Fss-Rag-Mini/README.md

FSS-Mini-RAG

A lightweight, educational RAG system that actually works
Built for beginners who want results, and developers who want to understand how RAG really works

Demo

See it in action: index a project and search semantically in seconds

How It Works

flowchart TD
    Start([🚀 Start FSS-Mini-RAG]) --> Interface{Choose Interface}
    
    Interface -->|Beginners| TUI[🖥️ Interactive TUI<br/>./rag-tui]
    Interface -->|Power Users| CLI[⚡ Advanced CLI<br/>./rag-mini <command>]
    
    TUI --> SelectFolder[📁 Select Folder to Index]
    CLI --> SelectFolder
    
    SelectFolder --> Index[🔍 Index Documents<br/>Creates searchable database]
    
    Index --> Ready{📚 Ready to Search}
    
    Ready -->|Quick Answers| Search[🔍 Search Mode<br/>Fast semantic search]
    Ready -->|Deep Analysis| Explore[🧠 Explore Mode<br/>AI-powered analysis]
    
    Search --> SearchResults[📋 Instant Results<br/>Ranked by relevance]
    Explore --> ExploreResults[💬 AI Conversation<br/>Context + reasoning]
    
    SearchResults --> More{Want More?}
    ExploreResults --> More
    
    More -->|Different Query| Ready
    More -->|Advanced Features| CLI
    More -->|Done| End([✅ Success!])
    
    CLI -.->|Full Power| AdvancedFeatures[⚡ Advanced Features:<br/>• Batch processing<br/>• Custom parameters<br/>• Automation scripts<br/>• Background server]
    
    style Start fill:#e8f5e8,stroke:#4caf50,stroke-width:2px
    style CLI fill:#fff9c4,stroke:#f57c00,stroke-width:3px
    style AdvancedFeatures fill:#fff9c4,stroke:#f57c00,stroke-width:2px
    style Search fill:#e3f2fd,stroke:#2196f3,stroke-width:2px
    style Explore fill:#f3e5f5,stroke:#9c27b0,stroke-width:2px
    style End fill:#e8f5e8,stroke:#4caf50,stroke-width:2px

What This Is

FSS-Mini-RAG is a distilled, lightweight implementation of a production-quality RAG (Retrieval Augmented Generation) search system. Born from 2 years of building, refining, and tuning RAG systems - from enterprise-scale solutions handling 14,000 queries/second to lightweight implementations that anyone can install and understand.

The Problem This Solves: Most RAG implementations are either too simple (poor results) or too complex (impossible to understand and modify). This bridges that gap.

Two Powerful Modes

FSS-Mini-RAG offers two distinct experiences optimized for different use cases:

🚀 Synthesis Mode - Fast & Consistent

./rag-mini search ~/project "authentication logic" --synthesize

• Perfect for: Quick answers, code discovery, fast lookups
• Speed: Lightning fast responses (no thinking overhead)
• Quality: Consistent, reliable results

🧠 Exploration Mode - Deep & Interactive

./rag-mini explore ~/project
> How does authentication work in this codebase?
> Why is the login function slow?
> What security concerns should I be aware of?

• Perfect for: Learning codebases, debugging, detailed analysis
• Features: Thinking-enabled LLM, conversation memory, follow-up questions
• Quality: Deep reasoning with full context awareness

Quick Start (2 Minutes)

Step 1: Install

# Linux/macOS
./install_mini_rag.sh

# Windows  
install_windows.bat

Step 2: Start Using

# Beginners: Interactive interface
./rag-tui                    # Linux/macOS
rag.bat                      # Windows

# Experienced users: Direct commands
./rag-mini index ~/project   # Index your project
./rag-mini search ~/project "your query"

That's it. No external dependencies, no configuration required, no PhD in computer science needed.

What Makes This Different

For Beginners

• Just works - Zero configuration required
• Multiple interfaces - TUI for learning, CLI for speed
• Educational - Shows you CLI commands as you use the TUI
• Solid results - Finds code by meaning, not just keywords

For Developers

• Hackable - Clean, documented code you can actually modify
• Configurable - YAML config for everything, or change the code directly
• Multiple embedding options - Ollama, ML models, or hash-based
• Production patterns - Streaming, batching, error handling, monitoring

For Learning

• Complete technical documentation - How chunking, embedding, and search actually work
• Educational tests - See the system in action with real examples
• No magic - Every decision explained, every component documented

Usage Examples

Find Code by Concept

./rag-mini search ~/project "user authentication"
# Finds: login functions, auth middleware, session handling, password validation

Natural Language Queries

./rag-mini search ~/project "error handling for database connections"
# Finds: try/catch blocks, connection pool error handlers, retry logic

Development Workflow

./rag-mini index ~/new-project              # Index once
./rag-mini search ~/new-project "API endpoints"   # Search as needed
./rag-mini status ~/new-project            # Check index health

Advanced usage: semantic search with synthesis and exploration modes

Installation Options

🎯 Copy & Paste Installation (Guaranteed to Work)

Perfect for beginners - these commands work on any fresh Ubuntu, Windows, or Mac system:

Fresh Ubuntu/Debian System:

# Install required system packages
sudo apt update && sudo apt install -y python3 python3-pip python3-venv git curl

# Clone and setup FSS-Mini-RAG
git clone https://github.com/FSSCoding/Fss-Mini-Rag.git
cd Fss-Mini-Rag

# Create isolated Python environment
python3 -m venv .venv
source .venv/bin/activate

# Install Python dependencies
pip install -r requirements.txt

# Optional: Install Ollama for best search quality (secure method)
curl -fsSL https://ollama.com/install.sh -o /tmp/ollama-install.sh
# Verify it's a shell script (basic safety check)
file /tmp/ollama-install.sh | grep -q "shell script" && chmod +x /tmp/ollama-install.sh && /tmp/ollama-install.sh
rm -f /tmp/ollama-install.sh
ollama serve &
sleep 3
ollama pull nomic-embed-text

# Ready to use!
./rag-mini index /path/to/your/project
./rag-mini search /path/to/your/project "your search query"

Fresh CentOS/RHEL/Fedora System:

# Install required system packages
sudo dnf install -y python3 python3-pip python3-venv git curl

# Clone and setup FSS-Mini-RAG
git clone https://github.com/FSSCoding/Fss-Mini-Rag.git
cd Fss-Mini-Rag

# Create isolated Python environment  
python3 -m venv .venv
source .venv/bin/activate

# Install Python dependencies
pip install -r requirements.txt

# Optional: Install Ollama for best search quality (secure method)
curl -fsSL https://ollama.com/install.sh -o /tmp/ollama-install.sh
# Verify it's a shell script (basic safety check)
file /tmp/ollama-install.sh | grep -q "shell script" && chmod +x /tmp/ollama-install.sh && /tmp/ollama-install.sh
rm -f /tmp/ollama-install.sh
ollama serve &
sleep 3
ollama pull nomic-embed-text

# Ready to use!
./rag-mini index /path/to/your/project
./rag-mini search /path/to/your/project "your search query"

Fresh macOS System:

# Install Homebrew (if not installed)
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

# Install required packages
brew install python3 git curl

# Clone and setup FSS-Mini-RAG
git clone https://github.com/FSSCoding/Fss-Mini-Rag.git
cd Fss-Mini-Rag

# Create isolated Python environment
python3 -m venv .venv
source .venv/bin/activate

# Install Python dependencies
pip install -r requirements.txt

# Optional: Install Ollama for best search quality (secure method)
curl -fsSL https://ollama.com/install.sh -o /tmp/ollama-install.sh
# Verify it's a shell script (basic safety check)
file /tmp/ollama-install.sh | grep -q "shell script" && chmod +x /tmp/ollama-install.sh && /tmp/ollama-install.sh
rm -f /tmp/ollama-install.sh
ollama serve &
sleep 3
ollama pull nomic-embed-text

# Ready to use!
./rag-mini index /path/to/your/project  
./rag-mini search /path/to/your/project "your search query"

Fresh Windows System:

REM Install Python (if not installed)
REM Download from: https://python.org/downloads (ensure "Add to PATH" is checked)
REM Install Git from: https://git-scm.com/download/win

REM Clone and setup FSS-Mini-RAG
git clone https://github.com/FSSCoding/Fss-Mini-Rag.git
cd Fss-Mini-Rag

REM Create isolated Python environment
python -m venv .venv
.venv\Scripts\activate.bat

REM Install Python dependencies  
pip install -r requirements.txt

REM Optional: Install Ollama for best search quality
REM Download from: https://ollama.com/download
REM Run installer, then:
ollama serve
REM In new terminal:
ollama pull nomic-embed-text

REM Ready to use!
rag.bat index C:\path\to\your\project
rag.bat search C:\path\to\your\project "your search query"

What these commands do:

• System packages: Install Python 3.8+, pip (package manager), venv (virtual environments), git (version control), curl (downloads)
• Clone repository: Download FSS-Mini-RAG source code to your computer
• Virtual environment: Create isolated Python space (prevents conflicts with system Python)
• Dependencies: Install required Python libraries (pandas, numpy, lancedb, etc.)
• Ollama (optional): AI model server for best search quality - works offline and free
• Model download: Get high-quality embedding model for semantic search
• Ready to use: Index any folder and search through it semantically

⚡ For Agents & CI/CD: Headless Installation

Perfect for automated deployments, agents, and CI/CD pipelines:

Linux/macOS:

./install_mini_rag.sh --headless
# Automated installation with sensible defaults
# No interactive prompts, perfect for scripts

Windows:

install_windows.bat --headless
# Automated installation with sensible defaults  
# No interactive prompts, perfect for scripts

What headless mode does:

• Uses existing virtual environment if available
• Installs core dependencies only (light mode)
• Downloads embedding model if Ollama is available
• Skips interactive prompts and tests
• Perfect for agent automation and CI/CD pipelines

🚀 Recommended: Full Installation

Linux/macOS:

./install_mini_rag.sh
# Handles Python setup, dependencies, optional AI models

Windows:

install_windows.bat
# Handles Python setup, dependencies, works reliably

Manual Setup

Linux/macOS:

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Windows:

python -m venv .venv
.venv\Scripts\activate.bat
pip install -r requirements.txt

Note: The experimental copy & run feature is provided for convenience but may fail on some systems. If you encounter issues, use the full installer for reliable setup.

System Requirements

• Python 3.8+ (installer checks and guides setup)
• Optional: Ollama (for best search quality - installer helps set up)
• Fallback: Works without external dependencies (uses built-in embeddings)

Project Philosophy

This implementation prioritizes:

1. Educational Value - You can understand and modify every part
2. Practical Results - Actually finds relevant code, not just keyword matches
3. Zero Friction - Works out of the box, configurable when needed
4. Real-world Patterns - Production techniques in beginner-friendly code

What's Inside

• Hybrid embedding system - Ollama → ML → Hash fallbacks
• Smart chunking - Language-aware code parsing
• Vector + keyword search - Best of both worlds
• Streaming architecture - Handles large codebases efficiently
• Multiple interfaces - TUI, CLI, Python API, server mode

Next Steps

• New users: Run ./rag-tui (Linux/macOS) or rag.bat (Windows) for guided experience
• Developers: Read TECHNICAL_GUIDE.md for implementation details
• Contributors: See CONTRIBUTING.md for development setup

Documentation

• Getting Started - Get running in 5 minutes
• Visual Diagrams - 📊 System flow charts and architecture diagrams
• TUI Guide - Complete walkthrough of the friendly interface
• Technical Guide - How the system actually works
• Troubleshooting - Fix common issues
• Beginner Glossary - Friendly terms and concepts

License

MIT - Use it, learn from it, build on it.

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Built by someone who got frustrated with RAG implementations that were either too simple to be useful or too complex to understand. This is the system I wish I'd found when I started.