# Getting Started with RAG Web Application

## Introduction

Welcome to the RAG Web Application! This is a sample document to help you test the system.

## What is RAG?

RAG stands for Retrieval-Augmented Generation. It's a technique that combines:

1. **Information Retrieval**: Finding relevant documents from a knowledge base
2. **Language Generation**: Using Large Language Models (LLMs) to generate natural answers
3. **Context Integration**: Combining retrieved information with the LLM's capabilities

### Key Benefits

- **Accurate Responses**: Answers are grounded in your actual documentation
- **Source Citations**: Every answer includes references to source documents
- **Up-to-Date Information**: Your knowledge base can be updated anytime
- **Flexible Querying**: Ask questions in natural language

## Features of This Application

### File Upload

You can upload multiple markdown files at once:
- Simply drag and drop files into the upload area
- Or click to browse and select files
- All files must have a `.md` extension

### Semantic Search

The application uses advanced semantic search:
- Questions are converted to vector embeddings
- Documents are chunked and embedded
- Cosine similarity finds the most relevant content
- The best matches are sent to the LLM for answer generation

### Chat Interface

The chat interface provides:
- Real-time responses
- Source citations for transparency
- Beautiful formatting with markdown support
- Smooth animations and transitions

## How It Works

### Step 1: Document Processing

When you upload markdown files:
1. Files are saved to the knowledge base directory
2. Content is parsed and split into semantic chunks
3. Each chunk is converted to a vector embedding
4. Embeddings are indexed for fast retrieval

### Step 2: Question Answering

When you ask a question:
1. Your question is converted to a vector embedding
2. The system searches for similar document chunks
3. Top matching chunks are retrieved
4. The LLM receives the chunks as context
5. An answer is generated based on the context
6. Sources are included with the response

## Example Questions

Try asking questions like:
- "What is RAG and how does it work?"
- "What are the key benefits of this application?"
- "How do I upload files?"
- "What happens when I ask a question?"

## Technical Details

### Embedding Model

The application uses the `all-MiniLM-L6-v2` model by default:
- Fast inference speed
- Good accuracy for most use cases
- 384-dimensional embeddings
- Trained on billions of sentence pairs

### Language Model

Powered by Ollama with Gemma 3:
- Local inference (no API costs)
- Privacy-focused (data stays on your machine)
- Fast responses with GPU acceleration
- Support for multiple open-source models

### Architecture

Built with modern web technologies:
- **FastAPI**: High-performance Python web framework
- **HTMX**: Dynamic HTML without complex JavaScript
- **TailwindCSS**: Beautiful, responsive styling
- **Ceylon Framework**: Rust-powered agent system

## Best Practices

### Document Organization

For best results:
- Use clear, descriptive headings in your markdown files
- Break content into logical sections
- Include code examples where relevant
- Add cross-references between related topics

### Question Formulation

Get better answers by:
- Being specific in your questions
- Providing context when needed
- Breaking complex questions into smaller parts
- Using terminology from your documents

### Knowledge Base Maintenance

Keep your knowledge base effective:
- Regularly update outdated information
- Remove duplicate or redundant files
- Organize documents by topic
- Include a variety of content types

## Troubleshooting

### Common Issues

**Problem**: No results found for my question
- **Solution**: Try rephrasing your question or using different keywords

**Problem**: Slow response times
- **Solution**: Consider reducing the number of documents or chunk sizes

**Problem**: Irrelevant answers
- **Solution**: Ensure your documents contain the information you're asking about

## Next Steps

1. Upload your own markdown documentation
2. Try asking various types of questions
3. Experiment with different document structures
4. Explore the knowledge base statistics

## Conclusion

This RAG Web Application provides a powerful way to interact with your documentation. Upload your knowledge base and start chatting!

For more information, check out:
- The README.md file in the project directory
- The Markdown RAG Guide in the parent directory
- The Ceylon Framework documentation

Happy chatting! 🚀
