fss-mini-rag-github/docs/TUI_GUIDE.md

FSS-Mini-RAG Text User Interface Guide

Overview

The TUI (Text User Interface) provides a beginner-friendly, menu-driven way to use FSS-Mini-RAG without memorizing command-line syntax. It's designed with a "learn by doing" approach - you use the friendly interface while seeing the CLI commands, naturally building confidence to use the command line directly.

Quick Start

./rag-tui

That's it! The TUI will guide you through everything.

Interface Design Philosophy

Learn by Doing

• No reading required - Jump in and start using it
• CLI commands shown - See equivalent commands as you work
• Progressive disclosure - Basic actions upfront, advanced options available
• Natural transition - Build confidence to try CLI commands

User Flow

1. Select Project → Choose directory to search
2. Index Project → Process files for search
3. Search Content → Find what you need quickly
4. Explore Project → Interactive AI-powered discovery (NEW!)
5. Configure System → Customize search behavior

Main Menu Options

1. Select Project Directory

Purpose: Choose which codebase to work with

Options:

• Enter project path - Type any directory path
• Use current directory - Index where you are now
• Browse recent projects - Pick from previously indexed projects

What You Learn:

• Project paths and directory navigation
• How RAG works with specific directories
• CLI equivalent: All commands need a project path

CLI Commands Shown:

# You'll see these patterns throughout
./rag-mini <command> /path/to/your/project

2. Index Project for Search

Purpose: Process files to make them searchable

What Happens:

• Scans all files in project directory
• Breaks text into searchable chunks
• Creates embeddings (AI numerical representations)
• Stores in local database (.mini-rag/ folder)

Interactive Elements:

• Force re-index option - Completely rebuild if needed
• Progress feedback - See files being processed
• Results summary - Files processed, chunks created, timing

What You Learn:

• Why indexing is necessary (one-time setup per project)
• What gets indexed (code files, documentation, configs)
• How fast the system works
• Storage location (.mini-rag/ directory)

CLI Commands Shown:

./rag-mini index /path/to/project          # Basic indexing
./rag-mini index /path/to/project --force  # Force complete re-index

3. Search Project

Purpose: Find code using natural language queries

Interactive Process:

1. Enter search query - Natural language or keywords
2. Set result limit - How many matches to show (1-20)
3. View results - See full content, not just snippets

Result Display:

• File path - Relative to project root
• Relevance score - How closely it matches your query
• Line numbers - Exact location in file
• Context - Function/class name if applicable
• Full content - Up to 8 lines of actual code/text
• Continuation info - How many more lines exist

Advanced Tips Shown:

• Enhanced search with ./rag-mini-enhanced
• Verbose output with --verbose flag
• Context-aware search for related code

What You Learn:

• Semantic search vs text search (finds concepts, not just words)
• How to phrase effective queries
• Reading search results and relevance scores
• When to use different search strategies

CLI Commands Shown:

./rag-mini search /path/to/project "authentication logic"
./rag-mini search /path/to/project "user login" --limit 10
./rag-mini-enhanced context /path/to/project "login()"

4. Explore Project (NEW!)

Purpose: Interactive AI-powered discovery with conversation memory

What Makes Explore Different:

• Conversational: Ask follow-up questions that build on previous answers
• AI Reasoning: Uses thinking mode for deeper analysis and explanations
• Educational: Perfect for understanding unfamiliar codebases
• Context Aware: Remembers what you've already discussed

Interactive Process:

1. First Question Guidance: Clear prompts with example questions
2. Starter Suggestions: Random helpful questions to get you going
3. Natural Follow-ups: Ask "why?", "how?", "show me more" naturally
4. Session Memory: AI remembers your conversation context

Explore Mode Features:

Quick Start Options:

• Option 1 - Help: Show example questions and explore mode capabilities
• Option 2 - Status: Project information and current exploration session
• Option 3 - Suggest: Get a random starter question picked from 7 curated examples

Starter Questions (randomly suggested):

• "What are the main components of this project?"
• "How is error handling implemented?"
• "Show me the authentication and security logic"
• "What are the key functions I should understand first?"
• "How does data flow through this system?"
• "What configuration options are available?"
• "Show me the most important files to understand"

Advanced Usage:

• Deep Questions: "Why is this function slow?" "How does the security work?"
• Code Analysis: "Explain this algorithm" "What could go wrong here?"
• Architecture: "How do these components interact?" "What's the design pattern?"
• Best Practices: "Is this code following best practices?" "How would you improve this?"

What You Learn:

• Conversational AI: How to have productive technical conversations with AI
• Code Understanding: Deep analysis capabilities beyond simple search
• Context Building: How conversation memory improves over time
• Question Techniques: Effective ways to explore unfamiliar code

CLI Commands Shown:

./rag-mini explore /path/to/project    # Start interactive exploration

Perfect For:

• Understanding new codebases
• Code review and analysis
• Learning from existing projects
• Documenting complex systems
• Onboarding new team members

5. View Status

Purpose: Check system health and project information

Information Displayed:

Project Status:

• Indexing status - Whether project is indexed
• File count - How many files are searchable
• Chunk count - Total searchable pieces
• Last update - When indexing was last run
• Average chunks per file - Efficiency metric

Embedding System Status:

• Current method - Ollama, ML fallback, or hash
• Quality level - High, good, or basic
• Model information - Which AI model is active

What You Learn:

• System architecture (embedding methods)
• Project statistics and health
• When re-indexing might be needed
• Performance characteristics

CLI Commands Shown:

./rag-mini status /path/to/project

6. Configuration Manager (ENHANCED!)

Purpose: Interactive configuration with user-friendly options

New Interactive Features:

• Live Configuration Dashboard - See current settings with clear status
• Quick Configuration Options - Change common settings without YAML editing
• Guided Setup - Explanations and presets for each option
• Validation - Input checking and helpful error messages

Main Configuration Options:

1. Adjust Chunk Size:

• Presets: Small (1000), Medium (2000), Large (3000), or custom
• Guidance: Performance vs accuracy explanations
• Smart Validation: Range checking and recommendations

2. Toggle Query Expansion:

• Educational Info: Clear explanation of benefits and requirements
• Easy Toggle: Simple on/off with confirmation
• System Check: Verifies Ollama availability for AI features

3. Configure Search Behavior:

• Result Count: Adjust default number of search results (1-100)
• BM25 Toggle: Enable/disable keyword matching boost
• Similarity Threshold: Fine-tune match sensitivity (0.0-1.0)

4. View/Edit Configuration File:

• Full File Viewer: Display complete config with syntax highlighting
• Editor Instructions: Commands for nano, vim, VS Code
• YAML Help: Format explanation and editing tips

5. Reset to Defaults:

• Safe Reset: Confirmation before resetting all settings
• Clear Explanations: Shows what defaults will be restored
• Backup Reminder: Suggests saving current config first

6. Advanced Settings:

• File Filtering: Min file size, exclude patterns (view only)
• Performance Settings: Batch sizes, streaming thresholds
• LLM Preferences: Model rankings and selection priorities

Key Settings Dashboard:

• 📁 Chunk size: 2000 characters (with emoji indicators)
• 🧠 Chunking strategy: semantic
• 🔍 Search results: 10 results
• 📊 Embedding method: ollama
• 🚀 Query expansion: enabled/disabled
• ⚡ LLM synthesis: enabled/disabled

What You Learn:

• Configuration Impact: How settings affect search quality and speed
• Interactive YAML: Easier than manual editing for beginners
• Best Practices: Recommended settings for different project types
• System Understanding: How all components work together

CLI Commands Shown:

cat /path/to/project/.mini-rag/config.yaml   # View config
nano /path/to/project/.mini-rag/config.yaml  # Edit config

Perfect For:

• Beginners who find YAML intimidating
• Quick adjustments without memorizing syntax
• Understanding what each setting actually does
• Safe experimentation with guided validation

7. CLI Command Reference

Purpose: Complete command reference for transitioning to CLI

Organized by Use Case:

Basic Commands:

• Daily usage patterns
• Essential operations
• Common options

Enhanced Commands:

• Advanced search features
• Analysis and optimization
• Pattern finding

Quick Scripts:

• Simplified wrappers
• Batch operations
• Development workflow integration

Options Reference:

• Flags and their purposes
• When to use each option
• Performance considerations

What You Learn:

• Complete CLI capabilities
• How TUI maps to CLI commands
• Advanced features not in TUI
• Integration possibilities

Educational Features

Progressive Learning

Stage 1: TUI Comfort

• Use menus and prompts
• See immediate results
• Build understanding through doing

Stage 2: CLI Awareness

• Notice commands being shown
• Understand command structure
• See patterns in usage

Stage 3: CLI Experimentation

• Try simple commands from TUI
• Compare TUI vs CLI speed
• Explore advanced options

Stage 4: CLI Proficiency

• Use CLI for daily tasks
• Script and automate workflows
• Customize for specific needs

Knowledge Building

Concepts Learned:

• Semantic search - AI understanding vs text matching
• Embeddings - How text becomes searchable numbers
• Chunking - Breaking files into meaningful pieces
• Configuration - Customizing for different projects
• Indexing - One-time setup vs incremental updates

Skills Developed:

• Query crafting - How to phrase effective searches
• Result interpretation - Understanding relevance scores
• System administration - Configuration and maintenance
• Workflow integration - Using RAG in development process

Advanced Usage Patterns

Project Management Workflow

1. New Project: Select directory → Index → Configure if needed
2. Existing Project: Check status → Search → Re-index if outdated
3. Multiple Projects: Use recent projects browser for quick switching

Search Strategies

Concept Searches:

• "user authentication" → finds login, auth, sessions
• "database connection" → finds DB code, connection pools, queries
• "error handling" → finds try/catch, error classes, logging

Specific Code Searches:

• "class UserManager" → finds class definitions
• "function authenticate()" → finds specific functions
• "config settings" → finds configuration code

Pattern Searches:

• "validation logic" → finds input validation across files
• "API endpoints" → finds route definitions and handlers
• "test cases" → finds unit tests and test data

Configuration Optimization

Small Projects (< 100 files):

• Default settings work well
• Consider smaller chunk sizes for very granular search

Large Projects (> 1000 files):

• Exclude build directories and dependencies
• Increase chunk sizes for broader context
• Use semantic chunking for code-heavy projects

Mixed Content Projects:

• Balance chunk sizes for code vs documentation
• Configure file patterns to include/exclude specific types
• Use appropriate embedding methods for content type

Troubleshooting

Common Issues

"Project not indexed":

• Use "Index project for search" from main menu
• Check if project path is correct
• Look for permission issues

"No results found":

• Try broader search terms
• Check project is actually indexed
• Verify files contain expected content

"Search results poor quality":

• Check embedding system status
• Consider re-indexing with --force
• Review configuration for project type

"System seems slow":

• Check if Ollama is running (best performance)
• Consider ML fallback installation
• Review project size and exclude patterns

Learning Resources

Built-in Help:

• TUI shows CLI commands throughout
• Configuration section explains all options
• Status shows system health

External Resources:

• README.md - Complete technical documentation
• examples/config.yaml - Configuration examples
• docs/GETTING_STARTED.md - Step-by-step setup guide

Community Patterns:

• Common search queries for different languages
• Project-specific configuration examples
• Integration with IDEs and editors

Tips for Success

Getting Started

1. Start with a small project - Learn the basics without complexity
2. Try different search terms - Experiment with phrasing
3. Watch the CLI commands - Notice patterns and structure
4. Use the status check - Understand what's happening

Building Expertise

1. Compare TUI vs CLI speed - See when CLI becomes faster
2. Experiment with configuration - Try different settings
3. Search your own code - Use familiar projects for learning
4. Try advanced searches - Explore enhanced commands

Transitioning to CLI

1. Copy commands from TUI - Start with exact commands shown
2. Modify gradually - Change options and see effects
3. Build shortcuts - Create aliases for common operations
4. Integrate with workflow - Add to development process

The TUI is designed to be your training wheels - use it as long as you need, and transition to CLI when you're ready. There's no pressure to abandon the TUI; it's a fully functional interface that many users prefer permanently.