-Mini-RAG is working perfectly!${NC}"
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else
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print_error "Search test failed or timed out"
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echo "Indexing worked but search had issues."
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fi
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# Clean up sample
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print_info "Cleaning up test files..."
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rm -rf "$sample_dir"
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else
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print_error "Sample test failed"
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echo "This might indicate an issue with the installation."
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print_error "❌ Indexing test failed or timed out"
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echo ""
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echo -e "${YELLOW}Possible causes:${NC}"
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echo "• Virtual environment not properly activated"
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echo "• Missing dependencies (try: pip install -r requirements.txt)"
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echo "• Path issues (ensure script runs from project directory)"
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echo "• Ollama connection issues (if using Ollama)"
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echo ""
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print_info "Cleaning up and continuing..."
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rm -rf "$sample_dir"
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return 1
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fi
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}
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@ -469,6 +469,31 @@ class OllamaEmbedder:
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"ollama_url": self.base_url if self.mode == "ollama" else None
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}
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def get_embedding_info(self) -> Dict[str, str]:
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"""Get human-readable embedding system information for installer."""
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status = self.get_status()
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if status["mode"] == "ollama":
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return {
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"method": f"Ollama ({status['ollama_model']})",
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"status": "working"
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}
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elif status["mode"] == "ml":
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return {
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"method": f"ML Fallback ({status['fallback_model']})",
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"status": "working"
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}
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elif status["mode"] == "hash":
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return {
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"method": "Hash-based (basic similarity)",
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"status": "working"
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}
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else:
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return {
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"method": "Unknown",
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"status": "error"
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}
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def warmup(self):
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"""Warm up the embedding system with a dummy request."""
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dummy_code = "def hello(): pass"
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4
rag-mini
4
rag-mini
@ -327,8 +327,8 @@ main() {
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exec "$PYTHON" "$SCRIPT_DIR/claude_rag/server.py" "$@"
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;;
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"index"|"search"|"status")
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# Direct CLI commands
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exec "$SCRIPT_DIR/rag-mini" "$@"
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# Direct CLI commands - call Python script
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exec "$PYTHON" "$SCRIPT_DIR/rag-mini.py" "$@"
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;;
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*)
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# Unknown command - show help
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